registryVersion: 1.12.0 models: - name: Llama 3.3 70B Instruct displayName: Llama 3.3 70B Instruct modelHubID: llama-3.3-instruct category: Language type: NGC description: Instruction-tuned model for multilingual chat, coding assistance, and synthetic data generation. Delivers strong performance across diverse benchmarks. requireLicense: true licenseAgreements: - label: Use Policy url: https://www.llama.com/llama3_3/use-policy/ - label: License Agreement url: https://www.llama.com/llama3_3/license/ modelVariants: - variantId: Llama 3.3 70B Instruct modelCard: {
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    "description": "# **Llama-3.3-70B-Instruct Overview**\n\n## **Description:**\n\n**Llama-3.3-70B-Instruct** is an auto-regressive language model that uses an optimized transformer architecture. It is designed for text-based tasks such as multilingual chat, coding assistance, and synthetic data generation, and is particularly optimized for dialogue-based use cases. With 70 billion parameters, it provides strong performance that is comparable to larger models but with lower hardware requirements, and it does not process images or audio.\n\nThis model is ready for commercial/non-commercial use.\n\nThis version introduces support for GB200 NVL72, GH200 NVL2, B200 and NVFP4. CUDA updated to version 12.9. For detailed information, refer to Release [Notes for NVIDIA NIM for LLMs LLM 1.12](https://docs.nvidia.com/nim/large-language-models/latest/release-notes.html). \n\n## **Third-Party Community Consideration**\n\nThis model is not owned or developed by NVIDIA. This model has been developed and built to a third-party's requirements for this application and use case; see link to Non-NVIDIA\\[meta-llama/Llama-3.3-70B-Instruct\\]  \n([https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct)). \n\n## **License/Terms of Use:**\n\n**GOVERNING TERMS:** The NIM container is governed by the [NVIDIA Software License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-license-agreement/) and the [Product-Specific Terms for NVIDIA AI Products](https://www.nvidia.com/en-us/agreements/enterprise-software/product-specific-terms-for-ai-products/); and the model is governed by the [NVIDIA AI Foundation Models Community License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-ai-foundation-models-community-license-agreement/). \n\n\n**ADDITIONAL INFORMATION**: [Llama 3.3 Community License Agreement](https://www.llama.com/llama3_3/license/). Built with Llama.\n\n## **Get Help**\n\n### Enterprise Support\nGet access to knowledge base articles and support cases or [submit a ticket](https://www.nvidia.com/en-us/data-center/products/ai-enterprise-suite/support/).\n\nYou are responsible for ensuring that your use of NVIDIA provided models complies with all applicable laws.\n\n## **Deployment Geography:**\n\nGlobal \n\n## **Use Case:**\n\nThis model is intended for developers, researchers, and enterprises. They would integrate it into applications and workflows for a variety of advanced text-based tasks.\n\n* For Conversational AI: Building sophisticated and natural-sounding chatbots for customer service, multilingual virtual assistants, and interactive dialogue systems.  \n* For Software Development: Engineers might use the model as a powerful coding assistant for generating code, debugging, explaining complex algorithms, and writing documentation.  \n* For Content Creation and Analysis: Businesses and content creators might use the model  to draft emails, generate marketing copy, summarize long documents, and create synthetic text data to train other machine learning models.\n\n## **Release Date:**\n\nBuild.Nvidia.com 12/17/2024 via  \n[llama-3.3-70b-instruct Model by Meta | NVIDIA NIM](https://build.nvidia.com/meta/llama-3_3-70b-instruct)\n\nGithub 12/13/2024 via   \n[https://github.blog/changelog/2024-12-13-llama-3-3-70b-instruct-is-now-available-on-github-models-ga/](https://github.blog/changelog/2024-12-13-llama-3-3-70b-instruct-is-now-available-on-github-models-ga/)\n\nHuggingface 12/06/2024 via   \n[https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct) \n\n**Reference(s):** \n\n[https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct)\n\n## **Model Architecture:** \n\nArchitecture Type: Transformer  \nNetwork Architecture: Llama-3.3-70B\n\nThis model was developed based on Meta-Llama-3.3-70B  \n[https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct).\n\nNumber of model parameters: 7.06*10^10\n\n## **Input:**\n\nInput Type(s): Text \n\nInput Format(s): String \n\nInput Parameters: One-Dimensional (1D)\n\nOther Properties Related to Input: The model processes input as tokens. The maximum context length is 8,192 tokens. Input text strings must be pre-processed by the model's specific Tiktoken tokenizer before being fed into the model.\n\n## **Output:**\n\nOutput Type(s): Text \n\nOutput Format(s): String\n\nOutput Parameters: One-Dimensional (1D)\n\nOther Properties Related to Output: The model generates text as a sequence of tokens. The length of the generated output can be controlled by inference parameters. The raw token output requires post-processing (de-tokenization) to be converted into a human-readable string.\n\nOur AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA's hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.\n\n## **Software Integration:**\n\nRuntime Engine: vLLM, TensorRT\n\nSupported Hardware Microarchitecture Compatibility:\n\nNVIDIA Ampere  \nNVIDIA Blackwell  \nNVIDIA Hopper  \nNVIDIA Lovelace \n\nPreferred Operating System(s):\n\nLinux   \nWindows\n\nThe integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.\n\n## **Model Version(s):**\n\nLlama-3.3-70B-Instruct\n\n## **Usage**\n\n### **Use with transformers**\n\nStarting with transformers \\>= 4.45.0 onward, you can run conversational inference using the Transformers pipeline abstraction or by leveraging the Auto classes with the generate() function.\n\nMake sure to update your transformers installation via pip install \\--upgrade transformers.\n\nSee the snippet below for usage with Transformers:\n\n```\nimport transformers\nimport torch\n\nmodel_id = \"meta-llama/Llama-3.3-70B-Instruct\"\n\npipeline = transformers.pipeline(\n    \"text-generation\",\n    model=model_id,\n    model_kwargs={\"torch_dtype\": torch.bfloat16},\n    device_map=\"auto\",\n)\n\nmessages = [\n    {\"role\": \"system\", \"content\": \"You are a pirate chatbot who always responds in pirate speak!\"},\n    {\"role\": \"user\", \"content\": \"Who are you?\"},\n]\n\noutputs = pipeline(\n    messages,\n    max_new_tokens=256,\n)\nprint(outputs[0][\"generated_text\"][-1])\n```\n\n### **Tool use with transformers**\n\nLLaMA-3.3 supports multiple tool use formats. You can see a full guide to prompt formatting [here](https://llama.meta.com/docs/model-cards-and-prompt-formats/llama3_1/).\n\nTool use is also supported through [chat templates](https://huggingface.co/docs/transformers/main/chat_templating#advanced-tool-use--function-calling) in Transformers. Here is a quick example showing a single simple tool:\n\n```\n# First, define a tool\ndef get_current_temperature(location: str) -> float:\n    \"\"\"\n    Get the current temperature at a location.\n    \n    Args:\n        location: The location to get the temperature for, in the format \"City, Country\"\n    Returns:\n        The current temperature at the specified location in the specified units, as a float.\n    \"\"\"\n    return 22.  # A real function should probably actually get the temperature!\n\n# Next, create a chat and apply the chat template\nmessages = [\n  {\"role\": \"system\", \"content\": \"You are a bot that responds to weather queries.\"},\n  {\"role\": \"user\", \"content\": \"Hey, what's the temperature in Paris right now?\"}\n]\n\ninputs = tokenizer.apply_chat_template(messages, tools=[get_current_temperature], add_generation_prompt=True)\n```\n\nYou can then generate text from this input as normal. If the model generates a tool call, you should add it to the chat like so:\n\n```\ntool_call = {\"name\": \"get_current_temperature\", \"arguments\": {\"location\": \"Paris, France\"}}\nmessages.append({\"role\": \"assistant\", \"tool_calls\": [{\"type\": \"function\", \"function\": tool_call}]})\n```\n\nand then call the tool and append the result, with the tool role, like so:\n\n```\nmessages.append({\"role\": \"tool\", \"name\": \"get_current_temperature\", \"content\": \"22.0\"})\n```\n\nAfter that, you can generate() again to let the model use the tool result in the chat. Note that this was a very brief introduction to tool calling \\- for more information, see the [LLaMA prompt format docs](https://llama.meta.com/docs/model-cards-and-prompt-formats/llama3_1/) and the Transformers [tool use documentation](https://huggingface.co/docs/transformers/main/chat_templating#advanced-tool-use--function-calling).\n\n### **Use with bitsandbytes**\n\nThe model checkpoints can be used in 8-bit and 4-bit for further memory optimisations using bitsandbytes and transformers\n\nSee the snippet below for usage:\n\n```\nimport torch\nfrom transformers import AutoModelForCausalLM, AutoTokenizer\n\nmodel_id = \"meta-llama/Llama-3.3-70B-Instruct\"\nquantization_config = BitsAndBytesConfig(load_in_8bit=True)\n\nquantized_model = AutoModelForCausalLM.from_pretrained(\n    model_id, device_map=\"auto\", torch_dtype=torch.bfloat16, quantization_config=quantization_config)\n\ntokenizer = AutoTokenizer.from_pretrained(model_id)\ninput_text = \"What are we having for dinner?\"\ninput_ids = tokenizer(input_text, return_tensors=\"pt\").to(\"cuda\")\n\noutput = quantized_model.generate(**input_ids, max_new_tokens=10)\n\nprint(tokenizer.decode(output[0], skip_special_tokens=True))\n```\n\nTo load in 4-bit simply pass load\\_in\\_4bit=True\n\n### **Use with llama**\n\nPlease, follow the instructions in the [repository](https://github.com/meta-llama/llama).\n\nTo download Original checkpoints, see the example command below leveraging huggingface-cli:\n\n```\nhuggingface-cli download meta-llama/Llama-3.3-70B-Instruct --include \"original/*\" --local-dir Llama-3.3-70B-Instruct\n```\n\n## **Training, Testing, and Evaluation Datasets:**\n\n### **Training Dataset**\n\n**Data Modality:** Text \n\n\n**Link:** Undisclosed\n\n**Data Collection Method:** Hybrid: Human, Synthetic, Automated\n\n**Labeling Method:** Hybrid: Human, Synthetic\n\n**Properties:** \n\nThe pre-training dataset contains over 15 trillion (15T) tokens from a diverse mix of publicly available online sources. The fine-tuning dataset consists of prompts and preference-ranked responses designed to improve helpfulness and safety.\n\n### **Testing Dataset**\n\n**Link:** Undisclosed\n\n**Data Collection Method:** Hybrid: Human, Synthetic, Automated\n\n**Labeling Method:** Hybrid: Human, Automated\n\n**Properties:** \n\nThe public datasets cover a wide range of tasks including massive multitask language understanding (MMLU), problem-solving (GSM8K), and code generation (HumanEval). Meta's internal evaluation set contains over 2,000 prompts designed to test for safety and helpfulness across various potentially risky categories. \n\n### **Evaluation Dataset**\n\n**Link:** Undisclosed\n\n**Data Collection Method:** Hybrid: Automated, Human, Synthetic\n\n**Labeling Method:** Hybrid: Human, Automated\n\n**Properties:** \n\nThe public datasets are industry-standard benchmarks designed to evaluate diverse capabilities like general knowledge, reasoning, coding, and math. For example, MMLU tests multitask knowledge, HumanEval tests code generation, and GSM8K tests grade-school math word problems. Meta's private evaluation set contains over 2,000 prompts for assessing safety and helpfulness.\n\n**Detailed Performance:**\n\n| Category | Benchmark | \\# Shots | Metric | Llama 3.1 8B Instruct | Llama 3.1 70B Instruct | Llama-3.3 70B Instruct | Llama 3.1 405B Instruct |\n| ----- | ----- | ----- | ----- | ----- | ----- | ----- | ----- |\n|  | MMLU (CoT) | 0 | macro\\_avg/acc | 73.0 | 86.0 | 86.0 | 88.6 |\n|  | MMLU Pro (CoT) | 5 | macro\\_avg/acc | 48.3 | 66.4 | 68.9 | 73.3 |\n| Steerability | IFEval |  |  | 80.4 | 87.5 | 92.1 | 88.6 |\n| Reasoning | GPQA Diamond (CoT) | 0 | acc | 31.8 | 48.0 | 50.5 | 49.0 |\n| Code | HumanEval | 0 | pass@1 | 72.6 | 80.5 | 88.4 | 89.0 |\n|  | MBPP EvalPlus (base) | 0 | pass@1 | 72.8 | 86.0 | 87.6 | 88.6 |\n| Math | MATH (CoT) | 0 | sympy\\_intersection\\_score | 51.9 | 68.0 | 77.0 | 73.8 |\n| Tool Use | BFCL v2 | 0 | overall\\_ast\\_summary/macro\\_avg/valid | 65.4 | 77.5 | 77.3 | 81.1 |\n| Multilingual | MGSM | 0 | em | 68.9 | 86.9 | 91.1 | 91.6 |\n\n## **Technical Limitations** \n\nTesting conducted to date has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, the model's potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying this model in any applications, developers should perform safety testing and tuning tailored to their specific applications. Please refer to available resources including the [Responsible Use Guide](https://llama.meta.com/responsible-use-guide), [Trust and Safety](https://llama.meta.com/trust-and-safety/) solutions, and other [resources](https://llama.meta.com/docs/get-started/) to learn more about responsible development. \n\n## **Inference:**\n\n**Acceleration Engine:** vLLM, TensorRT \n\n**Test Hardware:** \n   \n* B200 SXM   \n* H200 SXM  \n* H100 SXM  \n* A100 SXM 80GB  \n* A100 SXM 40GB  \n* L40S PCIe  \n* A10G  \n* H100 NVL  \n* H200 NVL  \n* GH200 96GB    \n* GB200 NVL72\n* GH200 NVL2\n* RTX 5090  \n* RTX 4090  \n* RTX 6000 Ada\n\n## **Ethical Considerations:**\n\nNVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).\n\nYou are responsible for ensuring that your use of NVIDIA provided models complies with all applicable laws.",
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key: PROFILE value: THROUGHPUT - key: PRECISION value: BF16 - key: GPU value: A10G - key: COUNT value: 8 - key: GPU DEVICE value: 2237:10DE - key: NIM VERSION value: 1.14.0 - key: DOWNLOAD SIZE value: 150GB - key: LLM ENGINE value: TENSORRT_LLM - profileId: nim/meta/llama-3.3-70b-instruct:h200x2-throughput-bf16-lciwvjwxkw framework: TensorRT-LLM displayName: Llama 3.3 70B Instruct H200x2 BF16 Throughput ngcMetadata: 64878d614ca9a859228cd55d140af0865823c2f3524e43c7be53c01c039481b6: model: meta/llama-3.3-70b-instruct release: 1.14.0 tags: feat_lora: 'false' gpu: H200 gpu_device: 2335:10de llm_engine: tensorrt_llm nim_workspace_hash_v1: 824477061f9c69bd79fd248a136a273e8d861d092fb853ede5e06e12510d8188 number_of_gpus: '2' pp: '1' precision: bf16 profile: throughput tp: '2' modelFormat: trt-llm spec: - key: PROFILE value: THROUGHPUT - key: PRECISION value: BF16 - key: GPU value: H200 - key: COUNT value: 2 - key: GPU DEVICE value: 2335:10DE - key: NIM VERSION value: 1.14.0 - key: DOWNLOAD SIZE value: 134GB - 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key: PROFILE value: THROUGHPUT - key: PRECISION value: BF16 - key: GPU value: A100 - key: COUNT value: 4 - key: GPU DEVICE value: 20B2:10DE - key: NIM VERSION value: 1.14.0 - key: DOWNLOAD SIZE value: 140GB - key: LLM ENGINE value: TENSORRT_LLM - profileId: nim/meta/llama-3.3-70b-instruct:l40sx4-throughput-bf16-rhzeshgk8w framework: TensorRT-LLM displayName: Llama 3.3 70B Instruct L40Sx4 BF16 Throughput ngcMetadata: 7cb838de5dad2c42066f0616756d0ad2708939c450b95416d41098e9931470c1: model: meta/llama-3.3-70b-instruct release: 1.14.0 tags: feat_lora: 'false' gpu: L40S gpu_device: 26b9:10de llm_engine: tensorrt_llm nim_workspace_hash_v1: c419c6ba54c118a6deb6ed9918e9c72e7f151698116c1d3c2bc32042a94d6bbb number_of_gpus: '4' pp: '1' precision: bf16 profile: throughput tp: '4' modelFormat: trt-llm spec: - key: PROFILE value: THROUGHPUT - key: PRECISION value: BF16 - key: GPU value: L40S - key: COUNT value: 4 - key: GPU DEVICE value: 26B9:10DE - key: NIM VERSION value: 1.14.0 - key: DOWNLOAD SIZE value: 140GB - 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key: LLM ENGINE value: TENSORRT_LLM labels: - Llama - Meta - Chat - Text Generation - Large Language Model - NVIDIA Validated config: architectures: - Other modelType: llama license: NVIDIA AI Foundation Models Community License - name: Llama 3.3 Nemotron Super 49B displayName: Llama 3.3 Nemotron Super 49B modelHubID: llama-3.3-nemotron-super-49b category: Language type: NGC description: Llama-3.3-Nemotron-Super-49B v1 and v1.5 are language models that can follow instructions, complete requests, and generate creative text formats. The Llama-3.3-Nemotron-Super-49B v1 series of Large Language Models (LLMs) are instruction-tuned versions of the Llama-Nemotron. requireLicense: true licenseAgreements: - label: Use Policy url: https://www.nvidia.com/en-us/agreements/enterprise-software/product-specific-terms-for-ai-products/ - label: License Agreement url: https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-license-agreement/ modelVariants: - variantId: Llama 3.3 Nemotron Super 49B V1 modelCard: {
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    "description": "# Llama-3.3-Nemotron-Super-49B-v1\n\n\n## Model Overview \n\nLlama-3.3-Nemotron-Super-49B-v1 is a large language model (LLM) which is a derivative of Meta Llama-3.3-70B-Instruct (AKA the reference model). It is a reasoning model that is post trained for reasoning, human chat preferences, and tasks, such as RAG and tool calling. The model supports a context length of 128K tokens.\n\nLlama-3.3-Nemotron-Super-49B-v1 is a model which offers a great tradeoff between model accuracy and efficiency. Efficiency (throughput) directly translates to savings. Using a novel Neural Architecture Search (NAS) approach, we greatly reduce the model\u2019s memory footprint, enabling larger workloads, as well as fitting the model on a single GPU at high workloads (H200). This NAS approach enables the selection of a desired point in the accuracy-efficiency tradeoff.\n\nThe model underwent a multi-phase post-training process to enhance both its reasoning and non-reasoning capabilities. This includes a supervised fine-tuning stage for Math, Code, Reasoning, and Tool Calling as well as multiple reinforcement learning (RL) stages using REINFORCE (RLOO) and Online Reward-aware Preference Optimization (RPO) algorithms for both chat and instruction-following. The final model checkpoint is obtained after merging the final SFT and Online RPO checkpoints. For more details on how the model was trained, please see [this blog](https://developer.nvidia.com/blog/build-enterprise-ai-agents-with-advanced-open-nvidia-llama-nemotron-reasoning-models/).\n\n![Architecture Diagram](https://assets.ngc.nvidia.com/products/api-catalog/llama-3_3-nemotron-super-49b-v1/diagram.jpg)\n\nThis model is part of the Llama Nemotron Collection. You can find the other model(s) in this family here: \n[Llama-3_1-Nemotron-Nano-8B-v1](https://build.nvidia.com/nvidia/llama-3_1-nemotron-nano-8b-v1)\n\nThis model is ready for commercial use. \n\n## License/Terms of Use\n\nGOVERNING TERMS: Your use of this model is governed by the [NVIDIA Open Model License.](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/) Additional Information: [Llama 3.3 Community License Agreement](https://www.llama.com/llama3_3/license/). Built with Llama.\n\n**You are responsible for ensuring that your use of NVIDIA AI Foundation Models complies with all applicable laws.**\n\n**Model Developer:** NVIDIA\n\n**Model Dates:** Trained between November 2024 and February 2025\n\n**Data Freshness:**  The pretraining data has a cutoff of 2023 per Meta Llama 3.3 70B\n\n### Use Case: <br>\nDevelopers designing AI Agent systems, chatbots, RAG systems, and other AI-powered applications. Also suitable for typical instruction-following tasks. <br>\n\n### Release Date:  <br>\n3/18/2025 <br>\n\n## References\n* [2502.00203] [Reward-aware Preference Optimization: A Unified Mathematical Framework for Model Alignment](https://arxiv.org/abs/2502.00203)\n\n## Model Architecture\n**Architecture Type:** Dense decoder-only Transformer model  \n**Network Architecture:** Llama 3.3 70B Instruct, customized through Neural Architecture Search (NAS)\n\nThe model is a derivative of Meta\u2019s Llama-3.3-70B-Instruct, using Neural Architecture Search (NAS). The NAS algorithm results in non-standard and non-repetitive blocks. This includes the following: \nSkip attention: In some blocks, the attention is skipped entirely, or replaced with a single linear layer.\nVariable FFN: The expansion/compression ratio in the FFN layer is different between blocks. \n\nWe utilize a block-wise distillation of the reference model, where for each block we create multiple variants providing different tradeoffs of quality vs. computational complexity, discussed in more depth below. We then search over the blocks to create a model which meets the required throughput and memory (optimized for a single H100-80GB GPU) while minimizing the quality degradation. The model then undergoes knowledge distillation (KD), with a focus on English single and multi-turn chat use-cases. The KD step included 40 billion tokens consisting of a mixture of 3 datasets - FineWeb, Buzz-V1.2 and Dolma.\n\n## Intended use\n\nLlama-3.3-Nemotron-Super-49B-v1 is a general purpose reasoning and chat model intended to be used in English and coding languages. Other non-English languages (German, French, Italian, Portuguese, Hindi, Spanish, and Thai) are also supported. \n\n## Input\n- **Input Type:** Text\n- **Input Format:** String\n- **Input Parameters:** One-Dimensional (1D)\n- **Other Properties Related to Input:** Context length up to 131,072 tokens\n\n## Output\n- **Output Type:** Text\n- **Output Format:** String\n- **Output Parameters:** One-Dimensional (1D)\n- **Other Properties Related to Output:** Context length up to 131,072 tokens\n\n## Model Version\n1.0 (3/18/2025)\n\n## Software Integration\n- **Runtime Engine:** Transformers\n- **Recommended Hardware Microarchitecture Compatibility:** \n   - NVIDIA Hopper\n   - NVIDIA Ampere\n\n## Quick Start and Usage Recommendations:\n\n1. Reasoning mode (ON/OFF) is controlled via the system prompt, which must be set as shown in the example below. All instructions should be contained within the user prompt\n2. We recommend setting temperature to `0.6`, and Top P to `0.95` for Reasoning ON mode\n3. We recommend using greedy decoding for Reasoning OFF mode\n4. We have provided a list of prompts to use for evaluation for each benchmark where a specific template is required\n\nYou can try this model out through the preview API, using this link: [Llama-3_3-Nemotron-Super-49B-v1](https://build.nvidia.com/nvidia/llama-3_3-nemotron-super-49b-v1).\n\n## Inference:\n**Engine:**\nTransformers  \n**Test Hardware:**\n- FP8: 1x NVIDIA H100-80GB GPU (Coming Soon)\n- BF16: \n   - 2x NVIDIA H100-80GB GPUs\n   - 2x NVIDIA A100-80GB GPUs\n      \n**[Preferred/Supported] Operating System(s):** Linux <br>\n\n## Training Datasets\n\nA large variety of training data was used for the knowledge distillation phase before post-training pipeline, 3 of which included: FineWeb, Buzz-V1.2, and Dolma.\n\nThe data for the multi-stage post-training phases for improvements in Code, Math, and Reasoning is a compilation of SFT and RL data that supports improvements of math, code, general reasoning, and instruction following capabilities of the original Llama instruct model. \n\nIn conjunction with this model release, NVIDIA has released 30M samples of post-training data, as public and permissive. [Llama-Nemotron-Post-Training-Dataset-v1](https://huggingface.co/datasets/nvidia/Llama-Nemotron-Post-Training-Dataset-v1)\n\nDistribution of the domains is as follows:\n\n| Category | Value     |\n|----------|-----------|\n| math     | 19,840,970|\n| code     | 9,612,677 |\n| science     | 708,920    |\n| instruction following       | 56,339    |\n| chat     | 39,792    |\n| safety   | 31,426    |\n\nPrompts have been sourced from either public and open corpus or synthetically generated. Responses were synthetically generated by a variety of models, with some prompts containing responses for both reasoning on and off modes, to train the model to distinguish between two modes. \n\nModels that were used in the creation of this dataset:\n- Llama-3.3-70B-Instruct\n- Llama-3.1-Nemotron-70B-Instruct\n- Llama-3.3-Nemotron-70B-Feedback/Edit/Select\n- Mixtral-8x22B-Instruct-v0.1\n- DeepSeek-R1\n- Qwen-2.5-Math-7B-Instruct\n- Qwen-2.5-Coder-32B-Instruct\n- Qwen-2.5-72B-Instruct\n- Qwen-2.5-32B-Instruct\n\n**Data Collection for Training Datasets:**\nHybrid: Automated, Human, Synthetic\n\n**Data Labeling for Training Datasets:**\nHybrid: Automated, Human, Synthetic\n\n## Evaluation Datasets \n\nWe used the datasets listed below to evaluate Llama-3.3-Nemotron-Super-49B-v1. \n\n**Data Collection for Evaluation Datasets:**\nHybrid: Human/Synthetic\n\n**Data Labeling for Evaluation Datasets:**\nHybrid: Human/Synthetic/Automatic\n\n## Evaluation Results\nThese results contain both Reasoning On, and Reasoning Off. We recommend using temperature=`0.6`, top_p=`0.95` for Reasoning On mode, and greedy decoding for Reasoning Off mode. All evaluations are done with 32k sequence length. We run the benchmarks up to 16 times and average the scores to be more accurate.\n\n> NOTE: Where applicable, a Prompt Template will be provided. While completing benchmarks, please ensure that you are parsing for the correct output format as per the provided prompt in order to reproduce the benchmarks seen below. \n\n### Arena-Hard\n\n| Reasoning Mode | Score |\n|--------------|------------|\n| Reasoning Off | 88.3 | \n\n\n### MATH500\n\n| Reasoning Mode | pass@1 |\n|--------------|------------|\n| Reasoning Off | 74.0 | \n| Reasoning On | 96.6  |\n\nUser Prompt Template: \n```\n\"Below is a math question. I want you to reason through the steps and then give a final answer. Your final answer should be in \\boxed{}.\\nQuestion: {question}\"\n```\n\n### AIME25\n\n| Reasoning Mode | pass@1 |\n|--------------|------------|\n| Reasoning Off | 13.33 | \n| Reasoning On | 58.4 |\n\nUser Prompt Template: \n```\n\"Below is a math question. I want you to reason through the steps and then give a final answer. Your final answer should be in \\boxed{}.\\nQuestion: {question}\"\n```\n\n### GPQA\n\n| Reasoning Mode | pass@1 |\n|--------------|------------|\n| Reasoning Off | 50 | \n| Reasoning On | 66.67 |\n\nUser Prompt Template: \n```\n\"What is the correct answer to this question: {question}\\nChoices:\\nA. {option_A}\\nB. {option_B}\\nC. {option_C}\\nD. {option_D}\\nLet's think step by step, and put the final answer (should be a single letter A, B, C, or D) into a \\boxed{}\"\n```\n\n### IFEval\n\n| Reasoning Mode | Strict:Instruction |\n|--------------|------------|\n| Reasoning Off | 89.21 | \n\n### BFCL V2 Live\n\n| Reasoning Mode | Score |\n|--------------|------------|\n| Reasoning Off | 73.7 | \n\nUser Prompt Template:\n```\nYou are an expert in composing functions. You are given a question and a set of possible functions. \nBased on the question, you will need to make one or more function/tool calls to achieve the purpose. \nIf none of the function can be used, point it out. If the given question lacks the parameters required by the function,\nalso point it out. You should only return the function call in tools call sections.\n\nIf you decide to invoke any of the function(s), you MUST put it in the format of <TOOLCALL>[func_name1(params_name1=params_value1, params_name2=params_value2...), func_name2(params)]</TOOLCALL>\n\nYou SHOULD NOT include any other text in the response.\nHere is a list of functions in JSON format that you can invoke.\n\n<AVAILABLE_TOOLS>{functions}</AVAILABLE_TOOLS>\n\n{user_prompt}\n```\n\n### MBPP 0-shot\n\n| Reasoning Mode | pass@1 |\n|--------------|------------|\n| Reasoning Off | 84.9| \n| Reasoning On | 91.3 |\n\nUser Prompt Template:\n````\nYou are an exceptionally intelligent coding assistant that consistently delivers accurate and reliable responses to user instructions.\n\n@@ Instruction\nHere is the given problem and test examples:\n{prompt}\nPlease use the python programming language to solve this problem.\nPlease make sure that your code includes the functions from the test samples and that the input and output formats of these functions match the test samples.\nPlease return all completed codes in one code block.\nThis code block should be in the following format:\n```python\n# Your codes here\n```\n````\n\n### MT-Bench\n\n| Reasoning Mode | Score |\n|--------------|------------|\n| Reasoning Off | 9.17 |\n\n## Ethical Considerations:\n\nNVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications.  When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. \n\nFor more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards, which you can find by clicking the [ModelCard++ tab](https://catalog.ngc.nvidia.com/orgs/nim/teams/nvidia/models/llama-3.3-nemotron-super-49b-v1/bias) above, next to Overview.\n\nPlease report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).",
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    "privacy": "|Field:|Response:|\n|:---:|:---:|\n|Generatable or Reverse engineerable personally-identifiable information?|None|\n|Was consent obtained for any personal data used?|None Known|\n|Personal data used to create this model?|None Known|\n|How often is dataset reviewed?|Before Release|\n|Is there provenance for all datasets used in training?|Yes|\n|Does data labeling (annotation, metadata) comply with privacy laws?|Not Applicable|\n|Is data compliant with data subject requests for data correction or removal, if such a request was made?|Not Applicable|\n|Applicable NVIDIA Privacy Policy|https://www.nvidia.com/en-us/about-nvidia/privacy-policy/|",
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    "safetyAndSecurity": "|Field:|Response:|\n|:---:|:---:|\n|Model Application(s):|Chat, Instruction Following, Chatbot Development, Code Generation, Reasoning|\n|Describe life critical application (if present):|None Known|\n|Use Case Restrictions:| Abide by the [NVIDIA Open Model License.](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/) Additional Information: [Llama 3.3 Community License Agreement](https://www.llama.com/llama3_3/license/). Built with Llama.|\n|Model and Dataset Restrictions:|The Principle of least privilege (PoLP) is applied limiting access for dataset generation.  Restrictions enforce dataset access during training, and dataset license constraints adhered to. Model checkpoints are made available on Hugging Face and NGC, and may become available on cloud providers' model catalog.|",
    "shortDescription": "Llama-3.3-Nemotron-Super-49B-v1 is a large language model (LLM) which is a derivative of Meta\u2019s Llama-3.3-70B-Instruct (AKA the reference model).",
    "teamName": "nvidia",
    "updatedDate": "2025-07-16T16:58:34.843Z"
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    "bias": "### **Bias**\n\n|Field:|Response:|\n|:---:|:---:|\n|Participation considerations from adversely impacted groups (protected classes) in model design and testing:|None|\n|Measures taken to mitigate against unwanted bias:|None|",
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    "createdDate": "2025-09-16T20:19:24.618Z",
    "description": "## **Llama-3.3-Nemotron-Super-49B-v1.5 Overview**\n\n## **Description:**\n\n**Llama-3.3-Nemotron-Super-49B-v1.5** is a significantly upgraded version of Llama-3.3-Nemotron-Super-49B-v1 and is a large language model (LLM) which is a derivative of Meta Llama-3.3-70B-Instruct (AKA the reference model). It is a reasoning model that is post trained for reasoning, human chat preferences, and agentic tasks, such as Retrieval-Augmented Generation (RAG) and tool calling. The model supports a context length of 128K tokens.\n\nLlama-3.3-Nemotron-Super-49B-v1.5 is a model which offers a great tradeoff between model accuracy and efficiency. Efficiency (throughput) directly translates to savings. Using a novel Neural Architecture Search (NAS) approach, we greatly reduce the model\u2019s memory footprint, enabling larger workloads, as well as fitting the model on a single GPU at high workloads (H200). This NAS approach enables the selection of a desired point in the accuracy-efficiency tradeoff. For more information on the NAS approach, please refer to [this paper](https://arxiv.org/abs/2411.19146).\n\nThe model underwent a multi-phase post-training process to enhance both its reasoning and non-reasoning capabilities. This includes a supervised fine-tuning stage for Math, Code, Science, and Tool Calling. Additionally, the model went through multiple stages of Reinforcement Learning (RL) including Reward-aware Preference Optimization (RPO) for chat, Reinforcement Learning with Verifiable Rewards (RLVR) for reasoning, and iterative Direct Preference Optimization (DPO) for Tool Calling capability enhancements. The final checkpoint was achieved after merging several RL and DPO checkpoints.\n\nThis model is part of the Llama Nemotron Collection. You can find the other model(s) in this family here:\n\n* [Llama-3.1-Nemotron-Nano-4B-v1.1](https://huggingface.co/nvidia/Llama-3.1-Nemotron-Nano-4B-v1.1)  \n* [Llama-3.1-Nemotron-Ultra-253B-v1](https://huggingface.co/nvidia/Llama-3_1-Nemotron-Ultra-253B-v1)\n\nThis model is ready for commercial use.\n\n## **License/Terms of Use**\n\n**GOVERNING TERMS:** The NIM container is governed by the [NVIDIA Software License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-license-agreement/) and the [Product-Specific Terms for NVIDIA AI Products](https://www.nvidia.com/en-us/agreements/enterprise-software/product-specific-terms-for-ai-products/); and the use of this model is governed by the [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/).\n\n**Additional Information:** [Llama 3.3 Community License Agreement](https://www.llama.com/llama3_3/license/). Built with Llama.\n\n##**Get Help**\n\n### Enterprise Support\nGet access to knowledge base articles and support cases or [submit a ticket](https://www.nvidia.com/en-us/data-center/products/ai-enterprise-suite/support/).\n**You are responsible for ensuring that your use of NVIDIA provided models complies with all applicable laws.**\n\n## **Deployment Geography:**\n\nGlobal\n\n## **Use Case:**\n\nLlama-3.3-Nemotron-Super-49B-v1.5 is a general purpose reasoning and chat model intended to be used in English and coding languages. Other non-English languages (German, French, Italian, Portuguese, Hindi, Spanish, and Thai) are also supported. Developers designing AI Agent systems, chatbots, RAG systems, and other AI-powered applications. Also suitable for typical instruction-following tasks.\n\n## **Release Date:**\n\n* Hugging Face 7/25/2025 via [Llama-3\\_3-Nemotron-Super-49B-v1\\_5](https://huggingface.co/nvidia/Llama-3_3-Nemotron-Super-49B-v1_5)  \n* Build.NVIDIA.com 7/25/2025 [Llama-3\\_3-Nemotron-Super-49B-v1\\_5](https://build.nvidia.com/nvidia/llama-3_3-nemotron-super-49b-v1_5)\n\n## **References**\n\n* [\\[2505.00949\\] Llama-Nemotron: Efficient Reasoning Models](https://arxiv.org/abs/2505.00949)  \n* [\\[2502.00203\\] Reward-aware Preference Optimization: A Unified Mathematical Framework for Model Alignment](https://arxiv.org/abs/2502.00203)  \n* [\\[2411.19146\\]Puzzle: Distillation-Based NAS for Inference-Optimized LLMs](https://arxiv.org/abs/2411.19146)\n\n## **Model Architecture**\n\n**Architecture Type:** Dense decoder-only Transformer model\n\n**Network Architecture:** Llama 3.3 70B Instruct, customized through Neural Architecture Search (NAS)\n\nThe model is a derivative of Meta\u2019s Llama-3.3-70B-Instruct, using Neural Architecture Search (NAS). The NAS algorithm results in non-standard and non-repetitive blocks. This includes the following:\n\nSkip attention: In some blocks, the attention is skipped entirely, or replaced with a single linear layer. Variable FFN: The expansion/compression ratio in the FFN layer is different between blocks.\n\nWe utilize a block-wise distillation of the reference model, where for each block we create multiple variants providing different tradeoffs of quality vs. computational complexity, discussed in more depth below. We then search over the blocks to create a model which meets the required throughput and memory (optimized for a single H100-80GB GPU) while minimizing the quality degradation. The model then undergoes knowledge distillation (KD), with a focus on English single and multi-turn chat use cases. The KD step included 40 billion tokens consisting of a mixture of 3 datasets \\- FineWeb, Buzz-V1.2, and Dolma.\n\n**Intended Use**  \nLlama-3.3-Nemotron-Super-49B-v1.5 is a general purpose reasoning and chat model intended to be used in English and coding languages. Other non-English languages (German, French, Italian, Portuguese, Hindi, Spanish, and Thai) are also supported.\n\n## **Input**\n\n* **Input Type(s):** Text  \n* **Input Format:** String  \n* **Input Parameters:** One-Dimensional (1D)  \n* **Other Properties Related to Input:** Context length up to 131,072 tokens\n\n## **Output**\n\n* **Output Type(s):** Text  \n* **Output Format:** String  \n* **Output Parameters:** One-Dimensional (1D)  \n* **Other Properties Related to Output:** Context length up to 131,072 tokens\n\nOur AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA\u2019s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.\n\n## **Software Integration:**\n\n* **Runtime Engine:** vLLM, TensorRT  \n* **Supported Hardware Microarchitecture Compatibility:**  \n  * NVIDIA Ampere  \n  * NVIDIA Hopper  \n* **Preferred Operating System(s):** Linux\n\nThe integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.\n\n## **Model Version:**\n\nLlama-3.3-Nemotron-Super-49B-v1.5-1.12.0 <br>\nLlama-3.3-Nemotron-Super-49B-v1.5-1.13.1 <br>\nLlama-3.3-Nemotron-Super-49B-v1.5-1.14.0 <br>\n\n## **Quick Start and Usage Recommendations:**\n\n* By default (empty system prompt), the model will respond in reasoning ON mode. Setting /no\\_think in the system prompt will enable reasoning OFF mode.  \n* We recommend setting temperature to 0.6 and Top P to 0.95 for Reasoning ON mode.  \n* We recommend using greedy decoding for Reasoning OFF mode.\n\nYou can try this model out through the preview API, using this link: [Llama-3\\_3-Nemotron-Super-49B-v1\\_5](https://build.nvidia.com/nvidia/llama-3_3-nemotron-super-49b-v1_5).\n\n**Use It with vLLM**\n\npip install vllm==0.9.2\n\nAn example on how to serve with vLLM:\n\n```\n$ python3 -m vllm.entrypoints.openai.api_server \\\n  --model \"nvidia/Llama-3_3-Nemotron-Super-49B-v1_5\" \\\n  --trust-remote-code \\\n  --seed=1 \\\n  --host=\"0.0.0.0\" \\\n  --port=5000 \\\n  --served-model-name \"Llama-3_3-Nemotron-Super-49B-v1_5\" \\\n  --tensor-parallel-size=8 \\\n  --max-model-len=65536 \\\n  --gpu-memory-utilization 0.95 \\\n  --enforce-eager\n```\n\n**Running a vLLM Server with Tool-call Support**\n\nTo enable tool calling usage with this model, we provide a tool parser in the repository. Here is an example on how to use it:\n\n```\n$ git clone https://huggingface.co/nvidia/Llama-3_3-Nemotron-Super-49B-v1_5\n\n$ conda create -n vllm python=3.12 -y\n$ conda activate vllm\n$ pip install vllm==0.9.2\n\n$ python3 -m vllm.entrypoints.openai.api_server \\\n  --model Llama-3_3-Nemotron-Super-49B-v1_5 \\\n  --trust-remote-code \\\n  --seed=1 \\\n  --host=\"0.0.0.0\" \\\n  --port=5000 \\\n  --served-model-name \"Llama-3_3-Nemotron-Super-49B-v1_5\" \\\n  --tensor-parallel-size=8 \\\n  --max-model-len=65536 \\\n  --gpu-memory-utilization 0.95 \\\n  --enforce-eager \\\n  --enable-auto-tool-choice \\\n  --tool-parser-plugin \"Llama-3_3-Nemotron-Super-49B-v1_5/llama_nemotron_toolcall_parser_no_streaming.py\" \\\n  --tool-call-parser \"llama_nemotron_json\"\n```\n\nAfter launching a vLLM server, you can call the server with tool-call support using a Python script like below.\n\n```py\nfrom openai import OpenAI\nclient = OpenAI(\n    base_url=\"http://0.0.0.0:5000/v1\",\n    api_key=\"dummy\",\n)\ncompletion = client.chat.completions.create(\n    model=\"Llama-3_3-Nemotron-Super-49B-v1_5\",\n    messages=[\n        {\"role\": \"system\", \"content\": \"\"},\n        {\"role\": \"user\", \"content\": \"My bill is $100. What will be the amount for 18% tip?\"}\n    ],\n    tools=[\n        {\n            \"type\": \"function\",\n            \"function\": {\n                \"name\": \"calculate_tip\",\n                \"parameters\": {\n                    \"type\": \"object\",\n                    \"properties\": {\n                        \"bill_total\": {\n                            \"type\": \"integer\",\n                            \"description\": \"The total amount of the bill\"\n                        },\n                        \"tip_percentage\": {\n                            \"type\": \"integer\",\n                            \"description\": \"The percentage of tip to be applied\"\n                        }\n                    },\n                    \"required\": [\"bill_total\", \"tip_percentage\"]\n                }\n            }\n        },\n        {\n            \"type\": \"function\",\n            \"function\": {\n                \"name\": \"convert_currency\",\n                \"parameters\": {\n                    \"type\": \"object\",\n                    \"properties\": {\n                        \"amount\": {\n                            \"type\": \"integer\",\n                            \"description\": \"The amount to be converted\"\n                        },\n                        \"from_currency\": {\n                            \"type\": \"string\",\n                            \"description\": \"The currency code to convert from\"\n                        },\n                        \"to_currency\": {\n                            \"type\": \"string\",\n                            \"description\": \"The currency code to convert to\"\n                        }\n                    },\n                    \"required\": [\"from_currency\", \"amount\", \"to_currency\"]\n                }\n            }\n        }\n    ],\n    temperature=0.6,\n    top_p=0.95,\n    max_tokens=32768,\n    stream=False\n)\nprint(completion.choices[0].message.content)\n'''\n<think>\nOkay, let's see. The user has a bill of $100 and wants to know the amount for an 18% tip. Hmm, I need to calculate the tip based on the bill total and the percentage. The tools provided include calculate_tip, which takes bill_total and tip_percentage as parameters. So the bill_total here is 100, and the tip_percentage is 18. I should call the calculate_tip function with these values. Wait, do I need to check if the parameters are integers? The bill is $100, which is an integer, and 18% is also an integer. So that fits the function's requirements. I don't need to convert any currency here because the user is asking about a tip in the same currency. So the correct tool to use is calculate_tip with those parameters.\n</think>\n'''\nprint(completion.choices[0].message.tool_calls)\n'''\n[ChatCompletionMessageToolCall(id='chatcmpl-tool-e341c6954d2c48c2a0e9071c7bdefd8b', function=Function(arguments='{\"bill_total\": 100, \"tip_percentage\": 18}', name='calculate_tip'), type='function')]\n'''\n```\n\n## **Training, Testing, and Evaluation Datasets:**\n\n### **Training Dataset:**\n\nA large variety of training data was used for the knowledge distillation phase before the post-training pipeline, 3 of which included: FineWeb, Buzz-V1.2, and Dolma.\n\nThe data for the multi-stage post-training phases for improvements in Code, Math, and Reasoning is a compilation of SFT and RL data that supports improvements of math, code, general reasoning, and instruction following capabilities of the original Llama instruct model.\n\nPrompts have been sourced from either public and open corpus or synthetically generated. Responses were synthetically generated by a variety of models, with some prompts containing responses for both reasoning on and off modes, to train the model to distinguish between two modes.\n\nNVIDIA will be releasing the post-training dataset in the coming weeks.\n\n**Data Modality:** Text \n\n**Data Collection Method by dataset:** Hybrid: Automated, Human, Synthetic\n\n**Labeling Method by dataset:** Hybrid: Automated, Human, Synthetic\n\n**Properties (Quantity, Dataset Descriptions, Sensor(s)):** \n\nQuantity: The knowledge distillation phase used 40 billion tokens. The underlying Dolma dataset contains approximately 3 trillion tokens, and FineWeb contains over 15 trillion tokens.\n\nDataset Descriptions: The training data is a composite of large-scale web text, synthetically generated conversational data for coding and instruction-following, and a diverse corpus of text from academic, literary, and encyclopedic sources. The model supports a context length of 128,000 tokens.\n\n### \n\n### **Testing Dataset:**\n\n**Data Collection Method by dataset:** Hybrid: Human, Synthetic\n\n**Labeling Method by dataset:** Hybrid: Human, Automated\n\n**Properties (Quantity, Dataset Descriptions, Sensor(s)):**\n\nQuantity: The benchmarks contain thousands of test items, including:\n\n* MMLU: \\~15,900 multiple-choice questions.  \n* GPQA: 448 difficult multiple-choice questions.  \n* HumanEval: 164 programming problems.  \n* GSM-8K: \\~8,500 grade-school math problems.  \n* MATH: 12,500 competition math problems.  \n* IF-Eval: Over 400 instruction-following prompts.\n\nDataset Descriptions: The evaluation suite is diverse, covering 57 academic and professional subjects (MMLU), expert-level reasoning (GPQA), Python code generation (HumanEval), mathematical problem-solving (GSM-8K, MATH), and the ability to follow precise instructions (IF-Eval).\n\n### **Evaluation Dataset:**\n\nWe used the datasets listed below to evaluate Llama-3.3-Nemotron-Super-49B-v1.5.\n\n**Data Collection Method by dataset:** Hybrid: Human. Synthetic\n\n**Labeling Method by dataset:** Hybrid: Human, Synthetic, Automatic\n\n**Evaluation Results**  \nWe evaluate the model using temperature=0.6, top\\_p=0.95, and 64k sequence length. We run the benchmarks up to 16 times and average the scores to be more accurate.\n\n### MATH500\n\n| Reasoning Mode | pass@1 (avg. over 4 runs) |\n| ----- | ----- |\n| Reasoning On | 97.4 |\n\n### AIME 2024\n\n| Reasoning Mode | pass@1 (avg. over 16 runs) |\n| ----- | ----- |\n| Reasoning On | 87.5 |\n\n### AIME 2025\n\n| Reasoning Mode | pass@1 (avg. over 16 runs) |\n| ----- | ----- |\n| Reasoning On | 82.71 |\n\n### GPQA\n\n| Reasoning Mode | pass@1 (avg. over 4 runs) |\n| ----- | ----- |\n| Reasoning On | 71.97 |\n\n### LiveCodeBench 24.10-25.02\n\n| Reasoning Mode | pass@1 (avg. over 4 runs) |\n| ----- | ----- |\n| Reasoning On | 73.58 |\n\n### BFCL v3\n\n| Reasoning Mode | pass@1 (avg. over 2 runs) |\n| ----- | ----- |\n| Reasoning On | 71.75 |\n\n### IFEval\n\n| Reasoning Mode | Strict:Instruction |\n| ----- | ----- |\n| Reasoning On | 88.61 |\n\n### ArenaHard\n\n| Reasoning Mode | pass@1 (avg. over 1 runs) |\n| ----- | ----- |\n| Reasoning On | 92.0 |\n\n### Humanity's Last Exam (Text-Only Subset)\n\n| Reasoning Mode | pass@1 (avg. over 1 runs) |\n| ----- | ----- |\n| Reasoning On | 7.64 |\n\n### MMLU Pro (CoT)\n\n| Reasoning Mode | pass@1 (avg. over 1 runs) |\n| ----- | ----- |\n| Reasoning On | 79.53 |\n\nAll evaluations were done using the [NeMo-Skills](https://github.com/NVIDIA/NeMo-Skills) repository.\n\n## **Inference:**\n\n**Acceleration Engine:**\n\n* vLLM, TensorRT\n\n**Test Hardware:**\n\n* 2x NVIDIA H100-80GB  \n* 2x NVIDIA A100-80GB GPUs\n* B200 SXM    \n* H200 SXM   \n* H100 SXM   \n* A100 SXM 80GB   \n* A100 SXM 40GB   \n* L40S PCIe   \n* A10G   \n* H100 NVL   \n* H200 NVL   \n* GH200 96GB   \n* GB200 \n* RTX 5090   \n* RTX 4090   \n* RTX 6000 Ada \n\n## **Ethical Considerations:**\n\nNVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.\n\nFor more detailed information on ethical considerations for this model, please see the Model Card++ [Bias](http://./bias.md), [Explainability](http://./explainability.md), [Safety & Security](http://./safety.md), and [Privacy](http://./privacy.md) Subcards.\n\nPlease report model quality, risk, security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).\n\nYou are responsible for ensuring that your use of NVIDIA provided models complies with all applicable laws.\n\n## **Citation**\n\n```py\n@misc{bercovich2025llamanemotronefficientreasoningmodels,\n      title={Llama-Nemotron: Efficient Reasoning Models}, \n      author={Akhiad Bercovich and Itay Levy and Izik Golan and Mohammad Dabbah and Ran El-Yaniv and Omri Puny and Ido Galil and Zach Moshe and Tomer Ronen and Najeeb Nabwani and Ido Shahaf and Oren Tropp and Ehud Karpas and Ran Zilberstein and Jiaqi Zeng and Soumye Singhal and Alexander Bukharin and Yian Zhang and Tugrul Konuk and Gerald Shen and Ameya Sunil Mahabaleshwarkar and Bilal Kartal and Yoshi Suhara and Olivier Delalleau and Zijia Chen and Zhilin Wang and David Mosallanezhad and Adi Renduchintala and Haifeng Qian and Dima Rekesh and Fei Jia and Somshubra Majumdar and Vahid Noroozi and Wasi Uddin Ahmad and Sean Narenthiran and Aleksander Ficek and Mehrzad Samadi and Jocelyn Huang and Siddhartha Jain and Igor Gitman and Ivan Moshkov and Wei Du and Shubham Toshniwal and George Armstrong and Branislav Kisacanin and Matvei Novikov and Daria Gitman and Evelina Bakhturina and Jane Polak Scowcroft and John Kamalu and Dan Su and Kezhi Kong and Markus Kliegl and Rabeeh Karimi and Ying Lin and Sanjeev Satheesh and Jupinder Parmar and Pritam Gundecha and Brandon Norick and Joseph Jennings and Shrimai Prabhumoye and Syeda Nahida Akter and Mostofa Patwary and Abhinav Khattar and Deepak Narayanan and Roger Waleffe and Jimmy Zhang and Bor-Yiing Su and Guyue Huang and Terry Kong and Parth Chadha and Sahil Jain and Christine Harvey and Elad Segal and Jining Huang and Sergey Kashirsky and Robert McQueen and Izzy Putterman and George Lam and Arun Venkatesan and Sherry Wu and Vinh Nguyen and Manoj Kilaru and Andrew Wang and Anna Warno and Abhilash Somasamudramath and Sandip Bhaskar and Maka Dong and Nave Assaf and Shahar Mor and Omer Ullman Argov and Scot Junkin and Oleksandr Romanenko and Pedro Larroy and Monika Katariya and Marco Rovinelli and Viji Balas and Nicholas Edelman and Anahita Bhiwandiwalla and Muthu Subramaniam and Smita Ithape and Karthik Ramamoorthy and Yuting Wu and Suguna Varshini Velury and Omri Almog and Joyjit Daw and Denys Fridman and Erick Galinkin and Michael Evans and Katherine Luna and Leon Derczynski and Nikki Pope and Eileen Long and Seth Schneider and Guillermo Siman and Tomasz Grzegorzek and Pablo Ribalta and Monika Katariya and Joey Conway and Trisha Saar and Ann Guan and Krzysztof Pawelec and Shyamala Prayaga and Oleksii Kuchaiev and Boris Ginsburg and Oluwatobi Olabiyi and Kari Briski and Jonathan Cohen and Bryan Catanzaro and Jonah Alben and Yonatan Geifman and Eric Chung and Chris Alexiuk},\n      year={2025},\n      eprint={2505.00949},\n      archivePrefix={arXiv},\n      primaryClass={cs.CL},\n      url={https://arxiv.org/abs/2505.00949}, \n}\n```",
    "displayName": "Llama-3.3-nemotron-super-49b-v1.5",
    "explainability": "### **Explainability**\n\n| Field: | Response: |\n| :---- | :---- |\n| Intended Application(s) & Domain(s): | Text generation, reasoning, summarization, and question answering.  |\n| Model Type: | Text-to-text transformer |\n| Intended Users: | This model is intended for developers, researchers, and customers building/utilizing LLMs, while balancing accuracy and efficiency. |\n| Output: | Text String(s) |\n| Describe how the model works: | Generates text by predicting the next word or token based on the context provided in the input sequence using multiple self-attention layers. |\n| Technical Limitations: | The model was trained on data that contains toxic language, unsafe content, and societal biases originally crawled from the internet. Therefore, the model may amplify those biases and return toxic responses especially when prompted with toxic prompts. The model may generate answers that may be inaccurate, omit key information, or include irrelevant or redundant text producing socially unacceptable or undesirable text, even if the prompt itself does not include anything explicitly offensive. The model demonstrates weakness to alignment-breaking attacks. Users are advised to deploy language model guardrails alongside this model to prevent potentially harmful outputs. The Model may generate answers that are inaccurate, omit key information, or include irrelevant or redundant text. |\n| Verified to have met prescribed quality standards? | Yes |\n| Performance Metrics: | Accuracy, Throughput, and user-side throughput |\n| Potential Known Risks: | The model was optimized explicitly for instruction following and as such is susceptible to prompt injection and jailbreaking in various forms as a result of its instruction tuning. The model should be paired with additional rails or system filtering to limit exposure to instructions from malicious sources -- either directly or indirectly by retrieval (e.g. via visiting a website) -- as they may yield outputs that can lead to harmful, system-level outcomes up to and including remote code execution in agentic systems when effective security controls including guardrails are not in place.The model was trained on data that contains toxic language and societal biases originally crawled from the internet. Therefore, the model may amplify those biases and return toxic responses especially when prompted with toxic prompts. The model may generate answers that may be inaccurate, omit key information, or include irrelevant or redundant text producing socially unacceptable or undesirable text, even if the prompt itself does not include anything explicitly offensive. Model output should be appropriately escaped before viewing or other processing.|\n| End User License Agreement: | Your use of this model is governed by the [NVIDIA Software License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-license-agreement/) and the [Product-Specific Terms for NVIDIA AI Products](https://www.nvidia.com/en-us/agreements/enterprise-software/product-specific-terms-for-ai-products/); and the use of this model is governed by the [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/). Additional Information: [Llama 3.3 Community License Agreement](https://www.llama.com/llama3_3/license/). Built with Llama. |",
    "framework": "Other",
    "hasPlayground": false,
    "hasSignedVersion": true,
    "isPlaygroundEnabled": false,
    "isPublic": false,
    "isReadOnly": true,
    "labels": [
        "NSPECT-8J48-AV9D",
        "nvaie:model:nvaie_supported",
        "nvidia_nim:model:nimmcro_nvidia_nim",
        "productNames:nim-dev",
        "productNames:nv-ai-enterprise",
        "technology:model:soln_nvidia_ai"
    ],
    "latestVersionIdStr": "rtx6000-blackwell-svx4-latency-bf16-ymougp5ifg",
    "latestVersionSizeInBytes": 107695648037,
    "logo": "https://catalog.ngc.nvidia.com/_next/image?url=https%3A%2F%2Fassets.ngc.nvidia.com%2Fproducts%2Fapi-catalog%2Fimages%2Fllama-3_3-nemotron-49b-instruct.jpg&w=640&q=90",
    "modelFormat": "SavedModel",
    "name": "llama-3.3-nemotron-super-49b-v1.5",
    "orgName": "nim",
    "precision": "OTHER",
    "privacy": "### Privacy\n\n|Field:|Response:|\n|:---:|:---:|\n|Generatable or Reverse engineerable personally-identifiable information?|None|\n|Was consent obtained for any personal data used?|None Known|\n|Personal data used to create this model?|None Known|\n|How often is dataset reviewed?|Before Release|\n|Is there provenance for all datasets used in training?|Yes|\n|Does data labeling (annotation, metadata) comply with privacy laws?|Yes|\n|Applicable NVIDIA Privacy Policy|https://www.nvidia.com/en-us/about-nvidia/privacy-policy/|",
    "productNames": [
        "nim-dev",
        "nv-ai-enterprise"
    ],
    "publicDatasetUsed": {},
    "publisher": "NVIDIA",
    "safetyAndSecurity": "### Safety & Security\n\n|Field:|Response:|\n|:---:|:---:|\n|Model Application(s):|Chat, Instruction Following, Chatbot Development, Code Generation, Reasoning|\n|Describe life critical application (if present):|None Known (please see referenced Known Risks in the Explainability subcard).|\n|Use Case Restrictions:|Your use of this model is governed by the [NVIDIA Software License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-license-agreement/) and the [Product-Specific Terms for NVIDIA AI Products](https://www.nvidia.com/en-us/agreements/enterprise-software/product-specific-terms-for-ai-products/); and the use of this model is governed by the [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/). Additional Information: [Llama 3.3 Community License Agreement](https://www.llama.com/llama3_3/license/). Built with Llama. |",
    "shortDescription": "Llama-3.3-Nemotron-Super-49B-v1.5 is a significantly upgraded version of Llama-3.3-Nemotron-Super-49B-v1 and is a large language model (LLM) which is a derivative of Meta Llama-3.3-70B-Instruct",
    "teamName": "nvidia",
    "updatedDate": "2025-10-17T18:04:03.572Z"
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NVIDIA Validated config: architectures: - Other modelType: llama license: NVIDIA AI Foundation Models Community License - name: Llama 3.1 Nemotron Nano displayName: Llama 3.1 Nemotron Nano modelHubID: llama-3.1-nemotron-nano category: Language type: NGC description: Llama 3.1 Nemotron Nano 8B or 4B is a language model that can follow instructions, complete requests, and generate creative text formats. requireLicense: true licenseAgreements: - label: Use Policy url: https://llama.meta.com/llama3/use-policy/ - label: License Agreement url: https://llama.meta.com/llama3/license/ modelVariants: - variantId: Llama 3.1 Nemotron Nano 4b V1.1 modelCard: {
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    "description": "## Model Overview \n\nLlama-3.1-Nemotron-Nano-4B-v1.1 is a large language model (LLM) reasoning model that is post trained for reasoning, human chat preferences, and tasks, such as RAG and tool calling. It  is a derivative of [nvidia/Llama-3.1-Minitron-4B-Width-Base](https://huggingface.co/nvidia/Llama-3.1-Minitron-4B-Width-Base), which is created from Llama 3.1 8B using [our LLM compression technique](https://arxiv.org/abs/2408.11796) and offers improvements in model accuracy and efficiency. \n\nLlama-3.1-Nemotron-Nano-4B-v1.1 is a model which offers a great tradeoff between model accuracy and efficiency. The model fits on a single RTX GPU and can be used locally. The model supports a context length of 128K.\n\nThis model underwent a multi-phase post-training process to enhance both its reasoning and non-reasoning capabilities. This includes a supervised fine-tuning stage for Math, Code, Reasoning, and Tool Calling as well as multiple reinforcement learning (RL) stages using Reward-aware Preference Optimization (RPO) algorithms for both chat and instruction-following. The final model checkpoint is obtained after merging the final SFT and RPO checkpoints\n\nThis model is part of the Llama Nemotron Collection. You can find the other model(s) in this family here: \n- [Llama-3.3-Nemotron-Ultra-253B-v1](https://huggingface.co/nvidia/Llama-3_1-Nemotron-Ultra-253B-v1)\n- [Llama-3.3-Nemotron-Super-49B-v1](https://huggingface.co/nvidia/Llama-3.3-Nemotron-Super-49B-v1)\n- [Llama-3.1-Nemotron-Nano-8B-v1](https://huggingface.co/nvidia/Llama-3.1-Nemotron-Nano-8B-v1)\n\nThis model is ready for commercial use.\n\n## License/Terms of Use\n\nThe NIM container is governed by the [NVIDIA Software License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-license-agreement) and [Product-Specific Terms for NVIDIA AI Products](https://www.nvidia.com/en-us/agreements/enterprise-software/product-specific-terms-for-ai-products). Your use of this model is governed by the [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/). Additional Information: [Llama 3.1 Community License Agreement](https://www.llama.com/llama3_1/license). Built with Llama.\n\n\n**You are responsible for ensuring that your use of NVIDIA AI Foundation Models complies with all applicable laws.**\n\n**Model Developer:** NVIDIA\n\n**Model Dates:** Trained between August 2024 and April 2025\n\n**Data Freshness:** The pretraining data has a cutoff of 2023 per Meta Llama 3.1 8B\n\n\n## Use Case: \n\nDevelopers designing AI Agent systems, chatbots, RAG systems, and other AI-powered applications. Also suitable for typical instruction-following tasks. Balance of model accuracy and compute efficiency (the model fits on a single RTX GPU and can be used locally).\n\n## Release Date: <br>\nNGC: May 2025 <br>\n\n## References\n\n- [\\[2502.00203\\] Reward-aware Preference Optimization: A Unified Mathematical Framework for Model Alignment](https://arxiv.org/abs/2502.00203)\n\n\n## Model Architecture\n\n**Architecture Type:** Dense decoder-only Transformer model\n\n**Network Architecture:** [Llama 3.1 Minitron Width 4B Base](https://huggingface.co/nvidia/Llama-3.1-Minitron-4B-Width-Base)\n\n## Intended use\n\nLlama-3.1-Nemotron-Nano-4B-v1.1 is a general purpose reasoning and chat model intended to be used in English and coding languages. Other non-English languages (German, French, Italian, Portuguese, Hindi, Spanish, and Thai) are also supported. \n\n# Input:\n- **Input Type:** Text\n- **Input Format:** String\n- **Input Parameters:** One-Dimensional (1D)\n- **Other Properties Related to Input:** Context length up to 131,072 tokens\n\n## Output:\n- **Output Type:** Text\n- **Output Format:** String\n- **Output Parameters:** One-Dimensional (1D)\n- **Other Properties Related to Output:** Context length up to 131,072 tokens\n\n## Model Version:\n1.0 (April 2025)\n\n## Software Integration\n- **Runtime Engine:** NeMo 24.12 <br>\n  \n- **Recommended Hardware Microarchitecture Compatibility:**\n    - NVIDIA Ampere\n    - NVIDIA Hopper\n- **Preferred/Supported Operating System:** Linux <br>\n\n## Quick Start and Usage Recommendations:\n\n1. Reasoning mode (ON/OFF) is controlled via the system prompt, which must be set as shown in the example below. All instructions should be contained within the user prompt\n2. We recommend setting temperature to `0.6`, and Top P to `0.95` for Reasoning ON mode\n3. We recommend using greedy decoding for Reasoning OFF mode\n4. We have provided a list of prompts to use for evaluation for each benchmark where a specific template is required\n\nSee the snippet below for usage with Hugging Face Transformers library. Reasoning mode (ON/OFF) is controlled via system prompt. Please see the example below.\nOur code requires the transformers package version to be `4.44.2` or higher.\n\n\n### Example of \u201cReasoning On:\u201d\n\n```python\nimport torch\nimport transformers\n\nmodel_id = \"nvidia/Llama-3.1-Nemotron-Nano-4B-v1.1\"\nmodel_kwargs = {\"torch_dtype\": torch.bfloat16, \"device_map\": \"auto\"}\ntokenizer = transformers.AutoTokenizer.from_pretrained(model_id)\ntokenizer.pad_token_id = tokenizer.eos_token_id\n\npipeline = transformers.pipeline(\n   \"text-generation\",\n   model=model_id,\n   tokenizer=tokenizer,\n   max_new_tokens=32768,\n   temperature=0.6,\n   top_p=0.95,\n   **model_kwargs\n)\n\n# Thinking can be \"on\" or \"off\"\nthinking = \"on\"\n\nprint(pipeline([{\"role\": \"system\", \"content\": f\"detailed thinking {thinking}\"}, {\"role\": \"user\", \"content\": \"Solve x*(sin(x)+2)=0\"}]))\n```\n\n\n### Example of \u201cReasoning Off:\u201d\n\n```python\nimport torch\nimport transformers\n\nmodel_id = \"nvidia/Llama-3.1-Nemotron-Nano-4B-v1\"\nmodel_kwargs = {\"torch_dtype\": torch.bfloat16, \"device_map\": \"auto\"}\ntokenizer = transformers.AutoTokenizer.from_pretrained(model_id)\ntokenizer.pad_token_id = tokenizer.eos_token_id\n\npipeline = transformers.pipeline(\n   \"text-generation\",\n   model=model_id,\n   tokenizer=tokenizer,\n   max_new_tokens=32768,\n   do_sample=False,\n   **model_kwargs\n)\n\n# Thinking can be \"on\" or \"off\"\nthinking = \"off\"\n\nprint(pipeline([{\"role\": \"system\", \"content\": f\"detailed thinking {thinking}\"}, {\"role\": \"user\", \"content\": \"Solve x*(sin(x)+2)=0\"}]))\n```\n\nFor some prompts, even though thinking is disabled, the model emergently prefers to think before responding. But if desired, the users can prevent it by pre-filling the assistant response.\n\n```python\nimport torch\nimport transformers\n\nmodel_id = \"nvidia/Llama-3.1-Nemotron-Nano-4B-v1.1\"\nmodel_kwargs = {\"torch_dtype\": torch.bfloat16, \"device_map\": \"auto\"}\ntokenizer = transformers.AutoTokenizer.from_pretrained(model_id)\ntokenizer.pad_token_id = tokenizer.eos_token_id\n\n# Thinking can be \"on\" or \"off\"\nthinking = \"off\"\n\npipeline = transformers.pipeline(\n   \"text-generation\",\n   model=model_id,\n   tokenizer=tokenizer,\n   max_new_tokens=32768,\n   do_sample=False,\n   **model_kwargs\n)\n\nprint(pipeline([{\"role\": \"system\", \"content\": f\"detailed thinking {thinking}\"}, {\"role\": \"user\", \"content\": \"Solve x*(sin(x)+2)=0\"}, {\"role\":\"assistant\", \"content\":\"<think>\\n</think>\"}]))\n```\n\n## Inference:\n**Test Hardware:**\n\n- BF16:\n    - 1x RTX 50 Series GPUs\n    - 1x RTX 40 Series GPUs\n    - 1x RTX 30 Series GPUs\n    - 1x H100-80GB GPU\n    - 1x A100-80GB GPU\n\n# Training and Evaluation Datasets \n\n## Training Datasets\n\nA large variety of training data was used for the post-training pipeline, including manually annotated data and synthetic data.\n\nThe data for the multi-stage post-training phases for improvements in Code, Math, and Reasoning is a compilation of SFT and RL data that supports improvements of math, code, general reasoning, and instruction following capabilities of the original Llama instruct model. \n\nPrompts have been sourced from either public and open corpus or synthetically generated. Responses were synthetically generated by a variety of models, with some prompts containing responses for both Reasoning On and Off modes, to train the model to distinguish between two modes. \n\n**Data Collection for Training Datasets:** <br>\n* Hybrid: Automated, Human, Synthetic <br>\n\n**Data Labeling for Training Datasets:** <br>\n* Not Applicable (N/A) <br>\n\n## Evaluation Datasets\n\nWe used the datasets listed below to evaluate Llama-3.1-Nemotron-Nano-4B-v1.1. \n\n**Data Collection for Evaluation Datasets:** Hybrid: Human/Synthetic\n\n**Data Labeling for Evaluation Datasets:** Hybrid: Human/Synthetic/Automatic\n\n## Evaluation Results\n\nThese results contain both \u201cReasoning On\u201d, and \u201cReasoning Off\u201d. We recommend using temperature=`0.6`, top_p=`0.95` for \u201cReasoning On\u201d mode, and greedy decoding for \u201cReasoning Off\u201d mode. All evaluations are done with 32k sequence length. We run the benchmarks up to 16 times and average the scores to be more accurate.\n\n> NOTE: Where applicable, a Prompt Template will be provided. While completing benchmarks, please ensure that you are parsing for the correct output format as per the provided prompt in order to reproduce the benchmarks seen below. \n\n### MT-Bench\n\n| Reasoning Mode | Score |\n|--------------|------------|\n| Reasoning Off | 7.6 |\n| Reasoning On | 8.1 |\n\n\n### MATH500\n\n| Reasoning Mode | pass@1 |\n|--------------|------------|\n| Reasoning Off | 72.0% | \n| Reasoning On | 95.1%  |\n\nUser Prompt Template: \n\n```\n\"Below is a math question. I want you to reason through the steps and then give a final answer. Your final answer should be in \\boxed{}.\\nQuestion: {question}\"\n```\n\n\n### AIME25\n\n| Reasoning Mode | pass@1 |\n|--------------|------------|\n| Reasoning Off | 13.3% | \n| Reasoning On | 46.7% |\n\nUser Prompt Template: \n\n```\n\"Below is a math question. I want you to reason through the steps and then give a final answer. Your final answer should be in \\boxed{}.\\nQuestion: {question}\"\n```\n\n\n### GPQA-D\n\n| Reasoning Mode | pass@1 |\n|--------------|------------|\n| Reasoning Off | 31.8% | \n| Reasoning On | 55.8% |\n\nUser Prompt Template: \n\n\n```\n\"What is the correct answer to this question: {question}\\nChoices:\\nA. {option_A}\\nB. {option_B}\\nC. {option_C}\\nD. {option_D}\\nLet's think step by step, and put the final answer (should be a single letter A, B, C, or D) into a \\boxed{}\"\n```\n\n\n### IFEval\n\n| Reasoning Mode | Strict:Prompt | Strict:Instruction |\n|--------------|------------|------------|\n| Reasoning Off | 73.6% | 80.8% |\n| Reasoning On | 75.4% | 82.6% |\n\n### BFCL v2 Live\n\n| Reasoning Mode | Score |\n|--------------|------------|\n| Reasoning Off | 57.1% | \n| Reasoning On | 64.2% | \n\nUser Prompt Template:\n\n\n```\n<AVAILABLE_TOOLS>{functions}</AVAILABLE_TOOLS>\n\n{user_prompt}\n```\n\n\n### MBPP 0-shot\n\n| Reasoning Mode | pass@1 |\n|--------------|------------|\n| Reasoning Off | 66.4% | \n| Reasoning On | 86.0% |\n\nUser Prompt Template:\n\n\n````\nYou are an exceptionally intelligent coding assistant that consistently delivers accurate and reliable responses to user instructions.\n\n@@ Instruction\nHere is the given problem and test examples:\n{prompt}\nPlease use the python programming language to solve this problem.\nPlease make sure that your code includes the functions from the test samples and that the input and output formats of these functions match the test samples.\nPlease return all completed codes in one code block.\nThis code block should be in the following format:\n```python\n# Your codes here\n```\n````\n\n\n## Ethical Considerations:\n\nNVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications.  When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. \n\nFor more detailed information on ethical considerations for this model, please see the Model Card++ [Explainability](explainability.md), [Bias](bias.md), [Safety & Security](safety.md), and [Privacy](privacy.md) Subcards.\n\nPlease report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).",
    "displayName": "Llama-3.1-Nemotron-Nano-4B-v1.1",
    "explainability": "|Field:|Response:|\n|:---|:---|\n|Intended Application(s) & Domain(s):|Text generation, reasoning, summarization, and question answering.|\n|Model Type: |Text-to-text transformer |\n|Intended Users:|This model is intended for developers, researchers, and customers building/utilizing LLMs, while balancing accuracy and efficiency.|\n|Output:|Text String(s)|\n|Describe how the model works:|Generates text by predicting the next word or token based on the context provided in the input sequence using multiple self-attention layers.|\n|Technical Limitations:|The model was trained on data that contains toxic language, unsafe content, and societal biases originally crawled from the internet. Therefore, the model may amplify those biases and return toxic responses especially when prompted with toxic prompts. The model may generate answers that may be inaccurate, omit key information, or include irrelevant or redundant text producing socially unacceptable or undesirable text, even if the prompt itself does not include anything explicitly offensive.<br><br>The model demonstrates weakness to alignment-breaking attacks. Users are advised to deploy language model guardrails alongside this model to prevent potentially harmful outputs.<br>|\n|Verified to have met prescribed quality standards?|Yes|\n|Performance Metrics:|Accuracy, Throughput, and user-side throughput|\n|Potential Known Risks:|The model was optimized explicitly for instruction following and as such is more susceptible to prompt injection and jailbreaking in various forms as a result of its instruction tuning. This means that the model should be paired with additional rails or system filtering to limit exposure to instructions from malicious sources  -- either directly or indirectly by retrieval (e.g. via visiting a website)  -- as they may yield outputs that can lead to harmful, system-level outcomes up to and including remote code execution in agentic systems when effective security controls including guardrails are not in place.<br><br>The model was trained on data that contains toxic language and societal biases originally crawled from the internet. Therefore, the model may amplify those biases and return toxic responses especially when prompted with toxic prompts. The model may generate answers that may be inaccurate, omit key information, or include irrelevant or redundant text producing socially unacceptable or undesirable text, even if the prompt itself does not include anything explicitly offensive.|\n|End User License Agreement:|The NIM container is governed by the [NVIDIA Software License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-license-agreement) and [Product-Specific Terms for NVIDIA AI Products](https://www.nvidia.com/en-us/agreements/enterprise-software/product-specific-terms-for-ai-products). Your use of this model is governed by the [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/). Additional Information: [Llama 3.1 Community License Agreement](https://www.llama.com/llama3_1/license). Built with Llama.",
    "framework": "Other",
    "hasPlayground": false,
    "hasSignedVersion": true,
    "isPlaygroundEnabled": false,
    "isPublic": false,
    "isReadOnly": true,
    "labels": [
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        "nvidia_nim:model:nimmcro_nvidia_nim",
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    "latestVersionIdStr": "l40sx1-throughput-lora-fp8-jt4xfye5rg",
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    "logo": "https://assets.ngc.nvidia.com/products/api-catalog/images/nemotron-mini-4b-instruct.jpg",
    "modelFormat": "N/A",
    "name": "llama3.1-nemotron-nano-4b-v1.1",
    "orgName": "nim",
    "precision": "N/A",
    "privacy": "|Field:|Response:|\n|:---|:---|\n|Generatable or Reverse engineerable personal data?|None|\n|Was consent obtained for any personal data used?|None Known|\n|Personal data used to create this model?|None Known|\n|How often is dataset reviewed?|Before Release|\n|Is there provenance for all datasets used in training?|Yes|\n|Does data labeling (annotation, metadata) comply with privacy laws?|Yes|\n|Applicable NVIDIA Privacy Policy|https://www.nvidia.com/en-us/about-nvidia/privacy-policy/|",
    "productNames": [
        "nim-dev",
        "nv-ai-enterprise"
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    "publicDatasetUsed": {},
    "publisher": "NVIDIA",
    "safetyAndSecurity": "|Field:|Response:|\n|:---|:---|\n|Model Application(s):|Chat, Instruction Following, Chatbot Development, Code Generation, Reasoning|\n|Describe life critical application (if present):|None Known (please see referenced Known Risks in the Explainability subcard).|\n|Use Case Restrictions:|The NIM container is governed by the [NVIDIA Software License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-license-agreement) and [Product-Specific Terms for NVIDIA AI Products](https://www.nvidia.com/en-us/agreements/enterprise-software/product-specific-terms-for-ai-products). Your use of this model is governed by the [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/). Additional Information: [Llama 3.1 Community License Agreement](https://www.llama.com/llama3_1/license). Built with Llama.\n|Model and Dataset Restrictions:|The Principle of least privilege (PoLP) is applied limiting access for dataset generation.  Restrictions enforce dataset access during training, and dataset license constraints adhered to. Model checkpoints are made available on Hugging Face and NGC, and may become available on cloud providers' model catalog.|",
    "shortDescription": "Llama-3.1-Nemotron-Nano-4B-v1.1 is a large language model (LLM) reasoning model that is post trained for reasoning, human chat preferences, and tasks, such as RAG and tool calling.",
    "teamName": "nvidia",
    "updatedDate": "2025-06-18T18:05:57.835Z"
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    "description": "# Model Overview\n\n## Description:\n\n[//]: # ([Provide additional details about the algorithm/model; include supporting image/video and/or reference blog/article, if available.] [This model is ready for commercial/non-commercial use.] OR [This model is for research and development only.] OR [This model is for demonstration purposes and not for production usage.] <br>)\n\nLlama-3.1-Nemotron-Nano-8B-v1 is a model for generating responses for roleplaying, retrieval augmented generation, and function calling.  It is a small language model (SLM) optimized through distillation, pruning and quantization for speed and on-device deployment. VRAM usage has been minimized to approximately 2 GB, providing significantly faster Time-to-First-Token compared to LLMs.\n\nThis model is ready for commercial use.\n\n### License/Terms of Use: \n[NVIDIA AI Foundation Models Community License Agreement](https://developer.nvidia.com/downloads/nv-ai-foundation-models-license)\n\n\n## References\n\nPlease refer to the [User Guide]() to use the model and use a suggested guideline for prompts.\n\n## Model Architecture:\n**Architecture Type:** Transformer <br>\n**Network Architecture:** Llama-3.1 <br>\n\n## Limitations\nThe model was trained on data that contains toxic language and societal biases originally crawled from the internet. Therefore, the model may amplify those biases and return toxic responses especially when prompted with toxic prompts. The model may generate answers that may be inaccurate, omit key information, or include irrelevant or redundant text producing socially unacceptable or undesirable text, even if the prompt itself does not include anything explicitly offensive. This issue could be exacerbated without the use of the recommended prompt template. \n\n## Input: \n**Input Type(s):**  Text (Prompt) <br>\n**Input Format(s):** String <br>\n**Input Parameters:** One Dimensional (1D) <br>\n**Other Properties Related to Input:** The model has a maximum of 4096 input tokens. <br>\n \n## Output: \n**Output Type(s):** Text (Response) <br>\n**Output Format:** String <br>\n**Output Parameters:** 1D <br>\n**Other Properties Related to Output:**  The model has a maximum of 4096 output tokens. Maximum output for both versions can be set apart from input.<br>\n\n\n## Prompt Format:\n\nWe recommend using the following prompt template, which was used to fine-tune the model. The model may not perform optimally without it.\n\n**Single Turn**\n\n```\n<extra_id_0>System\n{system prompt}\n\n<extra_id_1>User\n{prompt}\n<extra_id_1>Assistant\\n\n```\n\n**Tool use**\n\n```\n<extra_id_0>System\n{system prompt}\n\n<tool> ... </tool>\n<context> ... </context>\n\n<extra_id_1>User\n{prompt}\n<extra_id_1>Assistant\n<toolcall> ... </toolcall>\n<extra_id_1>Tool\n{tool response}\n<extra_id_1>Assistant\\n\n```\n\n\n## Software Integration: (On-Device)\n**Runtime(s):** AI Inference Manager (NVAIM) Version 1.0.0 <br>\n**Toolkit:**  NVAIM <br>\nSee [this document]() for details on how to integrate the model into NVAIM.\n\n**Supported Hardware Platform(s):** GPU supporting DirectX 11/12 and Vulkan 1.2 or higher <br>\n\n**[Preferred/Supported] Operating System(s):** <br>\n* Windows <br>\n\n## Software Integration: (Cloud)\n**Toolkit:** NVIDIA NIM <br>\nSee [this document]() for details on how to integrate the model into NVAIM.\n\n**[Preferred/Supported] Operating System(s):** <br>\n* Linux <br>\n\n### Model Version(s)\nLlama-3.1-Nemotron-Nano-8B-v1\n\n# Training & Evaluation: \n\n## Training Dataset:\n\n** Data Collection Method by dataset <br>\n* Hybrid: Automated, Human <br>\n\n** Labeling Method by dataset <br>\n* Hybrid: Automated, Human <br>\n\n**Properties:** <br>\n\nTrained on approximately 10000 Game/Non-Playable Character (NPC) dialog turns from domain chat data.\n\n## Evaluation Dataset:\n\n** Data Collection Method by dataset <br>\n* Hybrid: Automated, Human <br>\n\n** Labeling Method by dataset <br>\n* Human <br>\n\n**Properties:** <br>\n\nEvaluated on approximately Game/NPC 10000 dialog turns from domain chat data.  <br>\n\n## Inference:\n**Engine:** TRT-LLM <br>\n**Test Hardware:** <br>\n* A100 <br>\n* A10g <br>\n* H100  <br>\n* L40s  <br>\n\n**Supported Hardware Platform(s):** L40s, A10g, A100, H100<br>\n\n## Ethical Considerations:\n\nNVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications.  When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.  For more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards.  Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).\n\n**You are responsible for ensuring that your use of NVIDIA AI Foundation Models complies with all applicable laws.**",
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key: PRECISION value: BF16 - key: COUNT value: 1 - key: NIM VERSION value: 1.8.4 - key: DOWNLOAD SIZE value: 15GB labels: - Llama - Meta - Text Generation - Large Language Model - NVIDIA Validated - Nemo config: architectures: - Other modelType: llama license: NVIDIA AI Foundation Models Community License - name: Llama 3.1 Instruct displayName: Llama 3.1 Instruct modelHubID: llama-3.1-instruct category: Language type: NGC description: Multilingual instruction-following model optimized for chat, reasoning, and text generation. Strong performance across diverse language benchmarks. requireLicense: true licenseAgreements: - label: Use Policy url: https://llama.meta.com/llama3/use-policy/ - label: License Agreement url: https://llama.meta.com/llama3/license/ modelVariants: - variantId: Llama 3.1 8B Instruct modelCard: {
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    "description": "# **Llama-3.1-8B-Instruct Overview**\n\n## **Description:**\n\n**Llama-3.1-8B-Instruct** is an 8 billion parameter, instruction-tuned large language model created by Meta. This model is part of the Llama 3.1 family of open-access models and is specifically optimized for dialogue and conversational use cases, making it highly capable of following user instructions to perform a wide variety of natural language processing tasks.\n\nThis model is ready for commercial/non-commercial use.\n\nThis version introduces support for GB200 NVL72, GH200 NVL2, B200 and NVFP4. CUDA updated to version 12.9. For detailed information, refer to Release [Notes for NVIDIA NIM for LLMs LLM 1.12](https://docs.nvidia.com/nim/large-language-models/latest/release-notes.html). \n\n## **Third-Party Community Consideration**\n\nThis model is not owned or developed by NVIDIA. This model has been developed and built to a third-party's requirements for this application and use case; see link to Non-NVIDIA \\[meta-llama/Llama-3.1-8B-Instruct\\]  \n([https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct)). \n\n## **License/Terms of Use:**\n\n**GOVERNING TERMS:** The NIM container is governed by the [NVIDIA Software License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-license-agreement/) and the [Product-Specific Terms for NVIDIA AI Products](https://www.nvidia.com/en-us/agreements/enterprise-software/product-specific-terms-for-ai-products/); and the use of this model is governed by the [NVIDIA Community Model License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-community-models-license/). \n\n**ADDITIONAL INFORMATION**: [Llama 3.1 Community License Agreement](https://www.llama.com/llama3_1/license/). Built with Llama.\n\nYou are responsible for ensuring that your use of NVIDIA provided models complies with all applicable laws.\n\n## **Deployment Geography:**\n\nGlobal \n\n## **Use Case:**\n\nThis model is primarily used by developers, researchers, and businesses to build and experiment with a wide range of generative AI applications. Its combination of strong performance, efficiency, and an open-access license makes it highly versatile.\n\n* Developers and Businesses would use this model to create production-ready applications such as:  \n  * AI Chatbots and customer service agents.  \n  * Content creation tools for writing emails, marketing copy, and articles.  \n  * Summarization and question-answering systems for internal documents.  \n  * Code generation assistants to help programmers write and debug code.  \n* Researchers would use it to study large language model behavior, explore AI safety and alignment, and benchmark new training or fine-tuning techniques on a capable, open model.  \n* AI Hobbyists would use the model for personal projects, running it on consumer-grade hardware to experiment with creating their own AI assistants or exploring the frontiers of generative AI.\n\n## **Release Date:**\n\nBuild.Nvida.com 07/23/2024 via  \n(https://build.nvidia.com/meta/llama-3_1-8b-instruct)\n\nHuggingface 07/23/2024 via   \n(https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct)\n\n## **Reference(s):** \n\n[https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) \n\n## **Model Architecture:** \n\nArchitecture Type: Transformer  \nNetwork Architecture: Llama-3.1-8B\n\nThis model was developed based on Meta Llama-3.1-8B  \n[https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) \n\nNumber of model parameters: 8.03*10^9\n## **Input:**\n\nInput Type(s): Text \n\nInput Format(s): String \n\nInput Parameters: One-Dimensional (1D)\n\nOther Properties Related to Input: The primary input limit is the model's maximum context length, which is 128,000 tokens. All input text must be pre-processed using the model's specific tokenizer to convert the string into a sequence of token IDs. For conversational use, inputs should be formatted using the model's designated chat template.\n\n## **Output:**\n\nOutput Type(s): Text \n\nOutput Format(s): String\n\nOutput Parameters: One-Dimensional (1D)\n\nOther Properties Related to Output: The output consists of a variable-length sequence of tokens from the model's vocabulary. The total length of the input and output cannot exceed the 128,000-token context window. Post-processing is required to detokenize the raw token ID sequence into a human-readable string.\n\n## **Software Integration:**\n\nRuntime Engine: vLLM, TensorRT\n\nSupported Hardware Microarchitecture Compatibility:\n\nNVIDIA Ampere  \nNVIDIA Blackwell  \nNVIDIA Hopper  \nNVIDIA Lovelace \n\nPreferred Operating System(s):\n\nLinux   \nWindows  \nmacOS\n\nThe integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.\n\n## **Model Version(s):**\n\n*Llama-3.1-8B-Instruct-1.10.1 \n*Llama-3.1-8B-Instruct-1.12.0\n*Llama-3.1-8B-Instruct-1.13.1\n\n## **Usage**\n\n### **Use with transformers**\n\nStarting with transformers \\>= 4.43.0 onward, you can run conversational inference using the Transformers pipeline abstraction or by leveraging the Auto classes with the generate() function.\n\nMake sure to update your transformers installation via pip install \\--upgrade transformers.\n\n```\nimport transformers\nimport torch\n\nmodel_id = \"meta-llama/Meta-Llama-3.1-8B-Instruct\"\n\npipeline = transformers.pipeline(\n    \"text-generation\",\n    model=model_id,\n    model_kwargs={\"torch_dtype\": torch.bfloat16},\n    device_map=\"auto\",\n)\n\nmessages = [\n    {\"role\": \"system\", \"content\": \"You are a pirate chatbot who always responds in pirate speak!\"},\n    {\"role\": \"user\", \"content\": \"Who are you?\"},\n]\n\noutputs = pipeline(\n    messages,\n    max_new_tokens=256,\n)\nprint(outputs[0][\"generated_text\"][-1])\n```\n\nNote: You can also find detailed recipes on how to use the model locally, with torch.compile(), assisted generations, quantised and more at [huggingface-llama-recipes](https://github.com/huggingface/huggingface-llama-recipes)\n\n### **Tool use with transformers**\n\nLLaMA-3.1 supports multiple tool use formats. You can see a full guide to prompt formatting [here](https://llama.meta.com/docs/model-cards-and-prompt-formats/llama3_1/).\n\nTool use is also supported through [chat templates](https://huggingface.co/docs/transformers/main/chat_templating#advanced-tool-use--function-calling) in Transformers. Here is a quick example showing a single simple tool:\n\n```\n# First, define a tool\ndef get_current_temperature(location: str) -> float:\n    \"\"\"\n    Get the current temperature at a location.\n    \n    Args:\n        location: The location to get the temperature for, in the format \"City, Country\"\n    Returns:\n        The current temperature at the specified location in the specified units, as a float.\n    \"\"\"\n    return 22.  # A real function should probably actually get the temperature!\n\n# Next, create a chat and apply the chat template\nmessages = [\n  {\"role\": \"system\", \"content\": \"You are a bot that responds to weather queries.\"},\n  {\"role\": \"user\", \"content\": \"Hey, what's the temperature in Paris right now?\"}\n]\n\ninputs = tokenizer.apply_chat_template(messages, tools=[get_current_temperature], add_generation_prompt=True)\n```\n\nYou can then generate text from this input as normal. If the model generates a tool call, you should add it to the chat like so:\n\n```\ntool_call = {\"name\": \"get_current_temperature\", \"arguments\": {\"location\": \"Paris, France\"}}\nmessages.append({\"role\": \"assistant\", \"tool_calls\": [{\"type\": \"function\", \"function\": tool_call}]})\n```\n\nand then call the tool and append the result, with the tool role, like so:\n\n```\nmessages.append({\"role\": \"tool\", \"name\": \"get_current_temperature\", \"content\": \"22.0\"})\n```\n\nAfter that, you can generate() again to let the model use the tool result in the chat. Note that this was a very brief introduction to tool calling \\- for more information, see the [LLaMA prompt format docs](https://llama.meta.com/docs/model-cards-and-prompt-formats/llama3_1/) and the Transformers [tool use documentation](https://huggingface.co/docs/transformers/main/chat_templating#advanced-tool-use--function-calling).\n\n## **Training, Testing, and Evaluation Datasets:**\n\n### **Training Dataset**\n\n**Data Modality:** Text \n\n**Link:** Undisclosed\n\n**Data Collection Method:** Hybrid: Human, Synthetic, Automated\n\n**Labeling Method:** Hybrid: Human, Automated\n\n**Properties:** \n\nThe model was pre-trained on a dataset of over 15 trillion tokens. This dataset is a high-quality mix of publicly available data, heavily filtered for safety and quality. The instruction fine-tuning dataset is smaller and consists of high-quality prompts, responses, and preference rankings curated by humans.\n\n### **Testing Dataset**\n\n**Link:** Undisclosed\n\n**Data Collection Method:** Hybrid: Human, Automated\n\n**Labeling Method:** Human\n\n**Properties:** \n\nThe testing datasets comprise thousands of individual problems designed to measure model capabilities in specific areas:\n\n* **General Knowledge & Reasoning:** MMLU, DROP, AGIEval, BIG-Bench Hard  \n* **Mathematics:** GSM8K, MATH  \n* **Coding:** HumanEval\n\n### **Evaluation Dataset**\n\n**Link:** Undisclosed\n\n**Data Collection Method:** Hybrid: Human, Automated\n\n**Labeling Method:** Human\n\n**Properties:** \n\nThe evaluation datasets consist of thousands of questions and problems designed to test the model's capabilities in areas like general knowledge, reasoning, mathematics, and coding.\n\n**Base pretrained models**\n\n| Category | Benchmark | \\# Shots | Metric | Llama 3 8B | Llama 3.1 8B | Llama 3 70B | Llama 3.1 70B | Llama 3.1 405B |\n| :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- |\n| General | MMLU | 5 | macro\\_avg/acc\\_char | 66.7 | 66.7 | 79.5 | 79.3 | 85.2 |\n|  | MMLU-Pro (CoT) | 5 | macro\\_avg/acc\\_char | 36.2 | 37.1 | 55.0 | 53.8 | 61.6 |\n|  | AGIEval English | 3-5 | average/acc\\_char | 47.1 | 47.8 | 63.0 | 64.6 | 71.6 |\n|  | CommonSenseQA | 7 | acc\\_char | 72.6 | 75.0 | 83.8 | 84.1 | 85.8 |\n|  | Winogrande | 5 | acc\\_char | \\- | 60.5 | \\- | 83.3 | 86.7 |\n|  | BIG-Bench Hard (CoT) | 3 | average/em | 61.1 | 64.2 | 81.3 | 81.6 | 85.9 |\n|  | ARC-Challenge | 25 | acc\\_char | 79.4 | 79.7 | 93.1 | 92.9 | 96.1 |\n| Knowledge reasoning | TriviaQA-Wiki | 5 | em | 78.5 | 77.6 | 89.7 | 89.8 | 91.8 |\n| Reading comprehension | SQuAD | 1 | em | 76.4 | 77.0 | 85.6 | 81.8 | 89.3 |\n|  | QuAC (F1) | 1 | f1 | 44.4 | 44.9 | 51.1 | 51.1 | 53.6 |\n|  | BoolQ | 0 | acc\\_char | 75.7 | 75.0 | 79.0 | 79.4 | 80.0 |\n|  | DROP (F1) | 3 | f1 | 58.4 | 59.5 | 79.7 | 79.6 | 84.8 |\n\n**Instruction tuned models**\n\n| Category | Benchmark | \\# Shots | Metric | Llama 3 8B Instruct | Llama 3.1 8B Instruct | Llama 3 70B Instruct | Llama 3.1 70B Instruct | Llama 3.1 405B Instruct |\n| :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- |\n| General | MMLU | 5 | macro\\_avg/acc | 68.5 | 69.4 | 82.0 | 83.6 | 87.3 |\n|  | MMLU (CoT) | 0 | macro\\_avg/acc | 65.3 | 73.0 | 80.9 | 86.0 | 88.6 |\n|  | MMLU-Pro (CoT) | 5 | micro\\_avg/acc\\_char | 45.5 | 48.3 | 63.4 | 66.4 | 73.3 |\n|  | IFEval |  |  | 76.8 | 80.4 | 82.9 | 87.5 | 88.6 |\n| Reasoning | ARC-C | 0 | acc | 82.4 | 83.4 | 94.4 | 94.8 | 96.9 |\n|  | GPQA | 0 | em | 34.6 | 30.4 | 39.5 | 46.7 | 50.7 |\n| Code | HumanEval | 0 | pass@1 | 60.4 | 72.6 | 81.7 | 80.5 | 89.0 |\n|  | MBPP \\++ base version | 0 | pass@1 | 70.6 | 72.8 | 82.5 | 86.0 | 88.6 |\n|  | Multipl-E HumanEval | 0 | pass@1 | \\- | 50.8 | \\- | 65.5 | 75.2 |\n|  | Multipl-E MBPP | 0 | pass@1 | \\- | 52.4 | \\- | 62.0 | 65.7 |\n| Math | GSM-8K (CoT) | 8 | em\\_maj1@1 | 80.6 | 84.5 | 93.0 | 95.1 | 96.8 |\n|  | MATH (CoT) | 0 | final\\_em | 29.1 | 51.9 | 51.0 | 68.0 | 73.8 |\n| Tool Use | API-Bank | 0 | acc | 48.3 | 82.6 | 85.1 | 90.0 | 92.0 |\n|  | BFCL | 0 | acc | 60.3 | 76.1 | 83.0 | 84.8 | 88.5 |\n|  | Gorilla Benchmark API Bench | 0 | acc | 1.7 | 8.2 | 14.7 | 29.7 | 35.3 |\n|  | Nexus (0-shot) | 0 | macro\\_avg/acc | 18.1 | 38.5 | 47.8 | 56.7 | 58.7 |\n| Multilingual | Multilingual MGSM (CoT) | 0 | em | \\- | 68.9 | \\- | 86.9 | 91.6 |\n\n**Multilingual benchmarks**\n\n| Category | Benchmark | Language | Llama 3.1 8B | Llama 3.1 70B | Llama 3.1 405B |\n| :---- | :---- | :---- | :---- | :---- | :---- |\n| General | MMLU (5-shot, macro\\_avg/acc) | Portuguese | 62.12 | 80.13 | 84.95 |\n|  |  | Spanish | 62.45 | 80.05 | 85.08 |\n|  |  | Italian | 61.63 | 80.4 | 85.04 |\n|  |  | German | 60.59 | 79.27 | 84.36 |\n|  |  | French | 62.34 | 79.82 | 84.66 |\n|  |  | Hindi | 50.88 | 74.52 | 80.31 |\n|  |  | Thai | 50.32 | 72.95 | 78.21 |\n\n## **Technical Limitations** \n\n Testing conducted to date has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, the model's potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying this model in any applications, developers should perform safety testing and tuning tailored to their specific applications. Please refer to available resources including the [Responsible Use Guide](https://llama.meta.com/responsible-use-guide), [Trust and Safety](https://llama.meta.com/trust-and-safety/) solutions, and other [resources](https://llama.meta.com/docs/get-started/) to learn more about responsible development. \n\n## **Inference:**\n\n**Acceleration Engine:** vLLM, TensorRT \n\n**Test Hardware:** \n\n* B200 SXM  \n* H200 SXM  \n* H100 SXM  \n* A100 SXM 80GB  \n* A100 SXM 40GB  \n* L40S PCIe  \n* A10G  \n* H100 NVL  \n* H200 NVL  \n* GH200 96GB\n* GB200 NVL72\n* GH200 NVL2\n* RTX 5090  \n* RTX 4090  \n* RTX 6000 Ada\n\n## **Get Help**\n\n### Enterprise Support\nGet access to knowledge base articles and support cases or [submit a ticket](https://www.nvidia.com/en-us/data-center/products/ai-enterprise-suite/support/).\n\n## **Ethical Considerations:**\n\nNVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).\n\n**You are responsible for ensuring that your use of NVIDIA provided models complies with all applicable laws.**",
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profileId: nim/meta/llama-3.1-8b-instruct:b200x2-latency-nvfp4-urjebtmqkg framework: TensorRT-LLM displayName: Llama 3.1 8B Instruct B200x2 NVFP4 Latency ngcMetadata: 5eaaf502f6dab9ce29e7d034182bb56eeeb3e349633f4561018f27b3069189b3: model: meta/llama-3.1-8b-instruct release: 1.13.1 tags: feat_lora: 'false' gpu: B200 gpu_device: 2901:10de llm_engine: tensorrt_llm nim_workspace_hash_v1: 2693061ea8698de078f95517349178ce8894a51a97db468183032cba22ab04ae number_of_gpus: '2' pp: '1' precision: nvfp4 profile: latency tp: '2' modelFormat: trt-llm spec: - key: PROFILE value: LATENCY - key: PRECISION value: NVFP4 - key: GPU value: B200 - key: COUNT value: 2 - key: GPU DEVICE value: 2901:10DE - key: NIM VERSION value: 1.13.1 - key: DOWNLOAD SIZE value: 6GB - key: LLM ENGINE value: TENSORRT_LLM - profileId: nim/meta/llama-3.1-8b-instruct:8c22764a7e3675c50d4c7c9a4edb474456022b16__6 framework: TensorRT-LLM displayName: Llama 3.1 8B Instruct H100_NVLx1 FP8 Throughput ngcMetadata: 785d7d60df3f153a36413f29a16ac14bc5cfba73004bc7feee2bca9d78b10e6f: model: meta/llama-3.1-8b-instruct release: 1.13.1 tags: feat_lora: 'false' gpu: H100_NVL gpu_device: 2321:10de llm_engine: tensorrt_llm nim_workspace_hash_v1: a0de60706fabf3ee071fef41f0c14225a3d88799bb9728af810e57a7499f038f number_of_gpus: '2' pp: '1' precision: fp8 profile: throughput tp: '1' modelFormat: trt-llm spec: - 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profileId: nim/meta/llama-3.1-8b-instruct:gb200x1-throughput-nvfp4-ihgvv-o6wg framework: TensorRT-LLM displayName: Llama 3.1 8B Instruct GB200x1 NVFP4 Throughput ngcMetadata: adbc8a19059852df0c2ac75173b80f123b0901926e524a2c050ce60aa3ae5ca1: model: meta/llama-3.1-8b-instruct release: 1.13.1 tags: feat_lora: 'false' gpu: GB200 gpu_device: 2941:10de llm_engine: tensorrt_llm nim_workspace_hash_v1: a381f8cc9d090bc49cb320a47cdefe01b0555dc9409312516194cb19437436d0 number_of_gpus: '2' pp: '1' precision: nvfp4 profile: throughput tp: '1' modelFormat: trt-llm spec: - key: PROFILE value: THROUGHPUT - key: PRECISION value: NVFP4 - key: GPU value: GB200 - key: COUNT value: 1 - key: GPU DEVICE value: 2941:10DE - key: NIM VERSION value: 1.13.1 - key: DOWNLOAD SIZE value: 6GB - key: LLM ENGINE value: TENSORRT_LLM - profileId: nim/meta/llama-3.1-8b-instruct:8c22764a7e3675c50d4c7c9a4edb474456022b16__16 framework: TensorRT-LLM displayName: Llama 3.1 8B Instruct H100_NVLx2 BF16 Latency ngcMetadata: afc6d2a8f5c1affe8524a39c78d6f083cd56ac678f9cc9f89df33b0e0e530ec5: model: meta/llama-3.1-8b-instruct release: 1.13.1 tags: feat_lora: 'false' gpu: H100_NVL gpu_device: 2321:10de llm_engine: tensorrt_llm nim_workspace_hash_v1: d0040f5636dd8d4baa9c36337ccb4157b58e47ba7118b2d54b36e2ad96061ed0 number_of_gpus: '2' pp: '1' precision: bf16 profile: latency tp: '2' modelFormat: trt-llm spec: - 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    "description": "# **Llama-3.1-70B-Instruct Overview**\n\n## **Description:**\n\n**Llama-3.1-70B-Instruct** is a multilingual large language model from the Meta Llama 3.1 collection of pretrained and instruction-tuned generative models. This model is optimized for multilingual dialogue use cases and outperforms many available open-source and closed-source chat models on common industry benchmarks. Llama 3.1 is an auto-regressive language model that uses an optimized transformer architecture; the tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.\n\nThis model is ready for commercial/non-commercial use.\n\nThis version introduces support for GB200 NVL72, GH200 NVL2, B200 and NVFP4. CUDA updated to version 12.9. For detailed information, refer to Release [Notes for NVIDIA NIM for LLMs LLM 1.12](https://docs.nvidia.com/nim/large-language-models/latest/release-notes.html). \n\n## **Third-Party Community Consideration**\n\nThis model is not owned or developed by NVIDIA. This model has been developed and built to a third-party's requirements for this application and use case; see link to Non-NVIDIA\\[meta-llama/Llama-3.1-70B-Instruct\\]  \n([https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct)). \n\n## **License/Terms of Use:**\n\n**GOVERNING TERMS:** The NIM container is governed by the [NVIDIA Software License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-license-agreement/) and the [Product-Specific Terms for NVIDIA AI Products](https://www.nvidia.com/en-us/agreements/enterprise-software/product-specific-terms-for-ai-products/); and the use of the model is governed by the [NVIDIA Community Model License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-community-models-license/.).\n\n**ADDITIONAL INFORMATION:** [Llama 3.1 Community License Agreement](https://www.llama.com/llama3_3/license/). Built with Llama.\n\n## Get Help\n\n### Enterprise Support\n\nGet access to knowledge base articles and support cases or [submit a ticket](https://www.nvidia.com/en-us/data-center/products/ai-enterprise-suite/support/).\n\n**You are responsible for ensuring that your use of NVIDIA provided models complies with all applicable laws.**\n\n## **Deployment Geography:**\n\nGlobal \n\n## **Use Case:**\n\nDevelopers, AI researchers, and businesses would be expected to use this system to build and power a wide range of applications that require advanced reasoning, instruction-following, and multilingual dialogue capabilities. Specific applications include creating sophisticated chatbots and virtual assistants, developing powerful content creation and summarization tools, building complex question-answering systems, and powering multilingual customer support platforms.\n\n## **Release Date:**\n\nBuild.Nvidia.com 07/23/2024 via  \n[llama-3.1-70b-instruct Model by Meta | NVIDIA NIM](https://build.nvidia.com/meta/llama-3_1-70b-instruct)\n\nGithub 07/23/2024 via   \n[https://github.com/meta-llama/llama-models/blob/main/models/llama3\\_1/LICENSE](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE)\n\nHuggingface 07/23/2024 via   \n[https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct)\n\n**Reference(s):** \n\n[https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct)\n\n## **Model Architecture:** \n\nArchitecture Type: Transformer  \nNetwork Architecture: Llama-3.1-70B\n\nThis model was developed based on Meta-Llama-3.1-70B  \n[https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct)\n\nNumber of model parameters: 7.06*10^10\n\n## **Input:**\n\nInput Type(s): Text \n\nInput Format(s): String \n\nInput Parameters: One-Dimensional (1D)\n\nOther Properties Related to Input: The model accepts a string of text which is converted into tokens using the model's specific tokenizer. The total length of the input prompt and the generated output cannot exceed the model's context window of 128,000 tokens.\n\n## **Output:**\n\nOutput Type(s): Text \n\nOutput Format(s): String\n\nOutput Parameters: One-Dimensional (1D)\n\nOther Properties Related to Output: The model generates a string of text, produced token by token. The maximum length of the output is limited by the model's 128,000-token context window, less the length of the input prompt. Post-processing is required to decode the model's token-based output into a readable string.\n\nOur AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA's hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.\n\n## **Software Integration:**\n\nRuntime Engine: vLLM, TensorRT\n\nSupported Hardware Microarchitecture Compatibility:\n\nNVIDIA Ampere  \nNVIDIA Blackwell  \nNVIDIA Hopper  \nNVIDIA Lovelace \n\nPreferred Operating System(s):\n\nLinux   \nWindows\n\nThe integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.\n\n## **Model Version(s):**\n\nLlama-3.1-70B-Instruct\n\n## **Usage**\n\n**Use with transformers**\n\nStarting with transformers \\>= 4.45.0 onward, you can run conversational inference using the Transformers pipeline abstraction or by leveraging the Auto classes with the generate() function.\n\nMake sure to update your transformers installation via pip install \\--upgrade transformers.\n\nSee the snippet below for usage with Transformers:\n\n```\nimport transformers\nimport torch\n\nmodel_id = \"meta-llama/Meta-Llama-3.1-70B-Instruct\"\n\npipeline = transformers.pipeline(\n    \"text-generation\",\n    model=model_id,\n    model_kwargs={\"torch_dtype\": torch.bfloat16},\n    device_map=\"auto\",\n)\n\nmessages = [\n    {\"role\": \"system\", \"content\": \"You are a pirate chatbot who always responds in pirate speak!\"},\n    {\"role\": \"user\", \"content\": \"Who are you?\"},\n]\n\noutputs = pipeline(\n    messages,\n    max_new_tokens=256,\n)\nprint(outputs[0][\"generated_text\"][-1])\n```\n\n**Tool use with transformers**\n\nLLaMA-3.3 supports multiple tool use formats. You can see a full guide to prompt formatting [here](https://llama.meta.com/docs/model-cards-and-prompt-formats/llama3_1/).\n\nTool use is also supported through [chat templates](https://huggingface.co/docs/transformers/main/chat_templating#advanced-tool-use--function-calling) in Transformers. Here is a quick example showing a single simple tool:\n\n```\n# First, define a tool\ndef get_current_temperature(location: str) -> float:\n    \"\"\"\n    Get the current temperature at a location.\n    \n    Args:\n        location: The location to get the temperature for, in the format \"City, Country\"\n    Returns:\n        The current temperature at the specified location in the specified units, as a float.\n    \"\"\"\n    return 22.  # A real function should probably actually get the temperature!\n\n# Next, create a chat and apply the chat template\nmessages = [\n  {\"role\": \"system\", \"content\": \"You are a bot that responds to weather queries.\"},\n  {\"role\": \"user\", \"content\": \"Hey, what's the temperature in Paris right now?\"}\n]\n\ninputs = tokenizer.apply_chat_template(messages, tools=[get_current_temperature], add_generation_prompt=True)\n```\n\nYou can then generate text from this input as normal. If the model generates a tool call, you should add it to the chat like so:\n\n```\ntool_call = {\"name\": \"get_current_temperature\", \"arguments\": {\"location\": \"Paris, France\"}}\nmessages.append({\"role\": \"assistant\", \"tool_calls\": [{\"type\": \"function\", \"function\": tool_call}]})\n```\n\nand then call the tool and append the result, with the tool role, like so:\n\n```\nmessages.append({\"role\": \"tool\", \"name\": \"get_current_temperature\", \"content\": \"22.0\"})\n```\n\nAfter that, you can generate() again to let the model use the tool result in the chat. Note that this was a very brief introduction to tool calling \\- for more information, see the [LLaMA prompt format docs](https://llama.meta.com/docs/model-cards-and-prompt-formats/llama3_1/) and the Transformers [tool use documentation](https://huggingface.co/docs/transformers/main/chat_templating#advanced-tool-use--function-calling).\n\n**Use with bitsandbytes**\n\nThe model checkpoints can be used in 8-bit and 4-bit for further memory optimisations using bitsandbytes and transformers\n\nSee the snippet below for usage:\n\n```\nimport torch\nfrom transformers import AutoModelForCausalLM, AutoTokenizer\n\nmodel_id = \"meta-llama/Meta-Llama-3.1-70B-Instruct\"\nquantization_config = BitsAndBytesConfig(load_in_8bit=True)\n\nquantized_model = AutoModelForCausalLM.from_pretrained(\n    model_id, device_map=\"auto\", torch_dtype=torch.bfloat16, quantization_config=quantization_config)\n\ntokenizer = AutoTokenizer.from_pretrained(model_id)\ninput_text = \"What are we having for dinner?\"\ninput_ids = tokenizer(input_text, return_tensors=\"pt\").to(\"cuda\")\n\noutput = quantized_model.generate(**input_ids, max_new_tokens=10)\n\nprint(tokenizer.decode(output[0], skip_special_tokens=True))\n```\n\nTo load in 4-bit simply pass load\\_in\\_4bit=True\n\n**Use with llama**\n\nPlease, follow the instructions in the [repository](https://github.com/meta-llama/llama).\n\nTo download Original checkpoints, see the example command below leveraging huggingface-cli:\n\n```\nhuggingface-cli download meta-llama/Meta-Llama-3.1-70B-Instruct --include \"original/*\" --local-dir Meta-Llama-3.1-70B-Instruct\n```\n\n## **Training, Testing, and Evaluation Datasets:**\n\n### **Training Dataset**\n\n**Data Modality:** Text \n\n**Link:** Undisclosed\n\n**Data Collection Method:** Hybrid: Human, Synthetic, Automated\n\n**Labeling Method:** Hybrid: Automated, Human\n\n**Properties:** \n\nThe pretraining dataset contains over 15 trillion tokens. It is a multilingual dataset covering over 30 languages and was filtered heavily for quality using various techniques, including heuristic filters, NSFW filters, and text classifiers. The model's knowledge was trained on data with a cutoff of December 2023\\. \n\n### **Testing Dataset**\n\n**Link:** Undisclosed\n\n**Data Collection Method:** Hybrid: Human, Synthetic, Automated\n\n**Labeling Method:** Hybrid: Automated, Human\n\n**Properties:** \n\n**Description:**\n\nThe model was tested on a diverse set of evaluation data.\n\n* Public Benchmarks: These test a wide range of capabilities, from general knowledge and reasoning (MMLU, HellaSwag) to expert-level problem-solving (GPQA) and programming (HumanEval, which contains 164 programming problems).  \n* Internal Evaluation Set: Meta created a new high-quality test set of 2,000 prompts covering 12 key use cases (e.g., coding, reasoning, creative writing, instruction following). This set is used for human evaluation to assess performance on real-world, nuanced tasks. \n\n### **Evaluation Dataset**\n\n**Link:** Undisclosed\n\n**Data Collection Method:** Hybrid: Automated, Human, Synthetic\n\n**Labeling Method:** Hybrid: Human, Automated\n\n**Properties:** \n\nThe evaluation datasets are diverse and test a wide spectrum of capabilities. MMLU measures broad multitask knowledge. GPQA assesses advanced reasoning with difficult, expert-level questions. HumanEval and MATH specifically test code generation and mathematical reasoning abilities, respectively. Meta also utilizes a large, private, human-annotated evaluation set designed to assess model performance in real-world, nuanced scenarios. \n\n**Base pretrained models**\n\n| Category | Benchmark | \\# Shots | Metric | Llama 3 8B | Llama 3.1 8B | Llama 3 70B | Llama 3.1 70B | Llama 3.1 405B |\n| :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- |\n| General | MMLU | 5 | macro\\_avg/acc\\_char | 66.7 | 66.7 | 79.5 | 79.3 | 85.2 |\n|  | MMLU-Pro (CoT) | 5 | macro\\_avg/acc\\_char | 36.2 | 37.1 | 55.0 | 53.8 | 61.6 |\n|  | AGIEval English | 3-5 | average/acc\\_char | 47.1 | 47.8 | 63.0 | 64.6 | 71.6 |\n|  | CommonSenseQA | 7 | acc\\_char | 72.6 | 75.0 | 83.8 | 84.1 | 85.8 |\n|  | Winogrande | 5 | acc\\_char | \\- | 60.5 | \\- | 83.3 | 86.7 |\n|  | BIG-Bench Hard (CoT) | 3 | average/em | 61.1 | 64.2 | 81.3 | 81.6 | 85.9 |\n|  | ARC-Challenge | 25 | acc\\_char | 79.4 | 79.7 | 93.1 | 92.9 | 96.1 |\n| Knowledge reasoning | TriviaQA-Wiki | 5 | em | 78.5 | 77.6 | 89.7 | 89.8 | 91.8 |\n| Reading comprehension | SQuAD | 1 | em | 76.4 | 77.0 | 85.6 | 81.8 | 89.3 |\n|  | QuAC (F1) | 1 | f1 | 44.4 | 44.9 | 51.1 | 51.1 | 53.6 |\n|  | BoolQ | 0 | acc\\_char | 75.7 | 75.0 | 79.0 | 79.4 | 80.0 |\n|  | DROP (F1) | 3 | f1 | 58.4 | 59.5 | 79.7 | 79.6 | 84.8 |\n\n**Instruction tuned models**\n\n| Category | Benchmark | \\# Shots | Metric | Llama 3 8B Instruct | Llama 3.1 8B Instruct | Llama 3 70B Instruct | Llama 3.1 70B Instruct | Llama 3.1 405B Instruct |\n| :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- |\n| General | MMLU | 5 | macro\\_avg/acc | 68.5 | 69.4 | 82.0 | 83.6 | 87.3 |\n|  | MMLU (CoT) | 0 | macro\\_avg/acc | 65.3 | 73.0 | 80.9 | 86.0 | 88.6 |\n|  | MMLU-Pro (CoT) | 5 | micro\\_avg/acc\\_char | 45.5 | 48.3 | 63.4 | 66.4 | 73.3 |\n|  | IFEval |  |  | 76.8 | 80.4 | 82.9 | 87.5 | 88.6 |\n| Reasoning | ARC-C | 0 | acc | 82.4 | 83.4 | 94.4 | 94.8 | 96.9 |\n|  | GPQA | 0 | em | 34.6 | 30.4 | 39.5 | 46.7 | 50.7 |\n| Code | HumanEval | 0 | pass@1 | 60.4 | 72.6 | 81.7 | 80.5 | 89.0 |\n|  | MBPP \\++ base version | 0 | pass@1 | 70.6 | 72.8 | 82.5 | 86.0 | 88.6 |\n|  | Multipl-E HumanEval | 0 | pass@1 | \\- | 50.8 | \\- | 65.5 | 75.2 |\n|  | Multipl-E MBPP | 0 | pass@1 | \\- | 52.4 | \\- | 62.0 | 65.7 |\n| Math | GSM-8K (CoT) | 8 | em\\_maj1@1 | 80.6 | 84.5 | 93.0 | 95.1 | 96.8 |\n|  | MATH (CoT) | 0 | final\\_em | 29.1 | 51.9 | 51.0 | 68.0 | 73.8 |\n| Tool Use | API-Bank | 0 | acc | 48.3 | 82.6 | 85.1 | 90.0 | 92.0 |\n|  | BFCL | 0 | acc | 60.3 | 76.1 | 83.0 | 84.8 | 88.5 |\n|  | Gorilla Benchmark API Bench | 0 | acc | 1.7 | 8.2 | 14.7 | 29.7 | 35.3 |\n|  | Nexus (0-shot) | 0 | macro\\_avg/acc | 18.1 | 38.5 | 47.8 | 56.7 | 58.7 |\n| Multilingual | Multilingual MGSM (CoT) | 0 | em | \\- | 68.9 | \\- | 86.9 | 91.6 |\n\n**Multilingual benchmarks**\n\n| Category | Benchmark | Language | Llama 3.1 8B | Llama 3.1 70B | Llama 3.1 405B |\n| :---- | :---- | :---- | :---- | :---- | :---- |\n| General | MMLU (5-shot, macro\\_avg/acc) | Portuguese | 62.12 | 80.13 | 84.95 |\n|  |  | Spanish | 62.45 | 80.05 | 85.08 |\n|  |  | Italian | 61.63 | 80.4 | 85.04 |\n|  |  | German | 60.59 | 79.27 | 84.36 |\n|  |  | French | 62.34 | 79.82 | 84.66 |\n|  |  | Hindi | 50.88 | 74.52 | 80.31 |\n|  |  | Thai | 50.32 | 72.95 | 78.21 |\n\n## **Technical Limitations** \n\nTesting conducted to date has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, the model's potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying this model in any applications, developers should perform safety testing and tuning tailored to their specific applications. Please refer to available resources including the [Responsible Use Guide](https://llama.meta.com/responsible-use-guide), [Trust and Safety](https://llama.meta.com/trust-and-safety/) solutions, and other [resources](https://llama.meta.com/docs/get-started/) to learn more about responsible development. \n\n## **Inference:**\n\n**Acceleration Engine:** vLLM, TensorRT \n\n**Test Hardware:** \n\n  B200 SXM   \n  H200 SXM  \n  H100 SXM  \n  A100 SXM 80GB  \n  A100 SXM 40GB  \n  L40S PCIe  \n  A10G  \n  H100 NVL  \n  H200 NVL  \n  GH200 96GB  \n  GB200 NVL72   \n  GH200 NVL2     \n  RTX 5090  \n  RTX 4090  \n  RTX 6000 Ada\n\n## **Ethical Considerations:**\n\nNVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).\n\nYou are responsible for ensuring that your use of NVIDIA provided models complies with all applicable laws.",
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key: DOWNLOAD SIZE value: 263GB - key: LLM ENGINE value: TENSORRT_LLM - profileId: nim/meta/llama-3.1-70b-instruct:gb200x4-latency-bf16-3uozpudciw framework: TensorRT-LLM displayName: Llama 3.1 70B Instruct GB200x4 BF16 Latency ngcMetadata: fe20ab9158c65c3e7765e50c5c72ece46ee34a9e184dcdb13eda9bbef78ab300: model: meta/llama-3.1-70b-instruct release: 1.14.0 tags: feat_lora: 'false' gpu: GB200 gpu_device: 2941:10de llm_engine: tensorrt_llm nim_workspace_hash_v1: e6a18cf6935b817ca2968d06e1abd91444d73634b80ff54f3680d152cadc209e number_of_gpus: '4' pp: '1' precision: bf16 profile: latency tp: '4' modelFormat: trt-llm spec: - key: PROFILE value: LATENCY - key: PRECISION value: BF16 - key: GPU value: GB200 - key: COUNT value: 4 - key: GPU DEVICE value: 2941:10DE - key: NIM VERSION value: 1.14.0 - key: DOWNLOAD SIZE value: 138GB - key: LLM ENGINE value: TENSORRT_LLM labels: - Llama - Meta - Text Generation - Large Language Model - TensorRT-LLM - Language Generation - NeMo - NVIDIA Validated config: architectures: - Other modelType: llama license: NVIDIA AI Foundation Models Community License - name: StarCoder2-7B displayName: StarCoder2-7B modelHubID: starcoder-2 category: Code type: NGC description: Code generation model trained on 17 programming languages. The model is optimized for code completion, synthesis, and fill-in-the-middle objective. requireLicense: true licenseAgreements: - label: Use Policy url: https://llama.meta.com/llama3/use-policy/ - label: License Agreement url: https://llama.meta.com/llama3/license/ modelVariants: - variantId: StarCoder2-7B modelCard: {
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    "description": "# **Starcoder2-7B Overview**\n\n## **Description:**\n\n**StarCoder2-7B** generates source code from natural language instructions and code prompts across a wide range of programming languages. This 7-billion parameter model is part of the next generation of open-source large language models for code, developed by the BigCode collaboration, and was trained on The Stack v2, a massive, permissively licensed dataset covering over 600 programming languages. It is specifically designed to assist with tasks like code completion, code synthesis, and infilling (filling in missing code within a file).\n\nThis model is ready for commercial/non-commercial use.\n\nThis version introduces support for GB200 NVL72, GH200 NVL2, B200 and NVFP4. CUDA updated to version 12.9. For detailed information, refer to Release [Notes for NVIDIA NIM for LLMs LLM 1.12](https://docs.nvidia.com/nim/large-language-models/latest/release-notes.html). \n\n## **Third-Party Community Consideration**\n\nThis model is not owned or developed by NVIDIA. This model has been developed and built to a third-party's requirements for this application and use case; see link to Non-NVIDIA \\[bigcode/starcoder2-7b\\]  \n([bigcode/starcoder2-7b \u00b7 Hugging Face](https://huggingface.co/bigcode/starcoder2-7b##license)). \n\n## **License/Terms of Use:**\n\n**GOVERNING TERMS:** The NIM container is governed by the [NVIDIA Software License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-license-agreement/) and the [Product-Specific Terms for NVIDIA AI Products](https://www.nvidia.com/en-us/agreements/enterprise-software/product-specific-terms-for-ai-products/); except for the model which is governed by the [NVIDIA Community Model License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-community-models-license/.).\n\n**ADDITIONAL INFORMATION:** [BigCode Open RAIL-M v1 License Agreement](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement).\n\n## **Get Help**\n\n### Enterprise Support\nGet access to knowledge base articles and support cases or [submit a ticket](https://www.nvidia.com/en-us/data-center/products/ai-enterprise-suite/support/).\nYou are responsible for ensuring that your use of NVIDIA provided models complies with all applicable laws.\n\n## **Deployment Geography:**\n\nGlobal \n\n## **Use Case:**\n\nThe expected users of StarCoder2-7B are software developers, data scientists, programmers, and students. They would use this model as a sophisticated coding assistant integrated into their development environment (like VS Code) or as a standalone tool for the following purposes:\n\n* Accelerating Development: To significantly speed up the coding process using intelligent, context-aware code completion and infilling.\n\n* Code Synthesis: To generate entire functions, classes, or scripts from a natural language description (e.g., \"Write a JavaScript function that validates an email address using regex\").\n\n* Rapid Prototyping: To quickly create boilerplate code or functional prototypes for new applications or features.\n\n* Learning & Debugging: To understand a new programming language or framework by generating example code, or to get suggestions on how to refactor or fix a piece of buggy code.\n\n* Automation: To automate the creation of repetitive code, such as unit tests, data processing scripts, or configuration files.\n\n## **Release Date:**\n\nBuild.NVIDIA.com 03/18/2024 via  \n[starcoder2-7b Model by BigCode | NVIDIA NIM](https://build.nvidia.com/bigcode/starcoder2-7b)\n\nGithub 02/28/2024 via   \n[GitHub \\- bigcode-project/starcoder2: Home of StarCoder2\\!](https://github.com/bigcode-project/starcoder2)\n\nHuggingface 02/28/2024 via   \n[bigcode/starcoder2-7b \u00b7 Hugging Face](https://huggingface.co/bigcode/starcoder2-7b##license)\n\n## **Reference(s):** \n\n[bigcode/starcoder2-7b \u00b7 Hugging Face](https://huggingface.co/bigcode/starcoder2-7b##license)\n\n## **Model Architecture:** \n\nArchitecture Type: Transformer  \nNetwork Architecture: StarCoder2\n\nThis model was developed based on The Stack V2  \n[https://huggingface.co/datasets/bigcode/the-stack-v2-train-full-ids](https://huggingface.co/datasets/bigcode/the-stack-v2-train-full-ids) \n\nNumber of model parameters: 0.717*10^10\n\n## **Input:**\n\nInput Type(s): Text \n\nInput Format: String \n\nInput Parameters: One-Dimensional (1D)\n\nOther Properties Related to Input:\n\n* Tokens: The model accepts a sequence of tokens with a maximum context window of 16,384 tokens. This limit encompasses both the input prompt and the tokens generated by the model.  \n* Characters: Input strings can contain a wide range of Unicode characters, consistent with the diverse set of programming languages and natural languages found in the training data.  \n* Pre-Processing Needed: Raw input strings must be converted into a sequence of integer token IDs using the specific tokenizer associated with the StarCoder2-7B  model before being fed into the network.\n\n \n\n## **Output:**\n\nOutput Type(s): Text \n\nOutput Format: String\n\nOutput Parameters: One-Dimensional (1D)\n\nOther Properties Related to Output: \n\n* Tokens: The model generates a sequence of tokens. The length of the output is variable and is ultimately constrained by the model's maximum context window of 16,384 tokens (the sum of input and output tokens cannot exceed this limit).  \n* Characters (Including Restrictions): The output consists of a wide range of Unicode characters from the model's vocabulary, designed to produce syntactically correct source code and coherent natural language.  \n* Post-Processing Needed: The raw token IDs generated by the model must be decoded using the model's specific tokenizer to be converted into a human-readable string. Special control tokens (e.g., end-of-sequence tokens) may also need to be filtered from the final output.\n\nOur AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA's hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.\n\n## **Software Integration:**\n\nRuntime Engine: vLLM, TensorRT\n\nSupported Hardware Microarchitecture Compatibility: NVIDIA Hopper  \n\nPreferred Operating System(s):\n\nLinux   \nWindows\n\nThe integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.\n\n## **Model Version(s):**\n\nStarCoder2-7B-1.10.1 <br>\nStarCoder2-7B-1.12.0 <br>\nStarCoder2-7B-1.13.1 <br>\nStarCoder2-7B-1.14.0 <br>\nStarCoder2-7B-1.15.0 <br>\n\n## **Usage**\n\n**Running the model on CPU/GPU/multi GPU**\n\n* Using full precision:\n\n```\n# pip install git+https://github.com/huggingface/transformers.git # TODO: merge PR to main\nfrom transformers import AutoModelForCausalLM, AutoTokenizer\n\ncheckpoint = \"bigcode/starcoder2-7b\"\ndevice = \"cuda\" # for GPU usage or \"cpu\" for CPU usage\n\ntokenizer = AutoTokenizer.from_pretrained(checkpoint)\n# for multiple GPUs install accelerate and do `model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map=\"auto\")`\nmodel = AutoModelForCausalLM.from_pretrained(checkpoint).to(device)\n\ninputs = tokenizer.encode(\"def print_hello_world():\", return_tensors=\"pt\").to(device)\noutputs = model.generate(inputs)\nprint(tokenizer.decode(outputs[0]))\n```\n\n* Using torch.bfloat16:\n\n```\n# pip install accelerate\nimport torch\nfrom transformers import AutoTokenizer, AutoModelForCausalLM\n\ncheckpoint = \"bigcode/starcoder2-7b\"\ntokenizer = AutoTokenizer.from_pretrained(checkpoint)\n\n# for fp16 use `torch_dtype=torch.float16` instead\nmodel = AutoModelForCausalLM.from_pretrained(checkpoint, device_map=\"auto\", torch_dtype=torch.bfloat16)\n\ninputs = tokenizer.encode(\"def print_hello_world():\", return_tensors=\"pt\").to(\"cuda\")\noutputs = model.generate(inputs)\nprint(tokenizer.decode(outputs[0]))\n```\n\n```\nprint(f\"Memory footprint: {model.get_memory_footprint() / 1e6:.2f} MB\")\n```\n\n**Quantized Versions through bitsandbytes**\n\n* Using 8-bit precision (int8):\n\n```\n# pip install bitsandbytes accelerate\nfrom transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig\n\n# to use 4bit use `load_in_4bit=True` instead\nquantization_config = BitsAndBytesConfig(load_in_8bit=True)\n\ncheckpoint = \"bigcode/starcoder2-7b\"\ntokenizer = AutoTokenizer.from_pretrained(checkpoint)\nmodel = AutoModelForCausalLM.from_pretrained(checkpoint, quantization_config=quantization_config)\n\ninputs = tokenizer.encode(\"def print_hello_world():\", return_tensors=\"pt\").to(\"cuda\")\noutputs = model.generate(inputs)\nprint(tokenizer.decode(outputs[0]))\n```\n\n```\n\n>>> print(f\"Memory footprint: {model.get_memory_footprint() / 1e6:.2f} MB\")\n# load_in_8bit\nMemory footprint: 7670.52 MB\n# load_in_4bit\n>>> print(f\"Memory footprint: {model.get_memory_footprint() / 1e6:.2f} MB\")\nMemory footprint: 4197.64 MB\n```\n\n## **Training, Testing, and Evaluation Datasets:**\n\n### **Training Dataset:**\n\n**Data Modality:** Text \n\n**Link:** Undisclosed\n\n**Data Collection Method by dataset:** Hybrid: Human, Synthetic, Automated\n\n**Labeling Method by dataset:** Hybrid: Human, Synthetic, Automated\n\n**Properties:** \n\n* Quantity: StarCoder2-7B was trained on a processed subset of the dataset totaling approximately 3.3 trillion tokens. The complete, pre-processed Stack v2 dataset contains over 67TB of data.  \n* Dataset Descriptions: The Stack v2 is a very large dataset of source code from over 600 programming languages, supplemented with text from GitHub Issues, Pull Requests, and Kaggle Notebooks. A key feature of the dataset is that it has been filtered to only include content from repositories with permissive licenses. It also underwent an extensive PII (Personally Identifiable Information) redaction process to remove sensitive data.\n\n### **Testing Dataset:**\n\n**Link:** Undisclosed\n\n**Data Collection Method by dataset:** Hybrid: Human, Automated\n\n**Labeling Method by dataset:** Hybrid: Human, Automated\n\n**Properties:** \n\nQuantity: The benchmarks consist of a varied number of problems (e.g., HumanEval: 164 problems; MBPP: \\~400 test problems; DS-1000: 1,000 problems).\n\nDataset Descriptions: This is a collection of evaluation suites, not a single dataset. They are designed to measure the model's performance on specific downstream tasks:\n\n* HumanEval/MBPP: Test Python code generation from natural language docstrings.  \n* DS-1000: Tests code generation for data science tasks (e.g., using Pandas, NumPy).  \n* CruxEval: Tests the model's ability to predict the output of a given code snippet. The primary goal is to assess the functional correctness and logical reasoning capabilities of the model.\n\n### **Evaluation Dataset:**\n\n**Link:** Undisclosed\n\n**Data Collection Method by dataset:** Hybrid: Automated, Human, Synthetic\n\n**Labeling Method by dataset:** Hybrid: Human, Automated\n\n**Properties:** \n\nQuantity: The benchmarks consist of a varied number of problems (e.g., HumanEval: 164 problems; MBPP: \\~400 test problems; DS-1000: 1,000 problems).\n\nDataset Descriptions: This is a collection of evaluation suites designed to measure the model's performance on specific downstream tasks:\n\n* HumanEval/MBPP: Test Python code generation from natural language docstrings.  \n* DS-1000: Tests code generation for data science tasks (e.g., using Pandas, NumPy).  \n* CruxEval: Tests the model's ability to predict the output of a given code snippet. The primary goal is to assess the functional correctness and logical reasoning capabilities of the model. \n\n## **Technical Limitations** \n\nThe model has been trained on source code from 17 programming languages. The predominant language in source is English although other languages are also present. As such the model is capable of generating code snippets provided some context but the generated code is not guaranteed to work as intended. It can be inefficient and contain bugs or exploits. See [the paper](https://huggingface.co/papers/2402.19173) for an in-depth discussion of the model limitations. \n\n## **Inference:**\n\n**Acceleration Engine:** vLLM, TensorRT \n\n**Test Hardware:** \n  \n  H100 SXM <br> \n  H200 SXM (BF16 TP2) <br> \n  \n\n## **Ethical Considerations:**\n\nNVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).\n\nYou are responsible for ensuring that your use of NVIDIA provided models complies with all applicable laws.",
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    "description": "# **Mistral-7B-Instruct-v0.3 Overview**\n\n## **Description:**\n\n**Mistral-7B-Instruct-v0.3** is a large language model (LLM) that has been fine-tuned for instruction-based tasks. It is an improved version of the Mistral-7B-v0.3 model and is designed to be easily fine-tuned to achieve compelling performance. \n\nThis model is ready for commercial/non-commercial use.\n\nThis version introduces support for GB200 NVL72, GH200 NVL2, B200 and NVFP4. CUDA updated to version 12.9. For detailed information, refer to Release [Notes for NVIDIA NIM for LLMs LLM 1.12](https://docs.nvidia.com/nim/large-language-models/latest/release-notes.html). \n\n## **Third-Party Community Consideration**\n\nThis model is not owned or developed by NVIDIA. This model has been developed and built to a third-party's requirements for this application and use case; see link to Non-NVIDIA \\[mistralai/Mistral-7B-Instruct-v0.3\\]  \n([https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3)). \n\n## **License/Terms of Use:**\n\n**GOVERNING TERMS:** The NIM container is governed by the [NVIDIA Software License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-license-agreement/) and the [Product-Specific Terms for NVIDIA AI Products](https://www.nvidia.com/en-us/agreements/enterprise-software/product-specific-terms-for-ai-products/); and the use of the model is governed by the [NVIDIA AI Foundation Models Community License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-ai-foundation-models-community-license-agreement/).\n\n**ADDITIONAL INFORMATION:** Apache 2.0 License.\n\nYou are responsible for ensuring that your use of NVIDIA provided models complies with all applicable laws.\n\n## **Deployment Geography:**\n\nGlobal \n\n## **Use Case:**\n\nThis model is primarily intended for AI developers, researchers, and businesses seeking a powerful yet efficient foundational language model.\n\n### Expected uses include:\n\n* Fine-tuning for Custom Applications: Developers can use Mistral-7B-Instruct-v0.3 as a base to train specialized models for tasks like creating customer service chatbots, content summarization tools, code generation assistants, and sentiment analysis systems.\n\n* Research and Experimentation: Researchers can leverage this open-source model to study language model behavior, explore new training techniques, or establish performance benchmarks on various natural language processing tasks.\n\n* Prototyping AI Solutions: This model's excellent balance of high performance and relatively low computational cost makes it ideal for startups and individual developers to rapidly build and test proof-of-concept AI features before deploying larger-scale solutions.\n\n## **Release Date:**\n\nHuggingface 05/22/2024 via   \n[https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3) \n\n## **Reference(s):** \n\n[https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3) \n\n## **Model Architecture:** \n\nArchitecture Type: Transformer  \nNetwork Architecture: Mistral-7B-v0.3\n\nThis model was developed based on Mistral-7B-v0.3  \n[https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3)  \n\nNumber of model parameters: 7.25*10^9\n\n## **Input:**\n\nInput Type(s): Text \n\nInput Format: String \n\nInput Parameters: One-Dimensional (1D)\n\nOther Properties Related to Input:\n\n* Tokens: The model processes text as a sequence of tokens. The input string is converted into a sequence of integer token IDs by a tokenizer. The vocabulary size is 32,768 tokens.  \n    \n* Size and Length Limits: The maximum input sequence length supported by the model is 32,768 tokens.  \n    \n* Pre-Processing Needed: Yes. Raw input text must be tokenized. For chat or instruction-following tasks, the input must be formatted according to the model's specific chat template, which typically involves wrapping user prompts in \\[INST\\] and \\[/INST\\] tags.\n\n \n\n## **Output:**\n\nOutput Type(s): Text \n\nOutput Format: String\n\nOutput Parameters: One-Dimensional (1D)\n\nOther Properties Related to Output: \n\n* Tokens: The model generates a sequence of token IDs from its vocabulary of 32,768 tokens.  \n    \n* Post-Processing Needed: Yes. The generated sequence of token IDs must be decoded by the tokenizer to be converted into a human-readable string.  \n* Length: The output length is variable and is controlled by generation parameters. Generation stops when an end-of-sequence (EOS) token is produced or the maximum length is reached.\n\nOur AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA's hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.\n\n## **Software Integration:**\n\nRuntime Engine: vLLM, TensorRT\n\nSupported Hardware Microarchitecture Compatibility:\n\nNVIDIA Ampere  \nNVIDIA Blackwell  \nNVIDIA Hopper  \nNVIDIA Lovelace \n\nPreferred Operating System(s):\n\nLinux   \nWindows\n\nThe integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.\n\n## **Model Version(s):**\n\nMistral-7B-Instruct-v0.3\n\n## **Usage**\n\n**Instruct following**\n\n```\nfrom mistral_inference.transformer import Transformer\nfrom mistral_inference.generate import generate\n\nfrom mistral_common.tokens.tokenizers.mistral import MistralTokenizer\nfrom mistral_common.protocol.instruct.messages import UserMessage\nfrom mistral_common.protocol.instruct.request import ChatCompletionRequest\n\n\ntokenizer = MistralTokenizer.from_file(f\"{mistral_models_path}/tokenizer.model.v3\")\nmodel = Transformer.from_folder(mistral_models_path)\n\ncompletion_request = ChatCompletionRequest(messages=[UserMessage(content=\"Explain Machine Learning to me in a nutshell.\")])\n\ntokens = tokenizer.encode_chat_completion(completion_request).tokens\n\nout_tokens, _ = generate([tokens], model, max_tokens=64, temperature=0.0, eos_id=tokenizer.instruct_tokenizer.tokenizer.eos_id)\nresult = tokenizer.instruct_tokenizer.tokenizer.decode(out_tokens[0])\n\nprint(result)\n```\n\n**Function calling**\n\n```\nfrom mistral_common.protocol.instruct.tool_calls import Function, Tool\nfrom mistral_inference.transformer import Transformer\nfrom mistral_inference.generate import generate\n\nfrom mistral_common.tokens.tokenizers.mistral import MistralTokenizer\nfrom mistral_common.protocol.instruct.messages import UserMessage\nfrom mistral_common.protocol.instruct.request import ChatCompletionRequest\n\n\ntokenizer = MistralTokenizer.from_file(f\"{mistral_models_path}/tokenizer.model.v3\")\nmodel = Transformer.from_folder(mistral_models_path)\n\ncompletion_request = ChatCompletionRequest(\n    tools=[\n        Tool(\n            function=Function(\n                name=\"get_current_weather\",\n                description=\"Get the current weather\",\n                parameters={\n                    \"type\": \"object\",\n                    \"properties\": {\n                        \"location\": {\n                            \"type\": \"string\",\n                            \"description\": \"The city and state, e.g. San Francisco, CA\",\n                        },\n                        \"format\": {\n                            \"type\": \"string\",\n                            \"enum\": [\"celsius\", \"fahrenheit\"],\n                            \"description\": \"The temperature unit to use. Infer this from the users location.\",\n                        },\n                    },\n                    \"required\": [\"location\", \"format\"],\n                },\n            )\n        )\n    ],\n    messages=[\n        UserMessage(content=\"What's the weather like today in Paris?\"),\n        ],\n)\n\ntokens = tokenizer.encode_chat_completion(completion_request).tokens\n\nout_tokens, _ = generate([tokens], model, max_tokens=64, temperature=0.0, eos_id=tokenizer.instruct_tokenizer.tokenizer.eos_id)\nresult = tokenizer.instruct_tokenizer.tokenizer.decode(out_tokens[0])\n\nprint(result)\n```\n\n**Generate with transformers**\n\n```\nfrom transformers import pipeline\n\nmessages = [\n    {\"role\": \"system\", \"content\": \"You are a pirate chatbot who always responds in pirate speak!\"},\n    {\"role\": \"user\", \"content\": \"Who are you?\"},\n]\nchatbot = pipeline(\"text-generation\", model=\"mistralai/Mistral-7B-Instruct-v0.3\")\nchatbot(messages)\n```\n\n**Function calling with transformers**\n\nTo use this example, you'll need transformers version 4.42.0 or higher. Please see the [function calling guide](https://huggingface.co/docs/transformers/main/chat_templating#advanced-tool-use--function-calling) in the transformers docs for more information.\n\n```\nfrom transformers import AutoModelForCausalLM, AutoTokenizer\nimport torch\n\nmodel_id = \"mistralai/Mistral-7B-Instruct-v0.3\"\ntokenizer = AutoTokenizer.from_pretrained(model_id)\n\ndef get_current_weather(location: str, format: str):\n    \"\"\"\n    Get the current weather\n\n    Args:\n        location: The city and state, e.g. San Francisco, CA\n        format: The temperature unit to use. Infer this from the users location. (choices: [\"celsius\", \"fahrenheit\"])\n    \"\"\"\n    pass\n\nconversation = [{\"role\": \"user\", \"content\": \"What's the weather like in Paris?\"}]\ntools = [get_current_weather]\n\n\n# format and tokenize the tool use prompt \ninputs = tokenizer.apply_chat_template(\n            conversation,\n            tools=tools,\n            add_generation_prompt=True,\n            return_dict=True,\n            return_tensors=\"pt\",\n)\n\nmodel = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map=\"auto\")\n\ninputs.to(model.device)\noutputs = model.generate(**inputs, max_new_tokens=1000)\nprint(tokenizer.decode(outputs[0], skip_special_tokens=True))\n```\n\n## **Training, Testing, and Evaluation Datasets:**\n\n### **Training Dataset:**\n\n**Data Modality:** Text \n\n**Link:** Undisclosed\n\n**Data Collection Method by dataset:** Hybrid: Human, Synthetic, Automated\n\n**Labeling Method by dataset:** Hybrid: Automated, Synthetic, Human\n\n**Properties:** The pre-training data is a diverse mix of text and code from the public web. The fine-tuning data consists of high-quality instruction-response pairs.\n\n### **Testing Dataset:**\n\n**Link:** Undisclosed\n\n**Data Collection Method by dataset:** Hybrid: Human, Synthetic, Automated\n\n**Labeling Method by dataset:** Hybrid: Human, Automated\n\n**Properties:** \n\n* Quantity: The number of data items varies significantly per benchmark (e.g., MMLU has \\~15.9k questions, HellaSwag has \\~10k sentences).  \n* Dataset Descriptions: The model is tested against benchmarks designed to evaluate a wide range of capabilities, including: general knowledge and reasoning (MMLU, ARC), commonsense inference (HellaSwag, Winogrande), truthfulness (TruthfulQA), conversational ability (MT-Bench), and code generation (HumanEval, MBPP). \n\n### **Evaluation Dataset:**\n\n**Link:** Undisclosed\n\n**Data Collection Method by dataset:** Hybrid: Human, Synthetic, Automated\n\n**Labeling Method by dataset:** Hybrid: Human, Automated\n\n**Properties:** The model is evaluated against benchmarks designed to measure a wide range of capabilities, including: general knowledge and reasoning (MMLU), truthfulness (TruthfulQA), conversational ability (MT-Bench), and code generation (HumanEval). The quantity of data varies significantly per benchmark.\n\n## **Technical Limitations** \n\nThe Mistral-7B-Instruct model is a quick demonstration that the base model can be easily fine-tuned to achieve compelling performance. It does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to leverage guardrails, allowing for deployment in environments requiring moderated outputs. \n\n## **Inference:**\n\n**Acceleration Engine:** vLLM, TensorRT \n\n**Test Hardware:** \n \n* B200 SXM   \n* H200 SXM  \n* H100 SXM  \n* A100 SXM 80GB  \n* A100 SXM 40GB  \n* L40S PCIe  \n* A10G  \n* H100 NVL  \n* H200 NVL  \n* GH200 96GB\n* GB200 NVL72\n* GH200 NVL2   \n* RTX 5090  \n* RTX 4090  \n* RTX 6000 Ada\n\n## **Deployment Details:**\n\nVisit the [NIM Container LLM](https://docs.nvidia.com/nim/large-language-models/latest/introduction.html) page for release documentation, deployment guides, and more.\n\n## Get Help\n\n## **Enterprise Support**\nGet access to knowledge base articles and support cases or [submit a ticket](https://www.nvidia.com/en-us/data-center/products/ai-enterprise-suite/support/).\n\nOur AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA\u2019s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.\n\n## **Ethical Considerations:**\n\nNVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).\n\n**You are responsible for ensuring that your use of NVIDIA AI Foundation Models complies with all applicable laws.**",
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key: LLM ENGINE value: TENSORRT_LLM - profileId: nim/mistralai/mistral-7b-instruct-v0-3:h200x1-throughput-fp8-ez1figc57w framework: TensorRT-LLM displayName: Mistral 7B Instruct V0.3 H200x1 FP8 Throughput ngcMetadata: fd130b01c59445a3967c804d57077fd77a3b2f76048603516402320f809f88ad: model: mistralai/mistral-7b-instruct-v0.3 release: 1.12.0 tags: feat_lora: 'false' gpu: H200 gpu_device: 2335:10de llm_engine: tensorrt_llm nim_workspace_hash_v1: f830f542f5a36acc42ddf3d7f7823662ed361c4e8e0ee0007cfe70da3df2fb9f number_of_gpus: '2' pp: '1' precision: fp8 profile: throughput tp: '1' modelFormat: trt-llm spec: - key: PROFILE value: THROUGHPUT - key: PRECISION value: FP8 - key: GPU value: H200 - key: COUNT value: 1 - key: GPU DEVICE value: 2335:10DE - key: NIM VERSION value: 1.12.0 - key: DOWNLOAD SIZE value: 8GB - key: LLM ENGINE value: TENSORRT_LLM labels: - Mistral - Instruct - Large Language Model - TensorRT-LLM - Language Generation - NeMo - NVIDIA Validated config: architectures: - Other modelType: Mistral license: NVIDIA AI Foundation Models Community License - name: Mixtral Instruct displayName: Mixtral Instruct modelHubID: mixtral-instruct category: Language type: NGC description: Sparse Mixture-of-Experts model fine-tuned for instruction following with a 32K token context window. Matches or outperforms much larger dense models on standard benchmarks. modelVariants: - variantId: Mixtral 8x7B Instruct modelCard: 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displayName: Mixtral 8x7B Instruct source: URL: https://catalog.ngc.nvidia.com/orgs/nim/teams/mistralai/containers/mixtral-8x7b-instruct-v01 optimizationProfiles: - profileId: nim/mistralai/mixtral-8x7b-instruct-v01:a100x2-throughput-bf16-s69xvudfza framework: TensorRT-LLM displayName: Mixtral 8x7b Instruct V0.1 A100x2 BF16 Throughput ngcMetadata: 0db3b5e8468c9debf30bcf41cbfea084adc59000885efd6fdcb3bbb902651bd6: model: mistralai/mixtral-8x7b-instruct-v0.1 release: 1.8.4 tags: feat_lora: 'false' gpu: A100 gpu_device: 20b2:10de llm_engine: tensorrt_llm number_of_gpus: '2' pp: '1' precision: bf16 profile: throughput tp: '2' modelFormat: trt-llm spec: - key: PROFILE value: THROUGHPUT - key: PRECISION value: BF16 - key: GPU value: A100 - key: COUNT value: 2 - key: GPU DEVICE value: 20B2:10DE - key: NIM VERSION value: 1.8.4 - key: DOWNLOAD SIZE value: 88GB - key: LLM ENGINE value: TENSORRT_LLM - profileId: nim/mistralai/mixtral-8x7b-instruct-v01:h100x2-throughput-bf16-zwhl2fsi5a framework: TensorRT-LLM displayName: Mixtral 8x7b Instruct V0.1 H100x2 BF16 Throughput ngcMetadata: 1617d074ce252f66e96d5f0e331fa5c6cc0a0330519e56b5c66c60eb7d7bf4f9: model: mistralai/mixtral-8x7b-instruct-v0.1 release: 1.8.4 tags: feat_lora: 'false' gpu: H100 gpu_device: 2330:10de llm_engine: tensorrt_llm number_of_gpus: '2' pp: '1' precision: bf16 profile: throughput tp: '2' modelFormat: trt-llm spec: - key: PROFILE value: THROUGHPUT - key: PRECISION value: BF16 - key: GPU value: H100 - key: COUNT value: 2 - key: GPU DEVICE value: 2330:10DE - key: NIM VERSION value: 1.8.4 - key: DOWNLOAD SIZE value: 88GB - key: LLM ENGINE value: TENSORRT_LLM - profileId: nim/mistralai/mixtral-8x7b-instruct-v01:hf-a60832c-0508-tool-use-v2 framework: TensorRT-LLM displayName: Mixtral 8x7b Instruct V0.1 H100_NVLx4 BF16 Latency ngcMetadata: 28552abdb2c491d46065d52ca1dc1265b99ba95a5bf8daaee4c5de12511a3b4f: model: mistralai/mixtral-8x7b-instruct-v0.1 release: 1.8.4 tags: feat_lora: 'false' gpu: H100_NVL gpu_device: 2321:10de llm_engine: tensorrt_llm number_of_gpus: '4' pp: '1' precision: bf16 profile: latency tp: '4' modelFormat: trt-llm spec: - key: PROFILE value: LATENCY - key: PRECISION value: BF16 - key: GPU value: H100_NVL - key: COUNT value: 4 - key: GPU DEVICE value: 2321:10DE - key: NIM VERSION value: 1.8.4 - key: DOWNLOAD SIZE value: 87GB - key: LLM ENGINE value: TENSORRT_LLM - profileId: nim/mistralai/mixtral-8x7b-instruct-v01:l40sx4-throughput-fp8-hbavqk65yw framework: TensorRT-LLM displayName: Mixtral 8x7b Instruct V0.1 L40Sx4 FP8 Throughput ngcMetadata: 3d0e5989f2fbc23e7d4504cd69269c9636deb61d0efc12225d3d59d54afea297: model: mistralai/mixtral-8x7b-instruct-v0.1 release: 1.8.4 tags: feat_lora: 'false' gpu: L40S gpu_device: 26b9:10de llm_engine: tensorrt_llm number_of_gpus: '4' pp: '1' precision: fp8 profile: throughput tp: '4' modelFormat: trt-llm spec: - key: PROFILE value: THROUGHPUT - key: PRECISION value: FP8 - key: GPU value: L40S - key: COUNT value: 4 - key: GPU DEVICE value: 26B9:10DE - key: NIM VERSION value: 1.8.4 - key: DOWNLOAD SIZE value: 45GB - key: LLM ENGINE value: TENSORRT_LLM - profileId: nim/mistralai/mixtral-8x7b-instruct-v01:h200x1-throughput-bf16-00qqbltmrg framework: TensorRT-LLM displayName: Mixtral 8x7b Instruct V0.1 H200x1 BF16 Throughput ngcMetadata: 434e8d336fa23cbe151748d32b71e196d69f20d319ee8b59852a1ca31a48d311: model: mistralai/mixtral-8x7b-instruct-v0.1 release: 1.8.4 tags: feat_lora: 'false' gpu: H200 gpu_device: 2335:10de llm_engine: tensorrt_llm number_of_gpus: '1' pp: '1' precision: bf16 profile: throughput tp: '1' modelFormat: trt-llm spec: - key: PROFILE value: THROUGHPUT - key: PRECISION value: BF16 - key: GPU value: H200 - key: COUNT value: 1 - key: GPU DEVICE value: 2335:10DE - key: NIM VERSION value: 1.8.4 - key: DOWNLOAD SIZE value: 88GB - key: LLM ENGINE value: TENSORRT_LLM - profileId: nim/mistralai/mixtral-8x7b-instruct-v01:b200x2-latency-fp8-pwnesuqgxg framework: TensorRT-LLM displayName: Mixtral 8x7b Instruct V0.1 B200x2 FP8 Latency ngcMetadata: 4950d30811e1e426e97cda69e6c03a8a4819db8aa4abf34722ced4542a1f6b52: model: mistralai/mixtral-8x7b-instruct-v0.1 release: 1.8.4 tags: feat_lora: 'false' gpu: B200 gpu_device: 2901:10de llm_engine: tensorrt_llm number_of_gpus: '2' pp: '1' precision: fp8 profile: latency tp: '2' modelFormat: trt-llm spec: - key: PROFILE value: LATENCY - key: PRECISION value: FP8 - key: GPU value: B200 - key: COUNT value: 2 - key: GPU DEVICE value: 2901:10DE - key: NIM VERSION value: 1.8.4 - key: DOWNLOAD SIZE value: 44GB - key: LLM ENGINE value: TENSORRT_LLM - profileId: nim/mistralai/mixtral-8x7b-instruct-v01:hf-a60832c-0508-tool-use-v2__2 framework: TensorRT-LLM displayName: Mixtral 8x7b Instruct V0.1 H100_NVLx1 FP8 Throughput ngcMetadata: 5811750e70b7e9f340f4d670c72fcbd5282e254aeb31f62fd4f937cfb9361007: model: mistralai/mixtral-8x7b-instruct-v0.1 release: 1.8.4 tags: feat_lora: 'false' gpu: H100_NVL gpu_device: 2321:10de llm_engine: tensorrt_llm number_of_gpus: '1' pp: '1' precision: fp8 profile: throughput tp: '1' modelFormat: trt-llm spec: - key: PROFILE value: THROUGHPUT - key: PRECISION value: FP8 - key: GPU value: H100_NVL - key: COUNT value: 1 - key: GPU DEVICE value: 2321:10DE - key: NIM VERSION value: 1.8.4 - key: DOWNLOAD SIZE value: 87GB - key: LLM ENGINE value: TENSORRT_LLM - profileId: nim/mistralai/mixtral-8x7b-instruct-v01:h200x2-latency-bf16-uh6awyzpta framework: TensorRT-LLM displayName: Mixtral 8x7b Instruct V0.1 H200x2 BF16 Latency ngcMetadata: 6832a9395f54086162fd7b1c6cfaae17c7d1e535a60e2b7675504c9fc7b57689: model: mistralai/mixtral-8x7b-instruct-v0.1 release: 1.8.4 tags: feat_lora: 'false' gpu: H200 gpu_device: 2335:10de llm_engine: tensorrt_llm number_of_gpus: '2' pp: '1' precision: bf16 profile: latency tp: '2' modelFormat: trt-llm spec: - key: PROFILE value: LATENCY - key: PRECISION value: BF16 - key: GPU value: H200 - key: COUNT value: 2 - key: GPU DEVICE value: 2335:10DE - key: NIM VERSION value: 1.8.4 - key: DOWNLOAD SIZE value: 88GB - key: LLM ENGINE value: TENSORRT_LLM - profileId: nim/mistralai/mixtral-8x7b-instruct-v01:hf-a60832c-0508-tool-use-v2__3 framework: TensorRT-LLM displayName: Mixtral 8x7b Instruct V0.1 A100_SXM4_40GBx4 BF16 Throughput ngcMetadata: 6c29727e6e3d48a900c348c1fab181dc40bc926be07b06ca5b8eae42a6bc9901: model: mistralai/mixtral-8x7b-instruct-v0.1 release: 1.8.4 tags: feat_lora: 'false' gpu: A100_SXM4_40GB gpu_device: 20b0:10de llm_engine: tensorrt_llm number_of_gpus: '4' pp: '1' precision: bf16 profile: throughput tp: '4' modelFormat: trt-llm spec: - key: PROFILE value: THROUGHPUT - key: PRECISION value: BF16 - key: GPU value: A100_SXM4_40GB - key: COUNT value: 4 - key: GPU DEVICE value: 20B0:10DE - key: NIM VERSION value: 1.8.4 - key: DOWNLOAD SIZE value: 87GB - key: LLM ENGINE value: TENSORRT_LLM - profileId: nim/mistralai/mixtral-8x7b-instruct-v01:h100x2-latency-fp8-4-l0a-rlkq framework: TensorRT-LLM displayName: Mixtral 8x7b Instruct V0.1 H100x2 FP8 Latency ngcMetadata: 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Distilled reasoning model excelling at math, science, and code tasks. Delivers state-of-the-art performance on complex reasoning benchmarks. requireLicense: true licenseAgreements: - label: Use Policy url: https://llama.meta.com/llama3/use-policy/ - label: License Agreement url: https://llama.meta.com/llama3/license/ modelVariants: - variantId: Deepseek R1 Distill Llama 70b modelCard: {
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    "createdDate": "2025-02-07T00:32:58.270Z",
    "description": "**Model Overview**\n\n## Description:\n\nDeepSeek-R1-Distill-Llama-70B is a distilled version of the DeepSeek-R1 series, built upon the Llama3.3-70B-Instruct architecture. This model is designed to deliver efficient performance for reasoning, math, and code tasks while maintaining high accuracy. By distilling knowledge from the larger DeepSeek-R1 model, it provides state-of-the-art performance with reduced computational requirements.\n\nThis model is ready for both research and commercial use.\nFor more details, visit the [DeepSeek website](https://www.deepseek.com/).\n\n## Third-Party Community Consideration\n\nThis model is not owned or developed by NVIDIA. This model has been developed and built to a third-party\u2019s requirements for this application and use case; see link to Non-NVIDIA [DeepSeek-R1 Model Card](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-70B).\n\n### License/Terms of Use\n\nGOVERNING TERMS: The NIM container is governed by the [NVIDIA Software License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-license-agreement/) and [Product-Specific Terms for AI Products](https://www.nvidia.com/en-us/agreements/enterprise-software/product-specific-terms-for-ai-products/); and the use of this model is governed by the [NVIDIA Community Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-community-models-license/). Additional Information: [MIT License](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/mit.md); [Meta Llama 3.3 Community License Agreement](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct/blob/main/LICENSE). Built with Llama.\n\n## References:\n\n- [DeepSeek GitHub Repository](https://github.com/deepseek-ai/DeepSeek-V3)\n- [DeepSeek-R1 Paper](https://arxiv.org/abs/2501.12948)\n- [Hugging Face Model Card for DeepSeek-R1-Distill-Llama-70B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-70B)\n\n## Model Architecture:\n\n**Architecture Type:** Distilled version of Mixture of Experts (MoE) <br>\n**Base Model:** Llama3.3-70B-Instruct\n\n## Input:\n\n**Input Type(s):** Text <br>\n**Input Format(s):** String <br>\n**Input Parameters:** (1D) <br>\n**Other Properties Related to Input:** <br>\nDeepSeek recommends adhering to the following configurations when utilizing the DeepSeek-R1 series models, including benchmarking, to achieve the expected performance:\n\n1. Set the temperature within the range of 0.5-0.7 (0.6 is recommended) to prevent endless repetitions or incoherent outputs.\n2. **Avoid adding a system prompt; all instructions should be contained within the user prompt.**\n3. For mathematical problems, it is advisable to include a directive in your prompt such as: \"Please reason step by step, and put your final answer within \\boxed{}.\"\n4. When evaluating model performance, it is recommended to conduct multiple tests and average the results.\n\n## Output:\n\n**Output Type(s):** Text <br>\n**Output Format:** String <br>\n**Output Parameters:** (1D) <br>\n\n## Software Integration:\n\n**Runtime Engine(s):** TensorRT-LLM <br>\n**Supported Hardware Microarchitecture Compatibility:** NVIDIA Ampere, NVIDIA Blackwell, NVIDIA Jetson, NVIDIA Hopper, NVIDIA Lovelace, NVIDIA Pascal, NVIDIA Turing, and NVIDIA Volta architectures <br>\n**[Preferred/Supported] Operating System(s):** Linux\n\n## Model Version(s):\n\nDeepSeek-R1-Distill-Llama-70B\n\n# Training, Testing, and Evaluation Datasets:\n\n## Training Dataset:\n\n**Data Collection Method by dataset:** Hybrid: Human, Automated <br>\n**Labeling Method by dataset:** Hybrid: Human, Automated <br>\n\n## Testing Dataset:\n\n**Data Collection Method by dataset:** Hybrid: Human, Automated <br>\n**Labeling Method by dataset:** Hybrid: Human, Automated <br>\n\n## Evaluation Dataset:\n\n**Data Collection Method by dataset:** Hybrid: Human, Automated <br>\n**Labeling Method by dataset:** Hybrid: Human, Automated <br>\n\n## Inference:\n\n**Engine:** TensorRT-LLM <br>\n**Test Hardware:** NVIDIA Hopper\n\n## Ethical Considerations:\n\nNVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).\n\n## Model Limitations:\nThe base model was trained on data that contains toxic language and societal biases originally crawled from the internet. Therefore, the model may amplify those biases and return toxic responses especially when prompted with toxic prompts. 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    "description": "**Model Overview**\n\n## Description:\n\nDeepSeek-R1-Distill-Llama-8B is a distilled version of the DeepSeek-R1 series, built upon the Llama3.1-8B-Instruct architecture. This model is designed to deliver efficient performance for reasoning, math, and code tasks while maintaining high accuracy. By distilling knowledge from the larger DeepSeek-R1 model, it provides state-of-the-art performance with reduced computational requirements.\n\nThis model is ready for both research and commercial use.\nFor more details, visit the [DeepSeek website](https://www.deepseek.com/).\n\n## Third-Party Community Consideration\n\nThis model is not owned or developed by NVIDIA. This model has been developed and built to a third-party\u2019s requirements for this application and use case; see link to Non-NVIDIA [DeepSeek-R1 Model Card](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-8B).\n\n### License/Terms of Use\n\nGOVERNING TERMS: The NIM container is governed by the [NVIDIA Software License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-license-agreement/) and [Product-Specific Terms for AI Products](https://www.nvidia.com/en-us/agreements/enterprise-software/product-specific-terms-for-ai-products/); and the use of this model is governed by the [NVIDIA Community Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-community-models-license/). Additional Information: [MIT License](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/mit.md); [Meta Llama 3.3 Community License Agreement](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct/blob/main/LICENSE). Built with Llama.\n\n## References:\n\n- [DeepSeek GitHub Repository](https://github.com/deepseek-ai/DeepSeek-V3)\n- [DeepSeek-R1 Paper](https://arxiv.org/abs/2501.12948)\n- [Hugging Face Model Card for DeepSeek-R1-Distill-Llama-8B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-8B)\n\n## Model Architecture:\n\n**Architecture Type:** Distilled version of Mixture of Experts (MoE) <br>\n**Base Model:** Llama3.1-8B-Instruct\n\n## Input:\n\n**Input Type(s):** Text <br>\n**Input Format(s):** String <br>\n**Input Parameters:** (1D) <br>\n**Other Properties Related to Input:** <br>\nDeepSeek recommends adhering to the following configurations when utilizing the DeepSeek-R1 series models, including benchmarking, to achieve the expected performance:\n\n1. Set the temperature within the range of 0.5-0.7 (0.6 is recommended) to prevent endless repetitions or incoherent outputs.\n2. **Avoid adding a system prompt; all instructions should be contained within the user prompt.**\n3. For mathematical problems, it is advisable to include a directive in your prompt such as: \"Please reason step by step, and put your final answer within \\boxed{}.\"\n4. When evaluating model performance, it is recommended to conduct multiple tests and average the results.\n\n## Output:\n\n**Output Type(s):** Text <br>\n**Output Format:** String <br>\n**Output Parameters:** (1D) <br>\n\n## Software Integration:\n\n**Runtime Engine(s):** TensorRT-LLM <br>\n**Supported Hardware Microarchitecture Compatibility:** NVIDIA Ampere, NVIDIA Blackwell, NVIDIA Jetson, NVIDIA Hopper, NVIDIA Lovelace, NVIDIA Pascal, NVIDIA Turing, and NVIDIA Volta architectures <br>\n**[Preferred/Supported] Operating System(s):** Linux\n\n## Model Version(s):\n\nDeepSeek-R1-Distill-Llama-8B\n\n# Training, Testing, and Evaluation Datasets:\n\n## Training Dataset:\n\n**Data Collection Method by dataset:** Hybrid: Human, Automated <br>\n**Labeling Method by dataset:** Hybrid: Human, Automated <br>\n\n## Testing Dataset:\n\n**Data Collection Method by dataset:** Hybrid: Human, Automated <br>\n**Labeling Method by dataset:** Hybrid: Human, Automated <br>\n\n## Evaluation Dataset:\n\n**Data Collection Method by dataset:** Hybrid: Human, Automated <br>\n**Labeling Method by dataset:** Hybrid: Human, Automated <br>\n\n## Inference:\n\n**Engine:** TensorRT-LLM <br>\n**Test Hardware:** NVIDIA Hopper\n\n## Ethical Considerations:\n\nNVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).\n\n## Model Limitations:\nThe base model was trained on data that contains toxic language and societal biases originally crawled from the internet. Therefore, the model may amplify those biases and return toxic responses especially when prompted with toxic prompts. The model may generate answers that may be inaccurate, omit key information, or include irrelevant or redundant text producing socially unacceptable or undesirable text, even if the prompt itself does not include anything explicitly offensive.\n\n**You are responsible for ensuring that your use of NVIDIA AI Foundation Models complies with all applicable laws.**",
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key: PROFILE value: LATENCY - key: PRECISION value: BF16 - key: GPU value: L40S - key: COUNT value: 2 - key: GPU DEVICE value: 26B9:10DE - key: NIM VERSION value: 1.5.2 - key: DOWNLOAD SIZE value: 17GB - profileId: nim/deepseek-ai/deepseek-r1-distill-llama-8b:a10gx2-throughput-bf16-uv8ptkf8-g framework: TensorRT-LLM displayName: Deepseek R1 Distill Llama 8B A10Gx2 BF16 Throughput ngcMetadata: edbb37d3ef94a5cc38919ab86694b835307c0668ca6d41ea746796b34ced78f1: model: deepseek-ai/deepseek-r1-distill-llama-8b release: 1.5.2 tags: feat_lora: 'false' gpu: A10G gpu_device: 2237:10de llm_engine: tensorrt_llm number_of_gpus: '2' pp: '1' precision: bf16 profile: throughput tp: '2' modelFormat: trt-llm spec: - key: PROFILE value: THROUGHPUT - key: PRECISION value: BF16 - key: GPU value: A10G - key: COUNT value: 2 - key: GPU DEVICE value: 2237:10DE - key: NIM VERSION value: 1.5.2 - key: DOWNLOAD SIZE value: 17GB labels: - Deepseek - Distill - Llama - Meta - Chat - Large Language Model - NVIDIA Validated config: architectures: - Other modelType: llama license: NVIDIA AI Foundation Models Community License - name: Llama 3.2 Instruct displayName: Llama 3.2 Instruct modelHubID: llama-3.2-instruct category: Language type: NGC description: Lightweight multilingual models optimized for dialogue, agentic retrieval, and summarization. Strong benchmark performance in a compact footprint. requireLicense: true licenseAgreements: - label: Use Policy url: https://llama.meta.com/llama3_2/use-policy/ - label: License Agreement url: https://llama.meta.com/llama3_2/license/ modelVariants: - variantId: Llama 3.2 1B Instruct modelCard: {
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    "description": "## Model Information\n\nThe Meta Llama 3.2 collection of multilingual large language models (LLMs) is a collection of pre-trained and instruction-tuned generative models in 1B and 3B sizes (text in/text out). The Llama 3.2 instruction-tuned text only models are optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks. They outperform many of the available open source and closed chat models on common industry benchmarks. \n\nLlama 3.2 models are ready for commercial use.\n\nModels are accelerated by [TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM), a library for optimizing Large Language Model (LLM) inference on NVIDIA GPUs.\n\n**Models in this Collection:**  \n- Llama-3.2-1B\n- Llama-3.2-1B-Instruct\n- Llama-3.2-3B\n- Llama-3.2-3B-Instruct\n\n**Model Developer:** Meta\n\n**Model Version:** 3.2\n\n**Model Release Date:** September 25, 2024\n\n**Third-Party Community Consideration:**\nThis model is not owned or developed by NVIDIA. This model has been developed and built to a third-party\u2019s requirements for this application and use case; see link to Non-NVIDIA [Llama 3.2 Model Card](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/MODEL_CARD.md).\n\n**License:** Use of Llama 3.2 is governed by the [Llama 3.2 Community License](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE) (a custom, commercial license agreement).\n\n**Model Architecture:** Llama 3.2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety. \n\n|  | Training Data | Params | Input modalities | Output modalities | Context Length | GQA | Shared Embeddings | Token count | Knowledge cutoff |\n| :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- |\n| Llama 3.2 (text only)  | A new mix of publicly available online data. | 1B (1.23B) | Multilingual Text | Multilingual Text and code  | 128k | Yes | Yes | Up to 9T tokens | December 2023 |\n|  |  | 3B (3.21B) | Multilingual Text | Multilingual Text and code  |  |  |  |  |  |\n\n**Supported Languages:** English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai are officially supported. Llama 3.2 has been trained on a broader collection of languages than these 8 supported languages. Developers may fine-tune Llama 3.2 models for languages beyond these supported languages, provided they comply with the Llama 3.2 Community License and the Acceptable Use Policy. Developers are always expected to ensure that their deployments, including those that involve additional languages, are completed safely and responsibly.\n\n**Llama 3.2 Model Family:** Token counts refer to pre-training data only. All model versions use Grouped-Query Attention (GQA) for improved inference scalability.\n\n**Status:** This is a static model trained on an offline dataset. Future versions may be released that improve model capabilities and safety. \n\n**Feedback:** Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model [README](https://github.com/meta-llama/llama-models/tree/main/models/llama3_2). For more technical information about generation parameters and recipes for how to use Llama 3.2 in applications, please go [here](https://github.com/meta-llama/llama-recipes). \n\n## Intended Use\n\n**Intended Use Cases:** Llama 3.2 is intended for commercial and research use in multiple languages. Instruction tuned text only models are intended for assistant-like chat and agentic applications like knowledge retrieval and summarization, mobile AI powered writing assistants and query and prompt rewriting. Pre-trained models can be adapted for a variety of additional natural language generation tasks. \n\n**Out of Scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3.2 Community License. Use in languages beyond those explicitly referenced as supported in this model card.\n\n## Software Integration\n\n**Supported Hardware Microarchitecture Compatibility:**\n- NVIDIA Ampere\n- NVIDIA Hopper\n- NVIDIA Lovelace\n- NVIDIA Jetson\n\n**Supported Operating System(s):**\n- Linux \n- Windows\n\n## Hardware and Software\n\n**Training Factors:** We used custom training libraries, Meta's custom built GPU cluster, and production infrastructure for pre-training. Fine-tuning, annotation, and evaluation were also performed on production infrastructure.\n\n**Training Energy Use:** Training utilized a cumulative of **916k** GPU hours of computation on H100-80GB (TDP of 700W) type hardware, per the table below. Training time is the total GPU time required for training each model and power consumption is the peak power capacity per GPU device used, adjusted for power usage efficiency. \n\n## \n\n**Training Greenhouse Gas Emissions:** Estimated total location-based greenhouse gas emissions were **240** tons CO2eq for training. Since 2020, Meta has maintained net zero greenhouse gas emissions in its global operations and matched 100% of its electricity use with renewable energy; therefore, the total market-based greenhouse gas emissions for training were 0 tons CO2eq.\n\n|  | Training Time (GPU hours) | Logit Generation Time (GPU Hours) | Training Power Consumption (W) | Training Location-Based Greenhouse Gas Emissions (tons CO2eq) | Training Market-Based Greenhouse Gas Emissions (tons CO2eq) |\n| :---- | :---: | ----- | :---: | :---: | :---: |\n| Llama-3.2-1B | 370k | \\- | 700 | 107 | 0 |\n| Llama-3.2-3B | 460k | \\- | 700 | 133 | 0 |\n| Total | 830k |         86k |  | 240 | 0 |\n\nThe methodology used to determine training energy use and greenhouse gas emissions can be found [here](https://arxiv.org/pdf/2204.05149). Since Meta is openly releasing these models, the training energy use and greenhouse gas emissions will not be incurred by others.\n\n## Training Data\n\n**Data Collection Method:** Unknown  \n**Labeling Method:** Unknown\n\n**Overview:** Llama 3.2 was pre-trained on up to 9 trillion tokens of data from publicly available sources. For the 1B and 3B Llama 3.2 models, we incorporated logits from the Llama 3.1 8B and 70B models into the pre-training stage of the model development, where outputs (logits) from these larger models were used as token-level targets. Knowledge distillation was used after pruning to recover performance. In post-training we used a similar recipe as Llama 3.1 and produced final chat models by doing several rounds of alignment on top of the pre-trained model. Each round involved Supervised Fine-Tuning (SFT), Rejection Sampling (RS), and Direct Preference Optimization (DPO).\n\n**Data Freshness:** The pre-training data has a cutoff of December 2023.\n\n## Benchmarks \\- English Text\n\nIn this section, we report the results for Llama 3.2 models on standard automatic benchmarks. For all these evaluations, we used our internal evaluations library. \n\n### Base Pre-trained Models \n\n| Category | Benchmark | \\# Shots | Metric | Llama-3.2-1B | Llama-3.2-3B | Llama-3.1-8B |\n| ----- | ----- | :---: | :---: | :---: | :---: | :---: |\n| General | MMLU | 5 | macro\\_avg/acc\\_char | 32.2 | 58 | 66.7 |\n|  | AGIEval English | 3-5 | average/acc\\_char | 23.3 | 39.2 | 47.8 |\n|  | ARC-Challenge | 25 | acc\\_char | 32.8 | 69.1 | 79.7 |\n| Reading comprehension | SQuAD | 1 | em | 49.2 | 67.7 | 77 |\n|  | QuAC (F1) | 1 | f1 | 37.9 | 42.9 | 44.9 |\n|  | DROP (F1) | 3 | f1 | 28.0 | 45.2 | 59.5 |\n| Long Context | Needle in Haystack | 0 | em | 96.8 | 1 | 1 |\n\n### Instruction-Tuned Models\n\n| Capability |  | Benchmark | \\# Shots | Metric | Llama-3.2-1B-Instruct | Llama-3.2-3B-Instruct | Llama-3.1-8B-Instruct |\n| :---: | ----- | :---: | :---: | :---: | :---: | :---: | :---: |\n| General |  | MMLU | 5 | macro\\_avg/acc | 49.3 | 63.4 | 69.4 |\n| Re-writing |  | Open-rewrite eval | 0 | micro\\_avg/rougeL | 41.6 | 40.1 | 40.9 |\n| Summarization |  | TLDR9+ (test) | 1 | rougeL | 16.8 | 19.0 | 17.2 |\n| Instruction following |  | IFEval | 0 | Avg(Prompt/Instruction acc Loose/Strict) | 59.5 | 77.4 | 80.4 |\n| Math |  | GSM8K (CoT) | 8 | em\\_maj1@1 | 44.4 | 77.7 | 84.5 |\n|  |  | MATH (CoT) | 0 | final\\_em | 30.6 | 48.0 | 51.9 |\n| Reasoning |  | ARC-C | 0 | acc | 59.4 | 78.6 | 83.4 |\n|  |  | GPQA | 0 | acc | 27.2 | 32.8 | 32.8 |\n|  |  | Hellaswag | 0 | acc | 41.2 | 69.8 | 78.7 |\n| Tool Use |  | BFCL V2 | 0 | acc | 25.7 | 67.0 | 67.1 |\n|  |  | Nexus | 0 | macro\\_avg/acc | 13.5 | 34.3 | 38.5 |\n| Long Context |  | InfiniteBench/En.QA | 0 | longbook\\_qa/f1 | 20.3 | 19.8 | 27.3 |\n|  |  | InfiniteBench/En.MC | 0 | longbook\\_choice/acc | 38.0 | 63.3 | 72.2 |\n|  |  | NIH/Multi-needle | 0 | recall | 75.0 | 84.7 | 98.8 |\n| Multilingual |  | MGSM (CoT) | 0 | em | 24.5 | 58.2 | 68.9 |\n\n### Multilingual Benchmarks\n\n| Category | Benchmark | Language | Llama-3.2-1B-Instruct | Llama-3.2-3B-Instruct | Llama-3.1-8B-Instruct |\n| :---: | :---: | :---: | :---: | :---: | :---: |\n| General | MMLU (5-shot, macro\\_avg/acc) | Portuguese | 39.82 | 54.48 | 62.12 |\n|  |  | Spanish | 41.52 | 55.09 | 62.45 |\n|  |  | Italian | 39.79 | 53.77 | 61.63 |\n|  |  | German | 39.20 | 53.29 | 60.59 |\n|  |  | French | 40.47 | 54.59 | 62.34 |\n|  |  | Hindi | 33.51 | 43.31 | 50.88 |\n|  |  | Thai | 34.67 | 44.54 | 50.32 |\n\n\n## Responsibility & Safety\n\nAs part of our Responsible release approach, we followed a three-pronged strategy to managing trust & safety risks:\n\n1. Enable developers to deploy helpful, safe and flexible experiences for their target audience and for the use cases supported by Llama   \n2. Protect developers against adversarial users aiming to exploit Llama capabilities to potentially cause harm  \n3. Provide protections for the community to help prevent the misuse of our models\n\n### Responsible Deployment \n\n**Approach:** Llama is a foundational technology designed to be used in a variety of use cases. Examples on how Meta\u2019s Llama models have been responsibly deployed can be found in our [Community Stories webpage](https://llama.meta.com/community-stories/). Our approach is to build the most helpful models, enabling the world to benefit from the technology power, by aligning our model safety for generic use cases and addressing a standard set of harms. Developers are then in the driver\u2019s seat to tailor safety for their use cases, defining their own policies and deploying the models with the necessary safeguards in their Llama systems. Llama 3.2 was developed following the best practices outlined in our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/). \n\n#### Llama 3.2 Instruct \n\n**Objective:** Our main objectives for conducting safety fine-tuning are to provide the research community with a valuable resource for studying the robustness of safety fine-tuning, as well as to offer developers a readily available, safe, and powerful model for various applications to reduce the developer workload to deploy safe AI systems. We implemented the same set of safety mitigations as in Llama 3, and you can learn more about these in the Llama 3 [paper](https://ai.meta.com/research/publications/the-llama-3-herd-of-models/). \n\n**Fine-Tuning Data:** We employ a multi-faceted approach to data collection, combining human-generated data from our vendors with synthetic data to mitigate potential safety risks. We\u2019ve developed many large language model (LLM)-based classifiers that enable us to thoughtfully select high-quality prompts and responses, enhancing data quality control. \n\n**Refusals and Tone:** Building on the work we started with Llama 3, we put a great emphasis on model refusals to benign prompts as well as refusal tone. We included both borderline and adversarial prompts in our safety data strategy, and modified our safety data responses to follow tone guidelines. \n\n#### Llama 3.2 Systems\n\n**Safety as a System:** Large language models, including Llama 3.2, **are not designed to be deployed in isolation** but instead should be deployed as part of an overall AI system with additional safety guardrails as required. Developers are expected to deploy system safeguards when building agentic systems. Safeguards are key to achieve the right helpfulness-safety alignment as well as mitigating safety and security risks inherent to the system and any integration of the model or system with external tools. As part of our responsible release approach, we provide the community with [safeguards](https://llama.meta.com/trust-and-safety/) that developers should deploy with Llama models or other LLMs, including Llama Guard, Prompt Guard and Code Shield. All our [reference implementations](https://github.com/meta-llama/llama-agentic-system) demos contain these safeguards by default so developers can benefit from system-level safety out-of-the-box. \n\n### New Capabilities and Use Cases\n\n**Technological Advancement:** Llama releases usually introduce new capabilities that require specific considerations in addition to the best practices that generally apply across all Generative AI use cases. For prior release capabilities also supported by Llama 3.2, see [Llama 3.1 Model Card](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/MODEL_CARD.md), as the same considerations apply here as well.\n\n**Constrained Environments:** Llama 3.2 1B and 3B models are expected to be deployed in highly constrained environments, such as mobile devices. LLM Systems using smaller models will have a different alignment profile and safety/helpfulness tradeoff than more complex, larger systems. Developers should ensure the safety of their system meets the requirements of their use case. We recommend using lighter system safeguards for such use cases, like Llama Guard 3-1B or its mobile-optimized version. \n\n### Evaluations\n\n**Scaled Evaluations:** We built dedicated, adversarial evaluation datasets and evaluated systems composed of Llama models and Purple Llama safeguards to filter input prompt and output response. It is important to evaluate applications in context, and we recommend building dedicated evaluation dataset for your use case.\n\n**Red Teaming:** We conducted recurring red teaming exercises with the goal of discovering risks via adversarial prompting and we used the learnings to improve our benchmarks and safety tuning datasets. We partnered early with subject-matter experts in critical risk areas to understand the nature of these real-world harms and how such models may lead to unintended harm for society. Based on these conversations, we derived a set of adversarial goals for the red team to attempt to achieve, such as extracting harmful information or reprogramming the model to act in a potentially harmful capacity. The red team consisted of experts in cybersecurity, adversarial machine learning, responsible AI, and integrity in addition to multilingual content specialists with background in integrity issues in specific geographic markets.\n\n### Critical Risks \n\nIn addition to our safety work above, we took extra care on measuring and/or mitigating the following critical risk areas:\n\n**1\\. CBRNE (Chemical, Biological, Radiological, Nuclear, and Explosive Weapons):** Llama 3.2 1B and 3B models are smaller and less capable derivatives of Llama 3.1. For Llama 3.1 70B and 405B, to assess risks related to proliferation of chemical and biological weapons, we performed uplift testing designed to assess whether use of Llama 3.1 models could meaningfully increase the capabilities of malicious actors to plan or carry out attacks using these types of weapons and have determined that such testing also applies to the smaller 1B and 3B models. \n\n**2\\. Child Safety:** Child Safety risk assessments were conducted using a team of experts, to assess the model\u2019s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors including the additional languages Llama 3 is trained on. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences. \n\n**3\\. Cyber Attacks:** Our cyber attack uplift study investigated whether LLMs can enhance human capabilities in hacking tasks, both in terms of skill level and speed. Our attack automation study focused on evaluating the capabilities of LLMs when used as autonomous agents in cyber offensive operations, specifically in the context of ransomware attacks. This evaluation was distinct from previous studies that considered LLMs as interactive assistants. The primary objective was to assess whether these models could effectively function as independent agents in executing complex cyber-attacks without human intervention.\n\n### Community \n\n**Industry Partnerships:** Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership on AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama).\n\n**Grants:** We also set up the [Llama Impact Grants](https://llama.meta.com/llama-impact-grants/) program to identify and support the most compelling applications of Meta\u2019s Llama model for societal benefit across three categories: education, climate and open innovation. The 20 finalists from the hundreds of applications can be found [here](https://llama.meta.com/llama-impact-grants/#finalists). \n\n**Reporting:** Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community.\n\n## Ethical Considerations and Limitations\nNVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications.  When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.\nPlease report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).\n\n**Values:** The core values of Llama 3.2 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3.2 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress. \n\n**Testing:** Llama 3.2 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3.2\u2019s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3.2 models, developers should perform safety testing and tuning tailored to their specific applications of the model. Please refer to available resources including our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide), [Trust and Safety](https://llama.meta.com/trust-and-safety/) solutions, and other [resources](https://llama.meta.com/docs/get-started/) to learn more about responsible development.\n\n**You are responsible for ensuring that your use of NVIDIA AI Foundation Models complies with all applicable laws.**",
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key: PROFILE value: THROUGHPUT - key: PRECISION value: BF16 - key: GPU value: H100 - key: COUNT value: 1 - key: GPU DEVICE value: 2330:10DE - key: NIM VERSION value: 1.12.0 - key: DOWNLOAD SIZE value: 3GB - key: LLM ENGINE value: TENSORRT_LLM - profileId: nim/meta/llama-3.2-1b-instruct:h200x1-throughput-fp8-q4ene2avnw framework: TensorRT-LLM displayName: Llama 3.2 1B Instruct H200x1 FP8 Throughput ngcMetadata: 0b900e8d26b11d548f74a903739434bf00fc990439a9245042e344d253481719: model: meta/llama-3.2-1b-instruct release: 1.12.0 tags: feat_lora: 'false' gpu: H200 gpu_device: 2335:10de llm_engine: tensorrt_llm nim_workspace_hash_v1: b771a0a1bf21ee92364a0f1c9db64628d74919517edf09f47b079aab90af963e number_of_gpus: '2' pp: '1' precision: fp8 profile: throughput tp: '1' modelFormat: trt-llm spec: - key: PROFILE value: THROUGHPUT - key: PRECISION value: FP8 - key: GPU value: H200 - key: COUNT value: 1 - key: GPU DEVICE value: 2335:10DE - key: NIM VERSION value: 1.12.0 - key: DOWNLOAD SIZE value: 2GB - 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profileId: nim/meta/llama-3.2-1b-instruct:a10gx2-latency-bf16-sdcegxqefa framework: TensorRT-LLM displayName: Llama 3.2 1B Instruct A10Gx2 BF16 Latency ngcMetadata: 41eb6cd432c8d498926942101511e4da1e913d0d22adbc96ed547a8042d2b7ce: model: meta/llama-3.2-1b-instruct release: 1.12.0 tags: feat_lora: 'false' gpu: A10G gpu_device: 2237:10de llm_engine: tensorrt_llm nim_workspace_hash_v1: 071daef17e33c140ef8b82b89004ed3d5412e3eca9b4bedb8b57824dc05e975c number_of_gpus: '2' pp: '1' precision: bf16 profile: latency tp: '2' modelFormat: trt-llm spec: - key: PROFILE value: LATENCY - key: PRECISION value: BF16 - key: GPU value: A10G - key: COUNT value: 2 - key: GPU DEVICE value: 2237:10DE - key: NIM VERSION value: 1.12.0 - key: DOWNLOAD SIZE value: 4GB - key: LLM ENGINE value: TENSORRT_LLM - profileId: nim/meta/llama-3.2-1b-instruct:hf-9213176-tool_calling__10 framework: TensorRT-LLM displayName: Llama 3.2 1B Instruct A100_SXM4_40GBx2 BF16 Latency ngcMetadata: 43a51be16bada864c0ab6acc3e267e333fe69150a05287d5386e7cb39c7c61bb: model: meta/llama-3.2-1b-instruct release: 1.12.0 tags: feat_lora: 'false' gpu: A100_SXM4_40GB gpu_device: 20b0:10de llm_engine: tensorrt_llm nim_workspace_hash_v1: 04ee232eb94574469d111ff8001c42836ee881f67fbf9040fbdc582e6b1b1c42 number_of_gpus: '2' pp: '1' precision: bf16 profile: latency tp: '2' modelFormat: trt-llm spec: - 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key: LLM ENGINE value: TENSORRT_LLM - profileId: nim/meta/llama-3.2-1b-instruct:hf-9213176-tool_calling__12 framework: TensorRT-LLM displayName: Llama 3.2 1B Instruct A100_SXM4_40GBx1 BF16 Throughput ngcMetadata: 4acbbc32a700f17dd483e6a53914ec62688d029120fb0c216420e8481983d0e7: model: meta/llama-3.2-1b-instruct release: 1.12.0 tags: feat_lora: 'false' gpu: A100_SXM4_40GB gpu_device: 20b0:10de llm_engine: tensorrt_llm nim_workspace_hash_v1: 10849901781866ddf59b10df9f42464a7c089c5f0a61f41f6b25862f19195a7b number_of_gpus: '1' pp: '1' precision: bf16 profile: throughput tp: '1' modelFormat: trt-llm spec: - key: PROFILE value: THROUGHPUT - key: PRECISION value: BF16 - key: GPU value: A100_SXM4_40GB - key: COUNT value: 1 - key: GPU DEVICE value: 20B0:10DE - key: NIM VERSION value: 1.12.0 - key: DOWNLOAD SIZE value: 5GB - key: LLM ENGINE value: TENSORRT_LLM - profileId: nim/meta/llama-3.2-1b-instruct:l40sx2-latency-fp8-uvobdo54ig framework: TensorRT-LLM displayName: Llama 3.2 1B Instruct L40Sx2 FP8 Latency ngcMetadata: 55be74aa57225eb45db56bda45a2e1ad7a02f8f30d5f8eef9877df8adacc0550: model: meta/llama-3.2-1b-instruct release: 1.12.0 tags: feat_lora: 'false' gpu: L40S gpu_device: 26b9:10de llm_engine: tensorrt_llm nim_workspace_hash_v1: 0669a60d949ab85b17b5f2a73d7e9f6b131797740da6ed43e29e5f41066d571c number_of_gpus: '4' pp: '1' precision: fp8 profile: latency tp: '2' modelFormat: trt-llm spec: - 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profileId: nim/meta/llama-3.2-1b-instruct:hf-9213176-tool_calling__17 framework: TensorRT-LLM displayName: Llama 3.2 1B Instruct GH200_144GBx1 FP8 Throughput ngcMetadata: b4375abbe106a961f61ebfc40ecb73490ca64fadba7b06c156173aeec81ed2fa: model: meta/llama-3.2-1b-instruct release: 1.12.0 tags: feat_lora: 'false' gpu: GH200_144GB gpu_device: 2348:10de llm_engine: tensorrt_llm nim_workspace_hash_v1: 94f9fdc707b068711db2447004698e4a90be58c6d3329463ba57c85714bff488 number_of_gpus: '2' pp: '1' precision: fp8 profile: throughput tp: '1' modelFormat: trt-llm spec: - key: PROFILE value: THROUGHPUT - key: PRECISION value: FP8 - key: GPU value: GH200_144GB - key: COUNT value: 1 - key: GPU DEVICE value: 2348:10DE - key: NIM VERSION value: 1.12.0 - key: DOWNLOAD SIZE value: 5GB - key: LLM ENGINE value: TENSORRT_LLM - profileId: nim/meta/llama-3.2-1b-instruct:b200x2-latency-bf16-jvvom3lafg framework: TensorRT-LLM displayName: Llama 3.2 1B Instruct B200x2 BF16 Latency ngcMetadata: bb8774e429cd06145c3af972a557193da4859bbb406bdd6ab4eba1111b757ee4: model: meta/llama-3.2-1b-instruct release: 1.12.0 tags: feat_lora: 'false' gpu: B200 gpu_device: 2901:10de llm_engine: tensorrt_llm nim_workspace_hash_v1: 417053eecdaf868c0db37d64920e90eb29881638168c9781c33309dabd9852c8 number_of_gpus: '2' pp: '1' precision: bf16 profile: latency tp: '2' modelFormat: trt-llm spec: - 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    "description": "## Model Information\n\nThe Meta Llama 3.2 collection of multilingual large language models (LLMs) is a collection of pre-trained and instruction-tuned generative models in 1B and 3B sizes (text in/text out). The Llama 3.2 instruction-tuned text only models are optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks. They outperform many of the available open source and closed chat models on common industry benchmarks. \n\nLlama 3.2 models are ready for commercial use.\n\nModels are accelerated by [TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM), a library for optimizing Large Language Model (LLM) inference on NVIDIA GPUs.\n\n**Models in this Collection:**  \n- Llama-3.2-1B\n- Llama-3.2-1B-Instruct\n- Llama-3.2-3B\n- Llama-3.2-3B-Instruct\n\n**Model Developer:** Meta\n\n**Model Version:** 3.2\n\n**Model Release Date:** September 25, 2024\n\n**Third-Party Community Consideration:**\nThis model is not owned or developed by NVIDIA. This model has been developed and built to a third-party\u2019s requirements for this application and use case; see link to Non-NVIDIA [Llama 3.2 Model Card](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/MODEL_CARD.md).\n\n**License:** Use of Llama 3.2 is governed by the [Llama 3.2 Community License](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE) (a custom, commercial license agreement).\n\n**Model Architecture:** Llama 3.2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.\n\n|  | Training Data | Params | Input modalities | Output modalities | Context Length | GQA | Shared Embeddings | Token count | Knowledge cutoff |\n| :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- |\n| Llama 3.2 (text only)  | A new mix of publicly available online data. | 1B (1.23B) | Multilingual Text | Multilingual Text and code  | 128k | Yes | Yes | Up to 9T tokens | December 2023 |\n|  |  | 3B (3.21B) | Multilingual Text | Multilingual Text and code  |  |  |  |  |  |\n\n**Supported Languages:** English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai are officially supported. Llama 3.2 has been trained on a broader collection of languages than these 8 supported languages. Developers may fine-tune Llama 3.2 models for languages beyond these supported languages, provided they comply with the Llama 3.2 Community License and the Acceptable Use Policy. Developers are always expected to ensure that their deployments, including those that involve additional languages, are completed safely and responsibly.\n\n**Llama 3.2 Model Family:** Token counts refer to pre-training data only. All model versions use Grouped-Query Attention (GQA) for improved inference scalability.\n\n**Status:** This is a static model trained on an offline dataset. Future versions may be released that improve model capabilities and safety. \n\n**Feedback:** Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model [README](https://github.com/meta-llama/llama-models/tree/main/models/llama3_2). For more technical information about generation parameters and recipes for how to use Llama 3.2 in applications, please go [here](https://github.com/meta-llama/llama-recipes). \n\n## Intended Use\n\n**Intended Use Cases:** Llama 3.2 is intended for commercial and research use in multiple languages. Instruction tuned text only models are intended for assistant-like chat and agentic applications like knowledge retrieval and summarization, mobile AI powered writing assistants and query and prompt rewriting. Pre-trained models can be adapted for a variety of additional natural language generation tasks. \n\n**Out of Scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3.2 Community License. Use in languages beyond those explicitly referenced as supported in this model card.\n\n## Software Integration\n\n**Supported Hardware Microarchitecture Compatibility:**\n- NVIDIA Ampere\n- NVIDIA Hopper\n- NVIDIA Lovelace\n- NVIDIA Jetson\n\n**Supported Operating System(s):**\n- Linux \n- Windows\n\n## Hardware and Software\n\n**Training Factors:** We used custom training libraries, Meta's custom built GPU cluster, and production infrastructure for pre-training. Fine-tuning, annotation, and evaluation were also performed on production infrastructure.\n\n**Training Energy Use:** Training utilized a cumulative of **916k** GPU hours of computation on H100-80GB (TDP of 700W) type hardware, per the table below. Training time is the total GPU time required for training each model and power consumption is the peak power capacity per GPU device used, adjusted for power usage efficiency. \n\n## \n\n**Training Greenhouse Gas Emissions:** Estimated total location-based greenhouse gas emissions were **240** tons CO2eq for training. Since 2020, Meta has maintained net zero greenhouse gas emissions in its global operations and matched 100% of its electricity use with renewable energy; therefore, the total market-based greenhouse gas emissions for training were 0 tons CO2eq.\n\n|  | Training Time (GPU hours) | Logit Generation Time (GPU Hours) | Training Power Consumption (W) | Training Location-Based Greenhouse Gas Emissions (tons CO2eq) | Training Market-Based Greenhouse Gas Emissions (tons CO2eq) |\n| :---- | :---: | ----- | :---: | :---: | :---: |\n| Llama-3.2-1B | 370k | \\- | 700 | 107 | 0 |\n| Llama-3.2-3B | 460k | \\- | 700 | 133 | 0 |\n| Total | 830k |         86k |  | 240 | 0 |\n\nThe methodology used to determine training energy use and greenhouse gas emissions can be found [here](https://arxiv.org/pdf/2204.05149). Since Meta is openly releasing these models, the training energy use and greenhouse gas emissions will not be incurred by others.\n\n## Training Data\n\n**Data Collection Method:** Unknown  \n**Labeling Method:** Unknown\n\n**Overview:** Llama 3.2 was pre-trained on up to 9 trillion tokens of data from publicly available sources. For the 1B and 3B Llama 3.2 models, we incorporated logits from the Llama 3.1 8B and 70B models into the pre-training stage of the model development, where outputs (logits) from these larger models were used as token-level targets. Knowledge distillation was used after pruning to recover performance. In post-training we used a similar recipe as Llama 3.1 and produced final chat models by doing several rounds of alignment on top of the pre-trained model. Each round involved Supervised Fine-Tuning (SFT), Rejection Sampling (RS), and Direct Preference Optimization (DPO).\n\n**Data Freshness:** The pre-training data has a cutoff of December 2023.\n\n## Benchmarks \\- English Text\n\nIn this section, we report the results for Llama 3.2 models on standard automatic benchmarks. For all these evaluations, we used our internal evaluations library. \n\n### Base Pre-trained Models \n\n| Category | Benchmark | \\# Shots | Metric | Llama-3.2-1B | Llama-3.2-3B | Llama-3.1-8B |\n| ----- | ----- | :---: | :---: | :---: | :---: | :---: |\n| General | MMLU | 5 | macro\\_avg/acc\\_char | 32.2 | 58 | 66.7 |\n|  | AGIEval English | 3-5 | average/acc\\_char | 23.3 | 39.2 | 47.8 |\n|  | ARC-Challenge | 25 | acc\\_char | 32.8 | 69.1 | 79.7 |\n| Reading comprehension | SQuAD | 1 | em | 49.2 | 67.7 | 77 |\n|  | QuAC (F1) | 1 | f1 | 37.9 | 42.9 | 44.9 |\n|  | DROP (F1) | 3 | f1 | 28.0 | 45.2 | 59.5 |\n| Long Context | Needle in Haystack | 0 | em | 96.8 | 1 | 1 |\n\n### Instruction-Tuned Models\n\n| Capability |  | Benchmark | \\# Shots | Metric | Llama-3.2-1B-Instruct | Llama-3.2-3B-Instruct | Llama-3.1-8B-Instruct |\n| :---: | ----- | :---: | :---: | :---: | :---: | :---: | :---: |\n| General |  | MMLU | 5 | macro\\_avg/acc | 49.3 | 63.4 | 69.4 |\n| Re-writing |  | Open-rewrite eval | 0 | micro\\_avg/rougeL | 41.6 | 40.1 | 40.9 |\n| Summarization |  | TLDR9+ (test) | 1 | rougeL | 16.8 | 19.0 | 17.2 |\n| Instruction following |  | IFEval | 0 | Avg(Prompt/Instruction acc Loose/Strict) | 59.5 | 77.4 | 80.4 |\n| Math |  | GSM8K (CoT) | 8 | em\\_maj1@1 | 44.4 | 77.7 | 84.5 |\n|  |  | MATH (CoT) | 0 | final\\_em | 30.6 | 48.0 | 51.9 |\n| Reasoning |  | ARC-C | 0 | acc | 59.4 | 78.6 | 83.4 |\n|  |  | GPQA | 0 | acc | 27.2 | 32.8 | 32.8 |\n|  |  | Hellaswag | 0 | acc | 41.2 | 69.8 | 78.7 |\n| Tool Use |  | BFCL V2 | 0 | acc | 25.7 | 67.0 | 67.1 |\n|  |  | Nexus | 0 | macro\\_avg/acc | 13.5 | 34.3 | 38.5 |\n| Long Context |  | InfiniteBench/En.QA | 0 | longbook\\_qa/f1 | 20.3 | 19.8 | 27.3 |\n|  |  | InfiniteBench/En.MC | 0 | longbook\\_choice/acc | 38.0 | 63.3 | 72.2 |\n|  |  | NIH/Multi-needle | 0 | recall | 75.0 | 84.7 | 98.8 |\n| Multilingual |  | MGSM (CoT) | 0 | em | 24.5 | 58.2 | 68.9 |\n\n### Multilingual Benchmarks\n\n| Category | Benchmark | Language | Llama-3.2-1B-Instruct | Llama-3.2-3B-Instruct | Llama-3.1-8B-Instruct |\n| :---: | :---: | :---: | :---: | :---: | :---: |\n| General | MMLU (5-shot, macro\\_avg/acc) | Portuguese | 39.82 | 54.48 | 62.12 |\n|  |  | Spanish | 41.52 | 55.09 | 62.45 |\n|  |  | Italian | 39.79 | 53.77 | 61.63 |\n|  |  | German | 39.20 | 53.29 | 60.59 |\n|  |  | French | 40.47 | 54.59 | 62.34 |\n|  |  | Hindi | 33.51 | 43.31 | 50.88 |\n|  |  | Thai | 34.67 | 44.54 | 50.32 |\n\n## Responsibility & Safety\n\nAs part of our Responsible release approach, we followed a three-pronged strategy to managing trust & safety risks:\n\n1. Enable developers to deploy helpful, safe and flexible experiences for their target audience and for the use cases supported by Llama   \n2. Protect developers against adversarial users aiming to exploit Llama capabilities to potentially cause harm  \n3. Provide protections for the community to help prevent the misuse of our models\n\n### Responsible Deployment \n\n**Approach:** Llama is a foundational technology designed to be used in a variety of use cases. Examples on how Meta\u2019s Llama models have been responsibly deployed can be found in our [Community Stories webpage](https://llama.meta.com/community-stories/). Our approach is to build the most helpful models, enabling the world to benefit from the technology power, by aligning our model safety for generic use cases and addressing a standard set of harms. Developers are then in the driver\u2019s seat to tailor safety for their use cases, defining their own policies and deploying the models with the necessary safeguards in their Llama systems. Llama 3.2 was developed following the best practices outlined in our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/). \n\n#### Llama 3.2 Instruct \n\n**Objective:** Our main objectives for conducting safety fine-tuning are to provide the research community with a valuable resource for studying the robustness of safety fine-tuning, as well as to offer developers a readily available, safe, and powerful model for various applications to reduce the developer workload to deploy safe AI systems. We implemented the same set of safety mitigations as in Llama 3, and you can learn more about these in the Llama 3 [paper](https://ai.meta.com/research/publications/the-llama-3-herd-of-models/). \n\n**Fine-Tuning Data:** We employ a multi-faceted approach to data collection, combining human-generated data from our vendors with synthetic data to mitigate potential safety risks. We\u2019ve developed many large language model (LLM)-based classifiers that enable us to thoughtfully select high-quality prompts and responses, enhancing data quality control. \n\n**Refusals and Tone:** Building on the work we started with Llama 3, we put a great emphasis on model refusals to benign prompts as well as refusal tone. We included both borderline and adversarial prompts in our safety data strategy, and modified our safety data responses to follow tone guidelines. \n\n#### Llama 3.2 Systems\n\n**Safety as a System:** Large language models, including Llama 3.2, **are not designed to be deployed in isolation** but instead should be deployed as part of an overall AI system with additional safety guardrails as required. Developers are expected to deploy system safeguards when building agentic systems. Safeguards are key to achieve the right helpfulness-safety alignment as well as mitigating safety and security risks inherent to the system and any integration of the model or system with external tools. As part of our responsible release approach, we provide the community with [safeguards](https://llama.meta.com/trust-and-safety/) that developers should deploy with Llama models or other LLMs, including Llama Guard, Prompt Guard and Code Shield. All our [reference implementations](https://github.com/meta-llama/llama-agentic-system) demos contain these safeguards by default so developers can benefit from system-level safety out-of-the-box. \n\n### New Capabilities and Use Cases\n\n**Technological Advancement:** Llama releases usually introduce new capabilities that require specific considerations in addition to the best practices that generally apply across all Generative AI use cases. For prior release capabilities also supported by Llama 3.2, see [Llama 3.1 Model Card](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/MODEL_CARD.md), as the same considerations apply here as well.\n\n**Constrained Environments:** Llama 3.2 1B and 3B models are expected to be deployed in highly constrained environments, such as mobile devices. LLM Systems using smaller models will have a different alignment profile and safety/helpfulness tradeoff than more complex, larger systems. Developers should ensure the safety of their system meets the requirements of their use case. We recommend using lighter system safeguards for such use cases, like Llama Guard 3-1B or its mobile-optimized version. \n\n### Evaluations\n\n**Scaled Evaluations:** We built dedicated, adversarial evaluation datasets and evaluated systems composed of Llama models and Purple Llama safeguards to filter input prompt and output response. It is important to evaluate applications in context, and we recommend building dedicated evaluation dataset for your use case.\n\n**Red Teaming:** We conducted recurring red teaming exercises with the goal of discovering risks via adversarial prompting and we used the learnings to improve our benchmarks and safety tuning datasets. We partnered early with subject-matter experts in critical risk areas to understand the nature of these real-world harms and how such models may lead to unintended harm for society. Based on these conversations, we derived a set of adversarial goals for the red team to attempt to achieve, such as extracting harmful information or reprogramming the model to act in a potentially harmful capacity. The red team consisted of experts in cybersecurity, adversarial machine learning, responsible AI, and integrity in addition to multilingual content specialists with background in integrity issues in specific geographic markets.\n\n### Critical Risks \n\nIn addition to our safety work above, we took extra care on measuring and/or mitigating the following critical risk areas:\n\n**1\\. CBRNE (Chemical, Biological, Radiological, Nuclear, and Explosive Weapons):** Llama 3.2 1B and 3B models are smaller and less capable derivatives of Llama 3.1. For Llama 3.1 70B and 405B, to assess risks related to proliferation of chemical and biological weapons, we performed uplift testing designed to assess whether use of Llama 3.1 models could meaningfully increase the capabilities of malicious actors to plan or carry out attacks using these types of weapons and have determined that such testing also applies to the smaller 1B and 3B models. \n\n**2\\. Child Safety:** Child Safety risk assessments were conducted using a team of experts, to assess the model\u2019s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors including the additional languages Llama 3 is trained on. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences. \n\n**3\\. Cyber Attacks:** Our cyber attack uplift study investigated whether LLMs can enhance human capabilities in hacking tasks, both in terms of skill level and speed. Our attack automation study focused on evaluating the capabilities of LLMs when used as autonomous agents in cyber offensive operations, specifically in the context of ransomware attacks. This evaluation was distinct from previous studies that considered LLMs as interactive assistants. The primary objective was to assess whether these models could effectively function as independent agents in executing complex cyber-attacks without human intervention.\n\n### Community \n\n**Industry Partnerships:** Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership on AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama).\n\n**Grants:** We also set up the [Llama Impact Grants](https://llama.meta.com/llama-impact-grants/) program to identify and support the most compelling applications of Meta\u2019s Llama model for societal benefit across three categories: education, climate and open innovation. The 20 finalists from the hundreds of applications can be found [here](https://llama.meta.com/llama-impact-grants/#finalists). \n\n**Reporting:** Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community.\n\n## Ethical Considerations and Limitations\nNVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications.  When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.\nPlease report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).\n\n**Values:** The core values of Llama 3.2 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3.2 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress. \n\n**Testing:** Llama 3.2 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3.2\u2019s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3.2 models, developers should perform safety testing and tuning tailored to their specific applications of the model. Please refer to available resources including our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide), [Trust and Safety](https://llama.meta.com/trust-and-safety/) solutions, and other [resources](https://llama.meta.com/docs/get-started/) to learn more about responsible development.\n\n**You are responsible for ensuring that your use of NVIDIA AI Foundation Models complies with all applicable laws.**",
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key: PROFILE value: THROUGHPUT - key: PRECISION value: FP8 - key: GPU value: RTX6000_ADA - key: COUNT value: 1 - key: GPU DEVICE value: 26B1:10DE - key: NIM VERSION value: 1.10.1 - key: DOWNLOAD SIZE value: 12GB - key: LLM ENGINE value: TENSORRT_LLM - profileId: nim/meta/llama-3.2-3b-instruct:h20x1-throughput-fp8-bmppgnfoeq framework: TensorRT-LLM displayName: Llama 3.2 3B Instruct H20x1 FP8 Throughput ngcMetadata: 86be215b815363c818c00883dd403bd1f4ce5c610037637529a2a7e039973de6: model: meta/llama-3.2-3b-instruct release: 1.10.1 tags: feat_lora: 'false' gpu: H20 gpu_device: 2329:10de llm_engine: tensorrt_llm number_of_gpus: '1' pp: '1' precision: fp8 profile: throughput tp: '1' modelFormat: trt-llm spec: - key: PROFILE value: THROUGHPUT - key: PRECISION value: FP8 - key: GPU value: H20 - key: COUNT value: 1 - key: GPU DEVICE value: 2329:10DE - key: NIM VERSION value: 1.10.1 - key: DOWNLOAD SIZE value: 5GB - key: LLM ENGINE value: TENSORRT_LLM - profileId: nim/meta/llama-3.2-3b-instruct:b200x1-throughput-fp8-pysymm95jq framework: TensorRT-LLM displayName: Llama 3.2 3B Instruct B200x1 FP8 Throughput ngcMetadata: 8b87146e39b0305ae1d73bc053564d1b4b4c565f81aa5abe3e84385544ca9b60: model: meta/llama-3.2-3b-instruct release: 1.10.1 tags: feat_lora: 'false' gpu: B200 gpu_device: 2901:10de llm_engine: tensorrt_llm number_of_gpus: '1' pp: '1' precision: fp8 profile: throughput tp: '1' modelFormat: trt-llm spec: - key: PROFILE value: THROUGHPUT - key: PRECISION value: FP8 - key: GPU value: B200 - key: COUNT value: 1 - key: GPU DEVICE value: 2901:10DE - key: NIM VERSION value: 1.10.1 - key: DOWNLOAD SIZE value: 5GB - key: LLM ENGINE value: TENSORRT_LLM - profileId: nim/meta/llama-3.2-3b-instruct:l20x1-throughput-bf16-rpqq5ggd-q framework: TensorRT-LLM displayName: Llama 3.2 3B Instruct L20x1 BF16 Throughput ngcMetadata: 91c52b108cd75967df6ed98f3d1d73a34cb0899d625f6f86499089f545ebe458: model: meta/llama-3.2-3b-instruct release: 1.10.1 tags: feat_lora: 'false' gpu: L20 gpu_device: 26ba:10de llm_engine: tensorrt_llm number_of_gpus: '1' pp: '1' precision: bf16 profile: throughput tp: '1' modelFormat: trt-llm spec: - key: PROFILE value: THROUGHPUT - key: PRECISION value: BF16 - key: GPU value: L20 - key: COUNT value: 1 - key: GPU DEVICE value: 26BA:10DE - key: NIM VERSION value: 1.10.1 - key: DOWNLOAD SIZE value: 7GB - key: LLM ENGINE value: TENSORRT_LLM - profileId: nim/meta/llama-3.2-3b-instruct:hf-392a143-tool-use-v2__6 framework: TensorRT-LLM displayName: Llama 3.2 3B Instruct H100_NVLx2 FP8 Latency ngcMetadata: a00ce1e782317cd19ed192dcb0ce26ab8b0c1da8928c33de8893897888ff7580: model: meta/llama-3.2-3b-instruct release: 1.10.1 tags: feat_lora: 'false' gpu: H100_NVL gpu_device: 2321:10de llm_engine: tensorrt_llm number_of_gpus: '2' pp: '1' precision: fp8 profile: latency tp: '2' modelFormat: trt-llm spec: - 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key: PROFILE value: LATENCY - key: PRECISION value: FP8 - key: GPU value: L40S - key: COUNT value: 2 - key: GPU DEVICE value: 26B9:10DE - key: NIM VERSION value: 1.10.1 - key: DOWNLOAD SIZE value: 5GB - key: LLM ENGINE value: TENSORRT_LLM - profileId: nim/meta/llama-3.2-3b-instruct:l20x2-latency-fp8-f2nzfrgyia framework: TensorRT-LLM displayName: Llama 3.2 3B Instruct L20x2 FP8 Latency ngcMetadata: c610b690036f0e8ac96ea3ed1e584ef5c4f8a4cf1253664b8dc08df5b404de48: model: meta/llama-3.2-3b-instruct release: 1.10.1 tags: feat_lora: 'false' gpu: L20 gpu_device: 26ba:10de llm_engine: tensorrt_llm number_of_gpus: '2' pp: '1' precision: fp8 profile: latency tp: '2' modelFormat: trt-llm spec: - key: PROFILE value: LATENCY - key: PRECISION value: FP8 - key: GPU value: L20 - key: COUNT value: 2 - key: GPU DEVICE value: 26BA:10DE - key: NIM VERSION value: 1.10.1 - key: DOWNLOAD SIZE value: 5GB - key: LLM ENGINE value: TENSORRT_LLM - profileId: nim/meta/llama-3.2-3b-instruct:hf-392a143-tool-use-v2__10 framework: TensorRT-LLM displayName: Llama 3.2 3B Instruct A100_SXM4_40GBx1 BF16 Throughput ngcMetadata: c6821c013c559912c37e61d7b954c5ca8fe07dda76d8bea0f4a52320e0a54427: model: meta/llama-3.2-3b-instruct release: 1.10.1 tags: feat_lora: 'false' gpu: A100_SXM4_40GB gpu_device: 20b0:10de llm_engine: tensorrt_llm number_of_gpus: '1' pp: '1' precision: bf16 profile: throughput tp: '1' modelFormat: trt-llm spec: - key: PROFILE value: THROUGHPUT - key: PRECISION value: BF16 - key: GPU value: A100_SXM4_40GB - key: COUNT value: 1 - key: GPU DEVICE value: 20B0:10DE - key: NIM VERSION value: 1.10.1 - key: DOWNLOAD SIZE value: 12GB - key: LLM ENGINE value: TENSORRT_LLM - profileId: nim/meta/llama-3.2-3b-instruct:rtx4090x1-throughput-bf16-8m--uis3tg framework: TensorRT-LLM displayName: Llama 3.2 3B Instruct RTX4090x1 BF16 Throughput ngcMetadata: c78670b98ba7d5bc4105cbf723eb1cb514e3cb159dacd3d8b997b20c9ceeb1ea: model: meta/llama-3.2-3b-instruct release: 1.10.1 tags: feat_lora: 'false' gpu: RTX4090 gpu_device: 2684:10de llm_engine: tensorrt_llm number_of_gpus: '1' pp: '1' precision: bf16 profile: throughput tp: '1' modelFormat: trt-llm spec: - key: PROFILE value: THROUGHPUT - key: PRECISION value: BF16 - key: GPU value: RTX4090 - key: COUNT value: 1 - key: GPU DEVICE value: 2684:10DE - key: NIM VERSION value: 1.10.1 - key: DOWNLOAD SIZE value: 7GB - key: LLM ENGINE value: TENSORRT_LLM - profileId: nim/meta/llama-3.2-3b-instruct:l20x1-throughput-fp8-fqsk6q2inq framework: TensorRT-LLM displayName: Llama 3.2 3B Instruct L20x1 FP8 Throughput ngcMetadata: d2f14fb35f10d3ffef37a9f198d3c39f37a1452f65a1b523ec0135868fb23ba7: model: meta/llama-3.2-3b-instruct release: 1.10.1 tags: feat_lora: 'false' gpu: L20 gpu_device: 26ba:10de llm_engine: tensorrt_llm number_of_gpus: '1' pp: '1' precision: fp8 profile: throughput tp: '1' modelFormat: trt-llm spec: - key: PROFILE value: THROUGHPUT - key: PRECISION value: FP8 - key: GPU value: L20 - key: COUNT value: 1 - key: GPU DEVICE value: 26BA:10DE - key: NIM VERSION value: 1.10.1 - key: DOWNLOAD SIZE value: 5GB - key: LLM ENGINE value: TENSORRT_LLM - profileId: nim/meta/llama-3.2-3b-instruct:h200x2-latency-fp8-zzxu8dlxcw framework: TensorRT-LLM displayName: Llama 3.2 3B Instruct H200x2 FP8 Latency ngcMetadata: e4f217a5fb016b570e34b8a8eb06051ccfef9534ba43da973bb7f678242eaa5f: model: meta/llama-3.2-3b-instruct release: 1.10.1 tags: feat_lora: 'false' gpu: H200 gpu_device: 2335:10de llm_engine: tensorrt_llm number_of_gpus: '2' pp: '1' precision: fp8 profile: latency tp: '2' modelFormat: trt-llm spec: - key: PROFILE value: LATENCY - key: PRECISION value: FP8 - key: GPU value: H200 - key: COUNT value: 2 - key: GPU DEVICE value: 2335:10DE - key: NIM VERSION value: 1.10.1 - key: DOWNLOAD SIZE value: 5GB - key: LLM ENGINE value: TENSORRT_LLM - profileId: nim/meta/llama-3.2-3b-instruct:h100x1-throughput-bf16--lfg89p-ew framework: TensorRT-LLM displayName: Llama 3.2 3B Instruct H100x1 BF16 Throughput ngcMetadata: e7dbd9a8ce6270d2ec649a0fecbcae9b5336566113525f20aee3809ba5e63856: model: meta/llama-3.2-3b-instruct release: 1.10.1 tags: feat_lora: 'false' gpu: H100 gpu_device: 2330:10de llm_engine: tensorrt_llm number_of_gpus: '1' pp: '1' precision: bf16 profile: throughput tp: '1' modelFormat: trt-llm spec: - key: PROFILE value: THROUGHPUT - key: PRECISION value: BF16 - key: GPU value: H100 - key: COUNT value: 1 - key: GPU DEVICE value: 2330:10DE - key: NIM VERSION value: 1.10.1 - key: DOWNLOAD SIZE value: 7GB - key: LLM ENGINE value: TENSORRT_LLM - profileId: nim/meta/llama-3.2-3b-instruct:a10gx2-latency-bf16-0ksvrbt0ww framework: TensorRT-LLM displayName: Llama 3.2 3B Instruct A10Gx2 BF16 Latency ngcMetadata: ee94491ed7167340de93fe9d1c87f10ba424da6f497eeabf83b4edcbeb69364c: model: meta/llama-3.2-3b-instruct release: 1.10.1 tags: feat_lora: 'false' gpu: A10G gpu_device: 2237:10de llm_engine: tensorrt_llm number_of_gpus: '2' pp: '1' precision: bf16 profile: latency tp: '2' modelFormat: trt-llm spec: - key: PROFILE value: LATENCY - key: PRECISION value: BF16 - key: GPU value: A10G - key: COUNT value: 2 - key: GPU DEVICE value: 2237:10DE - key: NIM VERSION value: 1.10.1 - key: DOWNLOAD SIZE value: 8GB - key: LLM ENGINE value: TENSORRT_LLM - profileId: nim/meta/llama-3.2-3b-instruct:b200x2-latency-bf16-f-dquqynva framework: TensorRT-LLM displayName: Llama 3.2 3B Instruct B200x2 BF16 Latency ngcMetadata: f44768c625db71a327cf17e750d5e1a8e60171a8d8ef6b4c1c4b57fe74c9bf46: model: meta/llama-3.2-3b-instruct release: 1.10.1 tags: feat_lora: 'false' gpu: B200 gpu_device: 2901:10de llm_engine: tensorrt_llm number_of_gpus: '2' pp: '1' precision: bf16 profile: latency tp: '2' modelFormat: trt-llm spec: - key: PROFILE value: LATENCY - key: PRECISION value: BF16 - key: GPU value: B200 - key: COUNT value: 2 - key: GPU DEVICE value: 2901:10DE - key: NIM VERSION value: 1.10.1 - key: DOWNLOAD SIZE value: 8GB - key: LLM ENGINE value: TENSORRT_LLM - profileId: nim/meta/llama-3.2-3b-instruct:hf-392a143-tool-use-v2__11 framework: TensorRT-LLM displayName: Llama 3.2 3B Instruct GH200_480GBx1 FP8 Throughput ngcMetadata: f49b49f3d90159a594def51efd8595f1d618e288bca2721fe08e786a1ac67d04: model: meta/llama-3.2-3b-instruct release: 1.10.1 tags: feat_lora: 'false' gpu: GH200_480GB gpu_device: 2342:10de llm_engine: tensorrt_llm number_of_gpus: '1' pp: '1' precision: fp8 profile: throughput tp: '1' modelFormat: trt-llm spec: - key: PROFILE value: THROUGHPUT - key: PRECISION value: FP8 - key: GPU value: GH200_480GB - key: COUNT value: 1 - key: GPU DEVICE value: 2342:10DE - key: NIM VERSION value: 1.10.1 - key: DOWNLOAD SIZE value: 12GB - key: LLM ENGINE value: TENSORRT_LLM - profileId: nim/meta/llama-3.2-3b-instruct:rtx4090x1-latency-bf16-b25uxqlekg framework: TensorRT-LLM displayName: Llama 3.2 3B Instruct RTX4090x1 BF16 Latency ngcMetadata: f5e266ce2a4692b37b80e0cb6ab2dea59a54d26b80396f1a521921384bd79ffe: model: meta/llama-3.2-3b-instruct release: 1.10.1 tags: feat_lora: 'false' gpu: RTX4090 gpu_device: 2684:10de llm_engine: tensorrt_llm number_of_gpus: '1' pp: '1' precision: bf16 profile: latency tp: '1' modelFormat: trt-llm spec: - key: PROFILE value: LATENCY - key: PRECISION value: BF16 - key: GPU value: RTX4090 - key: COUNT value: 1 - key: GPU DEVICE value: 2684:10DE - key: NIM VERSION value: 1.10.1 - key: DOWNLOAD SIZE value: 7GB - key: LLM ENGINE value: TENSORRT_LLM - profileId: nim/meta/llama-3.2-3b-instruct:hf-392a143-tool-use-v2__12 framework: TensorRT-LLM displayName: Llama 3.2 3B Instruct GH200_480GBx1 BF16 Throughput ngcMetadata: f7f74ecd523cd63065a50016a8786a893b9b1efe0d313bc5bcc54682f56e55fe: model: meta/llama-3.2-3b-instruct release: 1.10.1 tags: feat_lora: 'false' gpu: GH200_480GB gpu_device: 2342:10de llm_engine: tensorrt_llm number_of_gpus: '1' pp: '1' precision: bf16 profile: throughput tp: '1' modelFormat: trt-llm spec: - key: PROFILE value: THROUGHPUT - key: PRECISION value: BF16 - key: GPU value: GH200_480GB - key: COUNT value: 1 - key: GPU DEVICE value: 2342:10DE - key: NIM VERSION value: 1.10.1 - key: DOWNLOAD SIZE value: 12GB - key: LLM ENGINE value: TENSORRT_LLM - profileId: nim/meta/llama-3.2-3b-instruct:l40sx2-latency-bf16-pb6dhqvrgw framework: TensorRT-LLM displayName: Llama 3.2 3B Instruct L40Sx2 BF16 Latency ngcMetadata: fa36c3502e92c50f78a1906242f929864955e702b7dbfbdb19758fb7ee9aa811: model: meta/llama-3.2-3b-instruct release: 1.10.1 tags: feat_lora: 'false' gpu: L40S gpu_device: 26b9:10de llm_engine: tensorrt_llm number_of_gpus: '2' pp: '1' precision: bf16 profile: latency tp: '2' modelFormat: trt-llm spec: - key: PROFILE value: LATENCY - key: PRECISION value: BF16 - key: GPU value: L40S - key: COUNT value: 2 - key: GPU DEVICE value: 26B9:10DE - key: NIM VERSION value: 1.10.1 - key: DOWNLOAD SIZE value: 8GB - key: LLM ENGINE value: TENSORRT_LLM labels: - Llama - Meta - Multilingual Large Language Model - NVIDIA Validated config: architectures: - Other modelType: llama license: NVIDIA AI Foundation Models Community License - name: NeMo Retriever-Parse displayName: Nemoretriever Parse modelHubID: nemoretriever-parse category: Retrieval type: NGC description: Document text extraction model that converts document images into structured text with bounding boxes and semantic classes. Improves retrieval accuracy and supports LLM training data generation. requireLicense: true licenseAgreements: - label: Use Policy url: https://llama.meta.com/llama3/use-policy/ - label: License Agreement url: https://llama.meta.com/llama3/license/ modelVariants: - variantId: nemoretriever-parse:1.2.0 modelCard: 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source: URL: https://build.nvidia.com/nvidia/nemoretriever-parse optimizationProfiles: - profileId: nim/nvidia/nemoretriever-parse:a100x1-throughput-bf16-e9wjao-enw framework: TensorRT-LLM displayName: nemoretriever-parse A100 BF16 Throughput ngcMetadata: 19c68819d9428cfa494e977f4d2be6378215a8f610cce9bdfc0aa3cdd7d66aa9: model: nvidia/nemoretriever-parse release: 1.2.0 tags: gpu: A100 gpu_device: 20b2:10de llm_engine: tensorrt_llm pp: '1' profile: throughput precision: bf16 tp: '1' modelFormat: trt-llm spec: - key: PROFILE value: Throughput - key: PRECISION value: BF16 - key: GPU value: A100 - key: COUNT value: 1 - key: GPU DEVICE value: 20b2:10de - key: NIM VERSION value: 1.2.0 - key: DOWNLOAD SIZE value: 600MB - profileId: nim/nvidia/nemoretriever-parse:h100x1-throughput-bf16-2apiazbpma framework: TensorRT-LLM displayName: nemoretriever-parse H100 BF16 Throughput ngcMetadata: 8db6dcd816ca1ce8d07e72d8b9c4682120b3c50799422361e35b4ab87820efd6: model: nvidia/nemoretriever-parse release: 1.2.0 tags: gpu: H100 gpu_device: 2330:10de llm_engine: tensorrt_llm pp: '1' profile: throughput precision: bf16 tp: '1' modelFormat: trt-llm spec: - key: PROFILE value: Throughput - key: PRECISION value: BF16 - key: GPU value: H100 - key: COUNT value: 1 - key: GPU DEVICE value: 2330:10de - key: NIM VERSION value: 1.2.0 - key: DOWNLOAD SIZE value: 600MB - profileId: nim/nvidia/nemoretriever-parse:l40sx1-throughput-bf16-r98ogb1a1a framework: TensorRT-LLM displayName: nemoretriever-parse L40S BF16 Throughput ngcMetadata: 00c8a43783e7acf3d59a0d773cd78d3d29eaa71fa4412af7af2fbaf20e196a8b: model: nvidia/nemoretriever-parse release: 1.2.0 tags: gpu: L40S gpu_device: 26b5:10de llm_engine: tensorrt_llm pp: '1' profile: throughput precision: bf16 tp: '1' modelFormat: trt-llm spec: - key: PROFILE value: Throughput - key: PRECISION value: BF16 - key: GPU value: L40S - key: COUNT value: 1 - key: GPU DEVICE value: 26b5:10de - key: NIM VERSION value: 1.2.0 - key: DOWNLOAD SIZE value: 600MB labels: - NeMo - Text Extraction - Large Language Model - NVIDIA Validated config: architectures: - Other modelType: llama license: NVIDIA AI Foundation Models Community License - name: Nemoretriever Graphic Elements V1 displayName: Nemoretriever Graphic Elements V1 modelHubID: nemoretriever-graphic-elements-v1 category: Retrieval type: NGC description: Specialized object detection model that extracts elements from charts and graphs — titles, axis labels, legends, and data annotations. Purpose-built for document understanding pipelines. requireLicense: true licenseAgreements: - label: Use Policy url: https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-community-models-license/ - label: License Agreement url: https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-license-agreement/ modelVariants: - variantId: Nemoretriever Graphic Elements V1 modelCard: {
    "accessType": "NOT_LISTED",
    "application": "Other",
    "bias": "| Field | Response |\n| ----- | ----- |\n| Participation considerations from adversely impacted groups [protected classes](https://www.senate.ca.gov/content/protected-classes) in model design and testing | None |\n| Measures taken to mitigate against unwanted bias | None |",
    "builtBy": "NVIDIA",
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    "createdDate": "2025-03-14T00:29:08.674Z",
    "description": "## **Model Overview**\n\n### **Description**\n\nThe **NeMo Retriever Graphic Elements v1** model is a specialized object detection system designed to identify and extract key elements from charts and graphs. Based on YOLOX, an anchor-free version of YOLO (You Only Look Once), this model combines a simpler architecture with enhanced performance. While the underlying technology builds upon work from [Megvii Technology](https://github.com/Megvii-BaseDetection/YOLOX), we developed our own base model through complete retraining rather than using pre-trained weights.\n\nThe model excels at detecting and localizing various graphic elements within chart images, including titles, axis labels, legends, and data point annotations. This capability makes it particularly valuable for document understanding tasks and automated data extraction from visual content.\n\nThis model is ready for commercial use and is a part of the NVIDIA NeMo Retriever family of NIM microservices specifically for object detection and multimodal extraction of enterprise documents.\n\nThis model supersedes the [CACHED](https://build.nvidia.com/university-at-buffalo/cached) model.\n\n### **License/Terms of use**\n\nUse of this model is governed by the [NVIDIA AI Foundation Models Community License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-ai-foundation-models-community-license-agreement/).\n\n**You are responsible for ensuring that your use of NVIDIA AI Foundation Models complies with all applicable laws.**\n\n**Deployment Geography**: Global\n\n**Use Case**: <br>\n\nThis model is designed for automating extraction of graphic elements of charts in enterprise documents. Key applications include:\n- Enterprise document extraction, embedding and indexing\n- Augmenting Retrieval Augmented Generation (RAG) workflows with multimodal retrieval\n- Data extraction from legacy documents and reports\n\n**Release Date**: 2025-03-17\n\n### **Model Architecture**\n\n**Architecture type:** YOLOX <br>\n**Network architecture:** DarkNet53 Backbone \\+ FPN Decoupled head (one 1x1 convolution \\+ 2 parallel 3x3 convolutions (one for the classification and one for the bounding box prediction)\n\nYOLOX is a single-stage object detector that improves on Yolo-v3. The model is fine-tuned to detect 10 classes of objects in documents:\n1. Chart title\n1. X-axis title\n1. Y-axis title\n1. X-axis label(s)\n1. Y-axis label(s)\n1. Legend label(s)\n1. Legend title\n1. Markings and values labels\n1. Miscellaneous other texts on the chart\n\n## **Input**\n\n**Input type(s):** Image <br>\n**Input format(s):** Red, Green, Blue (RGB) <br>\n**Input parameters:** Two Dimensional (2D) <br>\n**Other properties related to input:** Expected input is a `np.ndarray` image of shape `[Channel, Width, Height]`, or an `np.ndarray` batch of image of shape `[Batch, Channel, Width, Height]`.\n\n## **Output**\n\n**Output type(s):** Text associated to each of the following classes : <br>\n* `[\"chart_title\", \"x_title\", \"y_title\", \"xlabel\", \"ylabel\", \"other\", \"legend_label\", \"legend_title\", \"mark_label\", \"value_label\"]`\n\n**Output format:** Dict of String <br>\n**Output parameters:** 1D <br>\n**Other properties related to output:** None\n\n### Software Integration\n\n**Runtime Engine**: **NeMo Retriever Graphic Elements v1** NIM <br>\n**Supported Hardware Microarchitecture Compatibility**: NVIDIA Ampere, NVIDIA Hopper, NVIDIA Lovelace <br>\n**Supported Operating System(s)**: Linux\n\n## **Model Version(s):**\n\n* `nemoretriever-graphic-elements-v1`\n\n## **Training Dataset:**\n\n* PubMed Central (PMC) Chart Dataset\n\n  * **Link:** [https://chartinfo.github.io/index\\_2022.html](https://chartinfo.github.io/index_2022.html)\n  * **Data collection method:** Automated, Human\n  * **Labeling method**: Human\n  * **Description:** A real-world dataset collected from PubMed Central Documents and manually annotated, released in the ICPR 2022 CHART-Infographic competition. There are 5,614 images for chart element detection, 4,293 images for final plot detection and data extraction, and 22,924 images for chart classification.\n\n* DeepRule dataset\n\n  * **Link:** [https://github.com/soap117/DeepRule](https://github.com/soap117/DeepRule)\n  * **Data collection method:** Automated, Human\n  * **Labeling method**: Distillation by the CACHED model\n  * **Description:** The original dataset consists of 386,966 chart images obtained by crawling public Excel sheets from the web with texts overwritten to protect privacy. The CACHED model is used to pseudo-label the relevant classes. We used a subsample of 9,091 charts where a title was detected for training alongside with the 5,614 PMC training images.\n\n## **Evaluation Results**\n\nResults were evaluated using the **PMC Chart dataset**. The **Mean Average Precision (mAP)** was used as the evaluation metric to measure the model's ability to correctly identify and localize objects across different confidence thresholds.\n\n### **Data Collection & Labeling**\n- **Data collection method:** **Hybrid (Automated & Human)**\n- **Labeling method:** **Hybrid (Automated & Human)**\n- **Properties:** The validation dataset is the same as the **PMC Chart dataset**.\n\n### **Dataset Overview**\n\n**Number of bounding boxes and images per class:**\n\n| **Label**       | **Images** | **Boxes** |\n|----------------|----------:|---------:|\n| **chart_title**   | 38  | 38  |\n| **legend_label**  | 318 | 1077 |\n| **legend_title**  | 17  | 19  |\n| **mark_label**    | 42  | 219  |\n| **other**         | 113 | 464  |\n| **value_label**   | 52  | 726  |\n| **x_title**       | 404 | 437  |\n| **xlabel**        | 553 | 4091 |\n| **y_title**       | 502 | 505  |\n| **ylabel**        | 534 | 3944 |\n| **Total**         | 560 | **11,520** |\n\n### **Per-Class Performance Metrics**\n\n#### **Average Precision (AP)**\n| **Class**       | **AP**   | **Class**       | **AP**   | **Class**      | **AP**   |\n|----------------|---------:|----------------|---------:|---------------|---------:|\n| **chart_title**  | 82.38  | **x_title**     | 88.77  | **y_title**    | 89.48  |\n| **xlabel**      | 85.04  | **ylabel**      | 86.22  | **other**      | 55.14  |\n| **legend_label** | 84.09  | **legend_title** | 60.61  | **mark_label** | 49.31  |\n| **value_label**  | 62.66  |                |        |               |        |\n\n#### **Average Recall (AR)**\n| **Class**       | **AR**   | **Class**       | **AR**   | **Class**      | **AR**   |\n|----------------|---------:|----------------|---------:|---------------|---------:|\n| **chart_title**  | 93.16  | **x_title**     | 92.31  | **y_title**    | 92.32  |\n| **xlabel**      | 88.93  | **ylabel**      | 89.40  | **other**      | 79.48  |\n| **legend_label** | 88.07  | **legend_title** | 68.42  | **mark_label** | 73.61  |\n| **value_label**  | 68.32  |                |        |               |        |\n\n\n## **Inference:**\n\n**Engine:** Tensor(RT) <br>\n**Test hardware:** Tested on all supported hardware listed in compatibility section\n\n## **Ethical Considerations:**\n\nNVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.\n\n**For more detailed information on ethical considerations for this model**, please see the Model Card++ [Explainability](explainability.md), [Bias](bias.md), [Safety & Security](safety-security.md), and [Privacy](privacy.md) Subcards.\n\nPlease report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).\n\n## Get Help\n\n### Enterprise Support\nGet access to knowledge base articles and support cases or  submit a ticket at the [NVIDIA AI Enterprise Support Services page.](https://www.nvidia.com/en-us/data-center/products/ai-enterprise-suite/support/).\n\n### NVIDIA NIM Documentation\nVisit the [NeMo Retriever docs page](https://docs.nvidia.com/nemo/retriever/index.html) for release documentation, deployment guides and more.",
    "displayName": "NeMo Retriever Graphic Elements v1",
    "explainability": "| Field | Response |\n| ----- | ----- |\n| Intended Application & Domain: | Object Detection |\n| Model Type: | YOLOX-architecture for detection of graphic elements within images of charts. |\n| Intended User: | Enterprise developers, data scientists, and other technical users who need to extract textual elements from charts and graphs. |\n| Output: | A List of dictionaries containing lists of dictionaries of floating point numbers (representing bounding box information). <br> **Example**: `{\"data\": [{\"index\": 0,\"bounding_boxes\": {\"table\": [{\"x_min\": 0.6503,\"y_min\": 0.2161,\"x_max\": 0.7835,\"y_max\": 0.3236,\"confidence\": 0.9306}]}}]}` |\n| Describe how the model works: | Finds and identifies objects in images by first dividing the image into a grid. For each section of the grid, the model uses a series of neural networks to extract visual features and simultaneously predict what objects are present (in this case \"chart title\" or \"axis label\" etc.) and exactly where they are located in that section, all in a single pass through the image. |\n| Performance Metrics: | Accuracy, Throughput, and Latency |\n| Potential Known Risks: | This model will not always detect all graphic elements in an image, especially for uncommon elements or lower quality images. |\n| Licensing & Terms of Use: | Use of this model is governed by the [NVIDIA AI Foundation Models Community License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-ai-foundation-models-community-license-agreement/) and the [Apache 2.0 License](https://github.com/Megvii-BaseDetection/YOLOX/blob/main/LICENSE). |\n| Technical Limitations | The model may correctly detect graphic elements of charts, espectially on uncommon chart styles or lower quality images. |\n| Name the adversely impacted groups this has been tested to deliver comparable outcomes regardless of: | Not Applicable |\n| Verified to have met prescribed NVIDIA quality standards: | Yes |",
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    "privacy": "| Field | Response |\n| ----- | ----- |\n| Generatable or reverse engineerable personal data? | No |\n| Personal data used to create this model? | None |\n| How often is the dataset reviewed? | Before Every Release |\n| Is a mechanism in place to honor data subject right of access or deletion of personal data? | No |\n| Is there provenance for all datasets used in training? | Yes |\n| Does data labeling (annotation, metadata) comply with privacy laws? | Yes |",
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    "safetyAndSecurity": "| Field | Response |\n| ----- | ----- |\n| Model Application(s): | Object Detection for Retrieval, focused on Enterprise |\n| Describe the physical safety impact (if present). | Not Applicable |\n| Use Case Restrictions: | Abide by [NVIDIA AI Foundation Models Community License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-community-models-license/).   |\n| Model and dataset restrictions: | The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. Restrictions enforce dataset access during training, and dataset license constraints adhered to. |",
    "shortDescription": "NVIDIA NeMo\u2122 Retriever NIM for graphic elements v1 is a fine-tuned object detection model, trained specifically for detecting the elements of charts and tables in documents",
    "teamName": "nvidia",
    "updatedDate": "2025-10-15T18:43:31.513Z"
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Improved accuracy and broader element coverage over its predecessor. requireLicense: true licenseAgreements: - label: Use Policy url: https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-community-models-license/ - label: License Agreement url: https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-license-agreement/ modelVariants: - variantId: Nemoretriever Page Elements V2 modelCard: {
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    "bias": "| Field | Response |\n| ----- | ----- |\n| Participation considerations from adversely impacted groups [protected classes](https://www.senate.ca.gov/content/protected-classes) in model design and testing | None |\n| Measures taken to mitigate against unwanted bias | None |",
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    "createdDate": "2025-03-14T00:47:01.563Z",
    "description": "## Model Overview\n\n### Description\n\nThe **NeMo Retriever Page Elements v2** model is a specialized object detection model designed to identify and extract key elements from charts and graphs. While the underlying technology builds upon work from [Megvii Technology](https://github.com/Megvii-BaseDetection/YOLOX), we developed our own base model through complete retraining rather than using pre-trained weights. YOLOX is an anchor-free version of YOLO (You Only Look Once), this model combines a simpler architecture with enhanced performance. The model is trained to detect **tables**, **charts**, **infographics**, and **titles** in documents.\n\nThis model supersedes the [nv-yolox-page-elements](https://build.nvidia.com/nvidia/nv-yolox-page-elements-v1) model.\n\nThis model is ready for commercial use and is a part of the NVIDIA NeMo Retriever family of NIM microservices specifically for object detection and multimodal extraction of enterprise documents.\n\n### License/Terms of use\n\nThe use of this model is governed by the [NVIDIA AI Foundation Models Community License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-community-models-license/).\n\n**You are responsible for ensuring that your use of NVIDIA AI Foundation Models complies with all applicable laws.**\n\n### Model Architecture\n\n**Architecture Type**: YOLOX <br>\n**Network Architecture**: DarkNet53 Backbone \\+ FPN Decoupled head (one 1x1 convolution \\+ 2 parallel 3x3 convolutions (one for the classification and one for the bounding box prediction). YOLOX is a single-stage object detector that improves on Yolo-v3. <br>\n**Deployment Geography**: Global\n\n**Use Case**: <br>\nThis model is designed for automating extraction of charts, tables, infographics, and titles in enterprise documents. Key applications include:\n- Enterprise document extraction, embedding and indexing\n- Augmenting Retrieval Augmented Generation (RAG) workflows with multimodal retrieval\n- Data extraction from legacy documents and reports\n\n**Release Date**: 2025-03-17\n\n### Intended use\n\nThe **NeMo Retriever Page Elements v2** model is suitable for users who want to extract, and ultimately retrieve, tables, charts and infographics. It can be used for document analysis, understanding and processing.\n\n## Technical Details\n\n### Input\n\n**Input Type(s)**: Image <br>\n**Input Format(s)**: Red, Green, Blue (RGB) <br>\n**Input Parameters**: Two Dimensional (2D)<br>\n**Other Properties Related to Input**: Image size resized to `(1024, 1024)`\n\n### Output\n\n**Output Type(s)**: Array <br>\n**Output Format**: A dictionary of dictionaries containing `np.ndarray`. The outer dictionary contains each sample (page). Inner dictionary contains list of dictionaries with bounding boxes, class, and confidence for that page <br>\n**Output Parameters**: 1D <br>\n**Other Properties Related to Output**: Output contains Bounding box, detection confidence and object class (chart, table, infographic, title). Thresholds used for non-maximum suppression `conf_thresh = 0.01`; `iou_thresh = 0.5` <br>\n**Output Classes**: <br>\n  * Table\n    * Data structured in rows and columns\n  * Chart\n    * Specifically bar charts, line charts, or pie charts\n  * Infographic\n    * Visual representations of information that is more complex than a chart, including diagrams and flowcharts\n    * Maps are _not_ considered infographics\n  * Title\n    * Titles can be page titles, section titles, or table/chart/infographic titles\n\n### Software Integration\n\n**Runtime**: **NeMo Retriever Page Elements v2** NIM <br>\n**Supported Hardware Microarchitecture Compatibility**: NVIDIA Ampere, NVIDIA Hopper, NVIDIA Lovelace <br>\n**Supported Operating System(s)**: Linux <br>\n\n## Model Version(s):\n\n* `nemoretriever-page-elements-v2`\n\n## Training Dataset & Evaluation\n\n### Training Dataset\n\n**Data collection method by dataset**: Automated <br>\n**Labeling method by dataset**: Hybrid: Automated, Human <br>\n**Pretraining (by NVIDIA)**: 118,287 images of the [COCO train2017](https://cocodataset.org/#download) dataset <br>\n**Finetuning (by NVIDIA)**: 36,093 images from [Digital Corpora dataset](https://digitalcorpora.org/), with annotations from [Azure AI Document Intelligence](https://azure.microsoft.com/en-us/products/ai-services/ai-document-intelligence) and data annotation team <br>\n**Number of bounding boxes per class**: 35,328 tables, 44,178 titles, 11,313 charts and 6,500 infographics. The layout model of Document Intelligence was used with `2024-02-29-preview` API version.\n\n### Evaluation Results\n\nThe primary evaluation set is a cut of the Azure labels and digital corpora images. Number of bounding boxes per class: 1,483 tables, 1,965 titles, 404 charts and 500 infographics. Mean Average Precision (mAP) was used as an evaluation metric, which measures the model's ability to correctly identify and localize objects across different confidence thresholds.\n\n**Data collection method by dataset**: Hybrid: Automated, Human <br>\n**Labeling method by dataset**: Hybrid: Automated, Human <br>\n**Properties**: We evaluated with Azure labels from manually selected pages, as well as manual inspection on public PDFs and powerpoint slides.\n\n**Per-class Performance Metrics**:\n| Class       | AP (%) | AR (%) |\n|:------------|:-------|:-------|\n| table       | 45.619 | 69.814 |\n| chart       | 53.419 | 75.755 |\n| title       | 45.116 | 65.245 |\n| infographic | 96.591 | 97.400 |\n\n\n## Inference:\n\n**Engine**: TensorRT <br>\n**Test hardware**: See Support Matrix from NIM documentation\n\n## Ethical Considerations\n\nNVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.\n\n**For more detailed information on ethical considerations for this model**, please see the Model Card++ [Explainability](explainability.md), [Bias](bias.md), [Safety & Security](safety-security.md), and [Privacy](privacy.md) Subcards.\n\nPlease report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).\n\n## Get Help\n\n### Enterprise Support\nGet access to knowledge base articles and support cases or  submit a ticket at the [NVIDIA AI Enterprise Support Services page.](https://www.nvidia.com/en-us/data-center/products/ai-enterprise-suite/support/).\n\n### NVIDIA NIM Documentation\nVisit the [NeMo Retriever docs page](https://docs.nvidia.com/nemo/retriever/index.html) for release documentation, deployment guides and more.",
    "displayName": "NeMo Retriever Page Elements v2",
    "explainability": "| Field | Response |\n| ----- | ----- |\n| Intended Application & Domain: | Document Understanding |\n| Model Type: | YOLOX Object Detection for Charts, Tables, Infographics, and Titles |\n| Intended User: | Enterprise developers, data scientists, and other technical users who need to extract structural elements from documents. |\n| Output: | A List of dictionaries containing lists of dictionaries of floating point numbers (representing bounding box information). <br> **Example**: `{\"data\": [{\"index\": 0,\"bounding_boxes\": {\"table\": [{\"x_min\": 0.6503,\"y_min\": 0.2161,\"x_max\": 0.7835,\"y_max\": 0.3236,\"confidence\": 0.9306}]}}]}` |\n| Describe how the model works: | Finds and identifies objects in images by first dividing the image into a grid. For each section of the grid, the model uses a series of neural networks to extract visual features and simultaneously predict what objects are present (in this case \"chart\" or \"table\" etc.) and exactly where they are located in that section, all in a single pass through the image. |\n| Potential Known Risks: | This model may not always detect all elements in a document. |\n| Licensing & Terms of Use: | Use of this model is governed by the [NVIDIA AI Foundation Models Community License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-ai-foundation-models-community-license-agreement/) and the [Apache 2.0 License](https://github.com/Megvii-BaseDetection/YOLOX/blob/main/LICENSE). |\n| Technical Limitations | The model may not generalize to unknown document types/formats not commonly found on the web. |\n| Name the adversely impacted groups this has been tested to deliver comparable outcomes regardless of: | Not Applicable |\n| Verified to have met prescribed NVIDIA quality standards: | Yes |",
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    "privacy": "| Field | Response |\n| ----- | ----- |\n| Generatable or reverse engineerable personal data? | No |\n| Personal data used to create this model? | None |\n| How often is the dataset reviewed? | Before Every Release |\n| Is a mechanism in place to honor data subject right of access or deletion of personal data? | No |\n| Is there provenance for all datasets used in training? | Yes |\n| Does data labeling (annotation, metadata) comply with privacy laws? | Yes |",
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    "safetyAndSecurity": "| Field | Response |\n| ----- | ----- |\n| Model Application(s): | Object Detection for Retrieval, focused on Enterprise |\n| Describe the physical safety impact (if present). | Not Applicable |\n| Use Case Restrictions: | Abide by [NVIDIA AI Foundation Models Community License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-community-models-license/).   |\n| Model and dataset restrictions: | The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. Restrictions enforce dataset access during training, and dataset license constraints adhered to. |",
    "shortDescription": "NVIDIA NeMo\u2122 Retriever NIM for page elements v2 is a fine-tuned object detection model, trained specifically for detecting charts, tables, infographics, and titles on a document page.",
    "teamName": "nvidia",
    "updatedDate": "2025-10-15T18:43:49.378Z"
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key: PRECISION value: FP16 - key: GPU value: RTX6000 - key: COUNT value: 1 - key: NIM VERSION value: 1.6.0 - key: DOWNLOAD SIZE value: 1GB - key: BACKEND value: TRITON - key: MODEL TYPE value: TENSORRT - profileId: nim/nvidia/nemoretriever-page-elements-v2:l40sx1-trt-fp16-qnrq36wfcw framework: TensorRT-LLM displayName: Nemoretriever Page Elements V2 L40Sx1 FP16 ngcMetadata: bc1487bf0ec3430f17595fff029c1bc50668344c7a30f9e5d64ee061c6e2d5fa: model: nvidia/nemoretriever-page-elements-v2 release: 1.6.0 tags: backend: triton compute_capability: '8.9' model_type: tensorrt precision: fp16 modelFormat: trt-llm spec: - key: PRECISION value: FP16 - key: GPU value: L40S - key: COUNT value: 1 - key: NIM VERSION value: 1.6.0 - key: DOWNLOAD SIZE value: 1GB - key: BACKEND value: TRITON - key: MODEL TYPE value: TENSORRT - profileId: nim/nvidia/nemoretriever-page-elements-v2:a100x1-onnx-fp16-wagmq6-x1q framework: ONNX displayName: Nemoretriever Page Elements V2 ONNX FP16 ngcMetadata: edc693c6fccd68d266622eace04225421e353d7ce31e3b207afc5ff35124127b: model: nvidia/nemoretriever-page-elements-v2 release: 1.6.0 tags: backend: triton model_type: onnx precision: fp16 modelFormat: onnx spec: - 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label: Use Policy url: https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-community-models-license/ - label: License Agreement url: https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-license-agreement/ modelVariants: - variantId: Nemoretriever Table Structure V1 modelCard: {
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    "application": "Other",
    "bias": "| Field | Response |\n| ----- | ----- |\n| Participation considerations from adversely impacted groups [protected classes](https://www.senate.ca.gov/content/protected-classes) in model design and testing | None |\n| Measures taken to mitigate against unwanted bias | None |",
    "builtBy": "NVIDIA",
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    "createdDate": "2025-03-14T00:35:30.879Z",
    "description": "## Model Overview\n\n### Description\n\nThe **NeMo Retriever Table Structure v1** model is a specialized object detection model designed to identify and extract the structure of tables in images. Based on YOLOX, an anchor-free version of YOLO (You Only Look Once), this model combines a simpler architecture with enhanced performance. While the underlying technology builds upon work from [Megvii Technology](https://github.com/Megvii-BaseDetection/YOLOX), we developed our own base model through complete retraining rather than using pre-trained weights.\n\nThe model excels at detecting and localizing the fundamental structural elements within tables. Through careful fine-tuning, it can accurately identify and delineate three key components within tables:\n\n1. Individual cells (including merged cells)\n2. Rows\n3. Columns\n\nThis specialized focus on table structure enables precise decomposition of complex tables into their constituent parts, forming the foundation for downstream retrieval tasks. This model helps convert tables into the markdown format which can improve retrieval accuracy.\n\nThis model is ready for commercial use and is a part of the NVIDIA NeMo Retriever family of NIM microservices specifically for object detection and multimodal extraction of enterprise documents.\n\n### License/Terms of use\n\nThe use of this model is governed by the [NVIDIA AI Foundation Models Community License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-community-models-license/).\n\n**You are responsible for ensuring that your use of NVIDIA AI Foundation Models complies with all applicable laws.**\n\n### Model Architecture\n\n**Architecture Type**: YOLOX <br>\n**Network Architecture**: DarkNet53 Backbone \\+ FPN Decoupled head (one 1x1 convolution \\+ 2 parallel 3x3 convolutions (one for the classification and one for the bounding box prediction). The YOLOX architecture is a single-stage object detector that improves on Yolo-v3. <br>\n**Deployment Geography**: Global <br>\n\n**Use Case**: <br>\nThis model specializes in analyzing images containing tables by:\n- Detecting and extracting table structure elements (rows, columns, and cells)\n- Providing precise location information for each detected element\n- Supporting downstream tasks like table analysis and data extraction\n\nThe model is designed to work in conjunction with OCR (Optical Character Recognition) systems to:\n1. Identify the structural layout of tables\n2. Preserve the relationships between table elements\n3. Enable accurate extraction of tabular data from images\n\nIdeal for:\n- Document processing systems\n- Automated data extraction pipelines\n- Digital content management solutions\n- Business intelligence applications\n\n**Release Date**: 2025-03-17\n\n## Technical Details\n\n### Input\n\n**Input type(s)**: Image <br>\n**Input format(s)**: Red, Green, Blue (RGB) <br>\n**Input parameters**: Two Dimensional (2D) <br>\n**Other properties related to input**: Image size resized to `(1024, 1024)`\n\n### Output\n\n**Output Type(s)**: Array <br>\n**Output Format**: A dictionary of dictionaries containing `np.ndarray` objects. The outer dictionary contains each sample (table). Inner dictionary contains list of dictionaries with bounding boxes, class, and confidence for that table <br>\n**Output Parameters**: 1D <br>\n**Other Properties Related to Output**: Output contains Bounding box, detection confidence and object class (cell, row, column). Thresholds used for non-maximum suppression `conf_thresh = 0.01`; `iou_thresh = 0.25`\n\n### Software Integration\n\n**Runtime**: **NeMo Retriever Table Structure v1** NIM <br>\n**Supported Hardware Microarchitecture Compatibility**: NVIDIA Ampere, NVIDIA Hopper, NVIDIA Lovelace <br>\n**Supported Operating System(s)**: Linux\n\n## Model Version(s):\n\n* `nemoretriever-table-structure-v1`\n\n## Training Dataset & Evaluation\n\n### Training Dataset\n\n**Data collection method by dataset**: Automated <br>\n**Labeling method by dataset**: Automated <br>\n**Pretraining**: [COCO train2017](https://cocodataset.org/#download)\n**Finetuning (by NVIDIA)**: 23,977 images from [Digital Corpora dataset](https://digitalcorpora.org/), with annotations from [Azure AI Document Intelligence](https://azure.microsoft.com/en-us/products/ai-services/ai-document-intelligence).\nNumber of bounding boxes per class: 1,828,978 cells, 134,089 columns and 316,901 rows. The layout model of Document Intelligence was used with `2024-02-29-preview` API version.\n\n### Evaluation Results\n\n**The primary evaluation set**: 2,459 digital corpora images with Azure labels. Number of bounding boxes per class: 200,840 cells, 13,670 columns and 34,575 rows. mAP was used as an evaluation metric. <br>\n**Data collection method by dataset**: Hybrid: Automated, Human <br>\n**Labeling method by dataset**: Hybrid: Automated, Human <br>\n**Properties**: We evaluated with Azure labels from manually selected pages, as well as manual inspection on public PDFs and powerpoint slides.\n\n**Per-class Performance Metrics**:\n| Class  | Average Precision (%) | Average Recall (%) |\n|:-------|:----------------------|:------------------|\n| cell   | 58.365                | 60.647            |\n| row    | 76.992                | 81.115            |\n| column | 85.293                | 87.434            |\n\n## Inference:\n\n**Engine**: TensorRT. <br>\n**Test hardware**: See Support Matrix from NIM documentation.\n\n## Ethical Considerations\n\nNVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.\n\n**For more detailed information on ethical considerations for this model**, please see the Model Card++ [Explainability](explainability.md), [Bias](bias.md), [Safety & Security](safety-security.md), and [Privacy](privacy.md) Subcards.\n\nPlease report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).\n\n## Get Help\n\n### Enterprise Support\nGet access to knowledge base articles and support cases or  submit a ticket at the [NVIDIA AI Enterprise Support Services page.](https://www.nvidia.com/en-us/data-center/products/ai-enterprise-suite/support/).\n\n### NVIDIA NIM Documentation\nVisit the [NeMo Retriever docs page](https://docs.nvidia.com/nemo/retriever/index.html) for release documentation, deployment guides and more.",
    "displayName": "NeMo Retriever Table Structure v1",
    "explainability": "| Field | Response |\n| ----- | ----- |\n| Intended Application & Domain: | Object Detection |\n| Model Type: | YOLOX-architecture for detection of table structure within images of tables. |\n| Intended User: | Enterprise developers, data scientists, and other technical users who need to extract table structure from images. |\n| Output: | A List of dictionaries containing lists of dictionaries of floating point numbers (representing bounding box information). <br> **Example**: `{\"data\": [{\"index\": 0,\"bounding_boxes\": {\"table\": [{\"x_min\": 0.6503,\"y_min\": 0.2161,\"x_max\": 0.7835,\"y_max\": 0.3236,\"confidence\": 0.9306}]}}]}` |\n| Describe how the model works: | Finds and identifies objects in images by first dividing the image into a grid. For each section of the grid, the model uses a series of neural networks to extract visual features and simultaneously predict what objects are present (in this case \"cell\", \"row\", or \"column\") and exactly where they are located in that section, all in a single pass through the image. |\n| Potential Known Risks: | This model does not always guarantee to retrieve the correct table structure for a given image. |\n| Licensing & Terms of Use: | Use of this model is governed by the [NVIDIA AI Foundation Models Community License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-ai-foundation-models-community-license-agreement/). |\n| Technical Limitations | The model may correctly detect table elements, espectially on uncommon table styles or lower quality images. |\n| Name the adversely impacted groups this has been tested to deliver comparable outcomes regardless of: | Not Applicable |\n| Verified to have met prescribed NVIDIA quality standards: | Yes |",
    "framework": "Other",
    "hasPlayground": false,
    "hasSignedVersion": true,
    "isPlaygroundEnabled": false,
    "isPublic": false,
    "isReadOnly": true,
    "labels": [
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        "nvaie:model:nvaie_supported",
        "nvidia_nim:model:nimmcro_nvidia_nim",
        "productNames:nim-dev",
        "productNames:nv-ai-enterprise"
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    "latestVersionIdStr": "rtx6000-blackwell-svx1-trt-fp16-unyitj7ofa",
    "latestVersionSizeInBytes": 277219598,
    "logo": "https://developer-blogs.nvidia.com/wp-content/uploads/2024/03/nemo-retriever-graphic.png",
    "modelFormat": "Triton",
    "name": "nemoretriever-table-structure-v1",
    "orgName": "nim",
    "precision": "FP32",
    "privacy": "| Field | Response |\n| ----- | ----- |\n| Generatable or reverse engineerable personal data? | No |\n| Personal data used to create this model? | None |\n| How often is the dataset reviewed? | Before Every Release |\n| Is a mechanism in place to honor data subject right of access or deletion of personal data? | No |\n| Is there provenance for all datasets used in training? | Yes |\n| Does data labeling (annotation, metadata) comply with privacy laws? | Yes |",
    "productNames": [
        "nim-dev",
        "nv-ai-enterprise"
    ],
    "publicDatasetUsed": {},
    "publisher": "NVIDIA",
    "safetyAndSecurity": "| Field | Response |\n| ----- | ----- |\n| Model Application(s): | Object Detection for Retrieval, focused on Enterprise |\n| Describe the physical safety impact (if present). | Not Applicable |\n| Use Case Restrictions: | Abide by [NVIDIA AI Foundation Models Community License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-community-models-license/).   |\n| Model and dataset restrictions: | The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. Restrictions enforce dataset access during training, and dataset license constraints adhered to. |",
    "shortDescription": "NVIDIA NeMo\u2122 Retriever NIM for table structure v1 is a fine-tuned object detection model, trained specifically for detecting the structure of complex tables.",
    "teamName": "nvidia",
    "updatedDate": "2025-10-15T18:44:05.845Z"
} source: URL: https://catalog.ngc.nvidia.com/orgs/nim/teams/nvidia/containers/nemoretriever-table-structure-v1 optimizationProfiles: - profileId: nim/nvidia/nemoretriever-table-structure-v1:a10gx1-trt-fp16-ncblfgrrew framework: TensorRT-LLM displayName: Nemoretriever Table Structure V1 A10Gx1 FP16 ngcMetadata: 09231248dff89cf8859d9206931342e468fbddfe469df56334fbe00df7fda1da: model: nvidia/nemoretriever-table-structure-v1 release: 1.6.0 tags: backend: triton compute_capability: '8.6' model_type: tensorrt precision: fp16 modelFormat: trt-llm spec: - key: PRECISION value: FP16 - key: GPU value: A10G - key: COUNT value: 1 - key: NIM VERSION value: 1.6.0 - key: DOWNLOAD SIZE value: 1GB - key: BACKEND value: TRITON - key: MODEL TYPE value: TENSORRT - profileId: nim/nvidia/nemoretriever-table-structure-v1:b200x1-trt-fp16--ce2boy2vw framework: TensorRT-LLM displayName: Nemoretriever Table Structure V1 B200x1 FP16 ngcMetadata: 0f3b150544da8a053048c1e2a37a282b2c43f09a99253882578d12bc1f2cfca6: model: nvidia/nemoretriever-table-structure-v1 release: 1.6.0 tags: backend: triton compute_capability: '10.0' model_type: tensorrt precision: fp16 modelFormat: trt-llm spec: - key: PRECISION value: FP16 - key: GPU value: B200 - key: COUNT value: 1 - key: NIM VERSION value: 1.6.0 - key: DOWNLOAD SIZE value: 1GB - key: BACKEND value: TRITON - key: MODEL TYPE value: TENSORRT - profileId: nim/nvidia/nemoretriever-table-structure-v1:h100x1-trt-fp16-lnq0nmbo3g framework: TensorRT-LLM displayName: Nemoretriever Table Structure V1 H100x1 FP16 ngcMetadata: 58adeef41afa742e753314ae51818e9f017f2c92ba0bfdc01befe6234703a54c: model: nvidia/nemoretriever-table-structure-v1 release: 1.6.0 tags: backend: triton compute_capability: '9.0' model_type: tensorrt precision: fp16 modelFormat: trt-llm spec: - key: PRECISION value: FP16 - key: GPU value: H100 - key: COUNT value: 1 - key: NIM VERSION value: 1.6.0 - key: DOWNLOAD SIZE value: 1GB - key: BACKEND value: TRITON - key: MODEL TYPE value: TENSORRT - profileId: nim/nvidia/nemoretriever-table-structure-v1:rtx6000-blackwell-svx1-trt-fp16-unyitj7ofa framework: TensorRT-LLM displayName: Nemoretriever Table Structure V1 RTX6000x1 FP16 ngcMetadata: b4cc2f8b3d2dcf1afdcafbee8ea694c53aeee642d76f709ed0e79477b68a8dde: model: nvidia/nemoretriever-table-structure-v1 release: 1.6.0 tags: backend: triton compute_capability: '12.0' model_type: tensorrt precision: fp16 modelFormat: trt-llm spec: - key: PRECISION value: FP16 - key: GPU value: RTX6000 - key: COUNT value: 1 - key: NIM VERSION value: 1.6.0 - key: DOWNLOAD SIZE value: 1GB - key: BACKEND value: TRITON - key: MODEL TYPE value: TENSORRT - profileId: nim/nvidia/nemoretriever-table-structure-v1:l40sx1-trt-fp16-ddoabmkana framework: TensorRT-LLM displayName: Nemoretriever Table Structure V1 L40Sx1 FP16 ngcMetadata: bc1487bf0ec3430f17595fff029c1bc50668344c7a30f9e5d64ee061c6e2d5fa: model: nvidia/nemoretriever-table-structure-v1 release: 1.6.0 tags: backend: triton compute_capability: '8.9' model_type: tensorrt precision: fp16 modelFormat: trt-llm spec: - key: PRECISION value: FP16 - key: GPU value: L40S - key: COUNT value: 1 - key: NIM VERSION value: 1.6.0 - key: DOWNLOAD SIZE value: 1GB - key: BACKEND value: TRITON - key: MODEL TYPE value: TENSORRT - profileId: nim/nvidia/nemoretriever-table-structure-v1:a100x1-onnx-fp16-l8hnwsbr3g framework: ONNX displayName: Nemoretriever Table Structure V1 ONNX FP16 ngcMetadata: edc693c6fccd68d266622eace04225421e353d7ce31e3b207afc5ff35124127b: model: nvidia/nemoretriever-table-structure-v1 release: 1.6.0 tags: backend: triton model_type: onnx precision: fp16 modelFormat: onnx spec: - key: PRECISION value: FP16 - key: COUNT value: 1 - key: NIM VERSION value: 1.6.0 - key: DOWNLOAD SIZE value: 1GB - key: BACKEND value: TRITON - key: MODEL TYPE value: ONNX - profileId: nim/nvidia/nemoretriever-table-structure-v1:a100x1-trt-fp16-jvvssvik-q framework: TensorRT-LLM displayName: Nemoretriever Table Structure V1 A100x1 FP16 ngcMetadata: f0fb2f72a66230096c40fc3307872ebb9bce69816cbfc6e2918695ca824bd284: model: nvidia/nemoretriever-table-structure-v1 release: 1.6.0 tags: backend: triton compute_capability: '8.0' model_type: tensorrt precision: fp16 modelFormat: trt-llm spec: - key: PRECISION value: FP16 - key: GPU value: A100 - key: COUNT value: 1 - key: NIM VERSION value: 1.6.0 - key: DOWNLOAD SIZE value: 1GB - key: BACKEND value: TRITON - key: MODEL TYPE value: TENSORRT labels: - signed images - NSPECT-7OBP-T77C - NVIDIA AI Enterprise Supported - NVIDIA NIM config: architectures: - Other modelType: NIM license: NVIDIA AI Foundation Models Community License - name: PaddleOCR displayName: PaddleOCR modelHubID: paddleocr category: Retrieval type: NGC description: Ultra-lightweight OCR system developed by Baidu with broad support for diverse OCR algorithms. Covers the full pipeline from text detection through recognition across document types. requireLicense: true licenseAgreements: - label: Use Policy url: https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-community-models-license/ - label: License Agreement url: https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-license-agreement/ modelVariants: - variantId: PaddleOCR modelCard: 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Supports dynamic embedding sizing to significantly reduce storage footprint. requireLicense: true licenseAgreements: - label: Use Policy url: https://llama.meta.com/llama3/use-policy/ - label: License Agreement url: https://llama.meta.com/llama3/license/ modelVariants: - variantId: Llama 3.2 NV EmbedQA 1b V2 modelCard: {
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    "description": "## **Model Overview**\n\n### **Description**\n\nThe Llama 3.2 NeMo Retriever Embedding 1B model is optimized for **multilingual and cross-lingual** text question-answering retrieval with **support for long documents (up to 8192 tokens) and dynamic embedding size (Matryoshka Embeddings)**. This model was evaluated on 26 languages: English, Arabic, Bengali, Chinese, Czech, Danish, Dutch, Finnish, French, German, Hebrew, Hindi, Hungarian, Indonesian, Italian, Japanese, Korean, Norwegian, Persian, Polish, Portuguese, Russian, Spanish, Swedish, Thai, and Turkish.\n\nIn addition to enabling multilingual and cross-lingual question-answering retrieval, this model reduces the data storage footprint by 35x through dynamic embedding sizing and support for longer token length, making it feasible to handle large-scale datasets efficiently.\n\nAn embedding model is a crucial component of a text retrieval system, as it transforms textual information into dense vector representations. They are typically transformer encoders that process tokens of input text (for example: question, passage) to output an embedding.\n\nThis model is ready for commercial use.\n\nThe Llama 3.2 NeMo Retriever Embedding 1B model is a part of the NVIDIA NeMo Retriever collection of NIM, which provide state-of-the-art, commercially-ready models and microservices, optimized for the lowest latency and highest throughput. It features a production-ready information retrieval pipeline with enterprise support. The models that form the core of this solution have been trained using responsibly selected, auditable data sources. With multiple pre-trained models available as starting points, developers can also readily customize them for domain-specific use cases, such as information technology, human resource help assistants, and research & development research assistants.\n\n### **Intended use**\n\nThe Llama 3.2 NeMo Retriever Embedding 1B model is most suitable for users who want to build a multilingual question-and-answer application over a large text corpus, leveraging the latest dense retrieval technologies.\n\n### **License/Terms of use**\n\nThe use of this model is governed by the [NVIDIA AI Foundation Models Community License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-community-models-license/) and Llama 3.2 is licensed under the [Llama 3.2 Community License](https://www.llama.com/llama3_2/license/), Copyright \u00a9 Meta Platforms, Inc. All Rights Reserved.\n\n**You are responsible for ensuring that your use of NVIDIA AI Foundation Models complies with all applicable laws.**\n\n### **Release Date:  \n\nBuild.Nvidia.com: March 17, 2025 via build.nvidia.com/nvidia/llama-3_2-nv-embedqa-1b-v2 <br>\nOriginal NGC Release: March 17, 2025 <br>\nLast Updated NGC Release: July 11, 2025 <br>\n\n### **Deployment Geography**\nGlobal <br>\n\n\n### **Model Architecture**\n\n**Architecture Type:** Transformer<br>\n**Network Architecture:** Fine-tuned Llama3.2 1B Retriever<br>\n\nThis NeMo Retriever embedding model is a transformer encoder - a fine-tuned version of Llama3.2 1b, with 16 layers and an embedding size of 2048, which is trained on public datasets. The AdamW optimizer is employed incorporating 100 warm up steps and 5e-6 learning rate with WarmupDecayLR scheduler. Embedding models for text retrieval are typically trained using a bi-encoder architecture. This involves encoding a pair of sentences (for example, query and chunked passages) independently using the embedding model. Contrastive learning is used to maximize the similarity between the query and the passage that contains the answer, while minimizing the similarity between the query and sampled negative passages not useful to answer the question.\n\n### **Input**\n\n**Input Type:** Text<br>\n**Input Format:** List of strings<br>\n**Input Parameter:** 1D<br>\n**Other Properties Related to Input:** The model's maximum context length is 8192 tokens. Texts longer than maximum length must either be chunked or truncated.<br>\n\n### **Output**\n\n**Output Type:** Floats<br>\n**Output Format:** List of float arrays<br>\n**Output:** Model outputs embedding vectors of maximum dimension 2048 for each text string (can be configured based on 384, 512, 768, 1024, or 2048).<br>\n**Other Properties Related to Output:** N/A<br>\n\nOur AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA\u2019s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.\n\n### **Software Integration**\n\n**Runtime Engine:** NeMo Retriever embedding NIM<br>\n**Supported Hardware Microarchitecture Compatibility**: NVIDIA Ampere, NVIDIA Hopper, NVIDIA Lovelace<br>\n**Supported Operating System(s):** Linux<br>\n\n### **Model Version(s)**\n\nLlama 3.2 NeMo Retriever Embedding 1B v2<br>\nShort Name: llama-3.2-nv-embedqa-1b-v2<br>\n\n## **Training Dataset & Evaluation**\n\n### **Training Dataset**\n\nThe development of large-scale public open-QA datasets has enabled tremendous progress in powerful embedding models. However, one popular dataset named MS MARCO restricts \u200ccommercial licensing, limiting the use of these models in commercial settings. To address this, NVIDIA created its own training dataset blend based on public QA datasets, which each have a license for commercial applications.\n\n**Data Collection Method by dataset**: Hybrid: Automated, Human, Synthetic\n\n\n**Labeling Method by dataset**: Hybrid: Automated, Human, Synthetic\n\n\n**Properties:** Semi-supervised pre-training on 12M samples from public datasets and fine-tuning on 1M samples from public datasets.\n\n\n### **Evaluation Results**\n\nProperties: We evaluated the NeMo Rtriever embdding model in comparison to literature open & commercial retriever models on academic benchmarks for question-answering - [NQ](https://huggingface.co/datasets/BeIR/nq), [HotpotQA](https://huggingface.co/datasets/hotpot_qa) and [FiQA (Finance Q\\&A)](https://huggingface.co/datasets/BeIR/fiqa) from BeIR benchmark and TechQA dataset. Note that the model was evaluated offline on A100 GPUs using the model's PyTorch checkpoint.  In this benchmark, the metric used was Recall@5.\n\n| Open & Commercial Retrieval Models | Average Recall@5 on NQ, HotpotQA, FiQA, TechQA dataset |\n| ----- | ----- |\n| llama-3.2-nv-embedqa-1b-v2 (embedding dim 2048) | 68.60% |\n| llama-3.2-nv-embedqa-1b-v2 (embedding dim 384) | 64.48% |\n| llama-3.2-nv-embedqa-1b-v1 (embedding dim 2048) | 68.97% |\n| nv-embedqa-mistral-7b-v2 | 72.97% |\n| nv-embedqa-mistral-7B-v1 | 64.93% |\n| nv-embedqa-e5-v5 | 62.07% |\n| nv-embedqa-e5-v4 | 57.65% |\n| e5-large-unsupervised | 48.03% |\n| BM25 | 44.67%  |\n\nWe evaluated the multilingual capabilities on the academic benchmark [MIRACL](https://github.com/project-miracl/miracl) across 15 languages and translated the English and Spanish version of MIRACL into additional 11 languages. The reported scores are based on an internal version of MIRACL by selecting hard negatives for each query to reduce the corpus size.\n\n| Open & Commercial Retrieval Models | Average Recall@5 on multilingual |\n| ----- | ----- |\n| llama-3.2-nv-embedqa-1b-v2 (embedding dim 2048) | 60.75% |\n| llama-3.2-nv-embedqa-1b-v2 (embedding dim 384) | 58.62% |\n| llama-3.2-nv-embedqa-1b-v1 | 60.07% |\n| nv-embedqa-mistral-7b-v2 | 50.42% |\n| BM25 | 26.51% |\n\nWe evaluated the cross-lingual capabilities on the academic benchmark [MLQA](https://github.com/facebookresearch/MLQA/) based on 7 languages (Arabic, Chinese, English, German, Hindi, Spanish, Vietnamese). We consider only evaluation datasets when the query and documents are in different languages. We calculate the average Recall@5 across the 42 different language pairs.\n\n| Open & Commercial Retrieval Models | Average Recall@5 on MLQA dataset with different languages |\n| ----- | ----- |\n| llama-3.2-nv-embedqa-1b-v2 (embedding dim 2048) | 79.86% |\n| llama-3.2-nv-embedqa-1b-v2 (embedding dim 384) | 71.61% |\n| llama-3.2-nv-embedqa-1b-v1 (embedding dim 2048) | 78.77% |\n| nv-embedqa-mistral-7b-v2 | 68.38% |\n| BM25 | 13.01% |\n\nWe evaluated the support of long documents on the academic benchmark [Multilingual Long-Document Retrieval (MLDR)](https://huggingface.co/datasets/Shitao/MLDR) built on Wikipedia and mC4, covering 12 typologically diverse languages. The English version has a median length of 2399 tokens and 90th percentile of 7483 tokens using the llama 3.2 tokenizer. The MLDR dataset is based on synthetic generated questions with a LLM, which has the tendency to create questions with similar keywords than the positive document, but might not be representative for real user queries. This characteristic of the dataset benefits sparse embeddings like BM25.\n\n| Open & Commercial Retrieval Models | Average Recall@5 on MLDR |\n| ----- | ----- |\n| llama-3.2-nv-embedqa-1b-v2 (embedding dim 2048) | 59.55% |\n| llama-3.2-nv-embedqa-1b-v2 (embedding dim 384) | 54.77% |\n| llama-3.2-nv-embedqa-1b-v1 (embedding dim 2048) | 60.49% |\n| nv-embedqa-mistral-7b-v2 | 43.24% |\n| BM25 | 71.39% |\n\n**Data Collection Method by dataset**: Hybrid: Automated, Human, Synthetic\n\n**Labeling Method by dataset:** Hybrid: Automated, Human, Synthetic\n\n**Properties:** The evaluation datasets are based on [MTEB/BEIR](https://github.com/beir-cellar/beir), TextQA, TechQA, [MIRACL](https://github.com/project-miracl/miracl), [MLQA](https://github.com/facebookresearch/MLQA), and [MLDR](https://huggingface.co/datasets/Shitao/MLDR). The size ranges between 10,000s up to 5M depending on the dataset.\n\n**Inference**<br>\n**Engine:** TensorRT<br>\n**Test Hardware:** H100 PCIe/SXM, A100 PCIe/SXM, L40s, L4, and A10G<br>\n\n## **Ethical Considerations**\n\nNVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their supporting model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.\n\nFor more detailed information on ethical considerations for this model, please see the Model Card++ tab for the Explainability, Bias, Safety & Security, and Privacy subcards.\n\nPlease report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).",
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    "explainability": "| Field | Response |\n| ----- | ----- |\n| Intended Application & Domain: | Passage and query embedding for question and answer retrieval |\n| Model Type: | Transformer encoder |\n| Intended User: | Generative AI creators working with conversational AI models - users who want to build a multilingual question and answer application over a large text corpus, leveraging the latest dense retrieval technologies. |\n| Output: | Array of float numbers (Dense Vector Representation for the input text) |\n| Describe how the model works: | Model transforms the tokenized input text into a dense vector representation. |\n| Performance Metrics: | Accuracy, Throughput, and Latency |\n| Potential Known Risks: | This model does not always guarantee to retrieve the correct passage(s) for a given query. |\n| Licensing & Terms of Use: | The use of this model is governed by the [NVIDIA AI Foundation Models Community License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-community-models-license/) and Llama 3.2 is licensed under the [Llama 3.2 Community License](https://www.llama.com/llama3_2/license/), Copyright \u00a9 Meta Platforms, Inc. All Rights Reserved. |\n| Technical Limitations | The model\u2019s max sequence length is 8192. Therefore, the longer text inputs should be truncated.   |\n| Name the adversely impacted groups this has been tested to deliver comparable outcomes regardless of: | N/A |\n| Verified to have met prescribed NVIDIA quality standards: | Yes |",
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    "privacy": "| Field | Response |\n| ----- | ----- |\n| Generatable or reverse engineerable personal data? | None |\n| Personal data used to create this model? | None |\n| How often is dataset reviewed? | Dataset is initially reviewed upon addition, and subsequent reviews are conducted as needed or upon request for changes. |\n| Is there provenance for all datasets used in training? | Yes |\n| Does data labeling (annotation, metadata) comply with privacy laws? | Yes |\n| Is data compliant with data subject requests for data correction or removal, if such a request was made? | No, not possible with externally-sourced data. |\n| Applicable Privacy Policy | https://www.nvidia.com/en-us/about-nvidia/privacy-policy/ |",
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    "safetyAndSecurity": "| Field | Response |\n| ----- | ----- |\n| Model Application(s): | Text Embedding for Retrieval |\n| Use Case Restrictions: | Abide by [NVIDIA AI Foundation Models Community License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-community-models-license/).   |\n| Model and dataset restrictions: | The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. Restrictions enforce dataset access during training, and dataset license constraints adhered to. |\n| Describe the life critical impact (if present): | Not applicable. |",
    "shortDescription": "World-class multilingual and cross-lingual question-answering retrieval.",
    "teamName": "nvidia",
    "updatedDate": "2025-08-29T18:01:57.279Z"
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    "bias": "| Field | Response |\n| ----- | ----- |\n| Participation considerations from adversely impacted groups [protected classes](https://www.senate.ca.gov/content/protected-classes) in model design and testing | None |\n| Measures taken to mitigate against unwanted bias | None |",
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    "description": "## **Model Overview**\n\n### **Description**\n\nThe Llama 3.2 NeMo Retriever Reranking 1B model is optimized for providing a logit score that represents how relevant a document(s) is to a given query. The model was fine-tuned for **multilingual, cross-lingual** text question-answering retrieval, with support for **long documents (up to 8192 tokens)**.  This model was evaluated on 26 languages: English, Arabic, Bengali, Chinese, Czech, Danish, Dutch, Finnish, French, German, Hebrew, Hindi, Hungarian, Indonesian, Italian, Japanese, Korean, Norwegian, Persian, Polish, Portuguese, Russian, Spanish, Swedish, Thai, and Turkish.\n\n\nThis model is a component in a text retrieval system to improve the overall accuracy. A text retrieval system often uses an embedding model (dense) or lexical search (sparse) index to return relevant text passages given the input. A reranking model can be used to rerank the potential candidate into a final order. The reranking model has the question-passage pairs as an input and therefore, can process cross attention between the words. It\u2019s not feasible to apply a Ranking model on all documents in the knowledge base, therefore, ranking models are often deployed in combination with embedding models.\n\n\nThis model is ready for commercial use.\n\n\nThe Llama 3.2 NeMo Retriever Reranking 1B model is a part of the NeMo Retriever collection of NIM, which provide state-of-the-art, commercially-ready models and microservices, optimized for the lowest latency and highest throughput. It features a production-ready information retrieval pipeline with enterprise support. The models that form the core of this solution have been trained using responsibly selected, auditable data sources. With multiple pre-trained models available as starting points, developers can also readily customize them for their domain-specific use cases, such as information technology, human resource help assistants, and research & development research assistants.\n\n\n### **License/Terms of use**\n\nThe use of this model is governed by the [NVIDIA AI Foundation Models Community License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-community-models-license/)  and Llama 3.2 is licensed under the [Llama 3.2 Community License](https://www.llama.com/llama3_2/license/), Copyright \u00a9 Meta Platforms, Inc. All Rights Reserved.\n\n**You are responsible for ensuring that your use of NVIDIA AI Foundation Models complies with all applicable laws.**\n\n### **Intended use**\n\nThe Llama 3.2 NeMo Retriever Reranking 1B model is most suitable for users who want to improve their multilingual retrieval tasks by reranking a set of candidates for a given question.\n\n### **Model Architecture: Llama-3.2 1B Ranker**\n\n**Architecture Type:** Transformer\n**Network Architecture:** Fine-tuned meta-llama/Llama-3.2-1B\n\nThe Llama 3.2 NeMo Retriever Reranking 1B model is a transformer encoder fine-tuned for contrastive learning. We employ bi-directional attention when fine-tuning for higher accuracy. The last embedding output by the decoder model is used with a mean pooling strategy, and a binary classification head is fine-tuned for the ranking task.\n\nRanking models for text ranking are typically trained as a cross-encoder for sentence classification. This involves predicting the relevancy of a sentence pair (for example, question and chunked passages). The CrossEntropy loss is used to maximize the likelihood of passages containing information to answer the question and minimize the likelihood for (negative) passages that do not contain information to answer the question.\n\nWe trained the model on public datasets described in the Dataset and Training section.\n\n### **Input**\n\n**Input Type:** Pair of Texts\n**Input Format:** List of text pairs\n**Input Parameters:** 1D\n**Other Properties Related to Input:** The model was trained on question and answering over text documents from multiple languages. It was evaluated to work successfully with up to a sequence length of 8192 tokens. Longer texts are recommended to be either chunked or truncated.\n\n**Output**\n**Output Type:** Floats\n**Output Format:** List of floats\n**Output Parameters:** 1D\n**Other Properties Related to Output:** Each the probability score (or raw logits). Users can decide to implement a Sigmoid activation function applied to the logits in their usage of the model.\n\n### **Software Integration**\n\n**Runtime:** Llama 3.2 NeMo Retriever Reranking 1B NIM\n**Supported Hardware Microarchitecture Compatibility**: NVIDIA Ampere, NVIDIA Hopper, NVIDIA Lovelace\n**Supported Operating System(s):** Linux\n\n### **Model Version(s)**\n\nLlama 3.2 NeMo Retriever Reranking 1B\nShort Name: llama-3.2-nv-rerankqa-1b-v2\n\n## **Training Dataset & Evaluation**\n\n### **Training Dataset**\n\nThe development of large-scale public open-QA datasets has enabled tremendous progress in powerful embedding models. However, one popular dataset named [MSMARCO](https://microsoft.github.io/msmarco/) restricts \u200ccommercial licensing, limiting the use of these models in commercial settings. To address this, NVIDIA created its own training dataset blend based on public QA datasets, which each have a license for commercial applications.\n\n**Data Collection Method by dataset**: Automated, Unknown\n\n**Labeling Method by dataset:** Automated, Unknown\n\n**Properties:** This model was trained on 800k samples from public datasets.\n\n### **Evaluation Results**\n\nWe evaluate the pipelines on a set of evaluation benchmarks. We applied the ranking model to the candidates retrieved from a retrieval embedding model.\n\nOverall, the pipeline llama-3.2-nv-embedqa-1b-v2 + llama-3.2-nv-rerankqa-1b-v2 provides high BEIR+TechQA accuracy with multilingual and crosslingual support. The llama-3.2-nv-rerankqa-1B-v2  ranking model is 3.5x smaller than the nv-rerankqa-mistral-4b-v3 model.\n\nWe evaluated the NVIDIA Retrieval QA Embedding Model in comparison to literature open & commercial retriever models on academic benchmarks for question-answering \\- [NQ](https://huggingface.co/datasets/BeIR/nq), [HotpotQA](https://huggingface.co/datasets/hotpot_qa) and [FiQA (Finance Q\\&A)](https://huggingface.co/datasets/BeIR/fiqa) from BeIR benchmark and TechQA dataset. In this benchmark, the metric used was Recall@5. As described, we need to apply the ranking model on the output of an embedding model.\n\n| Open & Commercial Reranker Models | Average Recall@5 on NQ, HotpotQA, FiQA, TechQA dataset |\n| ----- | ----- |\n| llama-3.2-nv-embedqa-1b-v2 + llama-3.2-nv-rerankqa-1b-v2 | 73.64% |\n| llama-3.2-nv-embedqa-1b-v2 | 68.60% |\n| nv-embedqa-e5-v5 \\+ nv-rerankQA-mistral-4b-v3 | 75.45% |\n| nv-embedqa-e5-v5 | 62.07% |\n| nv-embedqa-e5-v4 | 57.65% |\n| e5-large\\_unsupervised | 48.03% |\n| BM25 | 44.67% |\n\nWe evaluated the model\u2019s multilingual capabilities on the [MIRACL](https://github.com/project-miracl/miracl) academic benchmark \\- a multilingual retrieval dataset, across 15 languages, and on an additional 11 languages that were translated from the English and Spanish versions of MIRACL. The reported scores are based on a custom subsampled version by selecting hard negatives for each query to reduce the corpus size.\n\n| Open & Commercial Retrieval Models | Average Recall@5 on MIRACL multilingual datasets |\n| :---- | :---- |\n| llama-3.2-nv-embedqa-1b-v2 + llama-3.2-nv-rerankqa-1b-v2 | 65.80% |\n| llama-3.2-nv-embedqa-1b-v2 | 60.75% |\n| nv-embedqa-mistral-7b-v2 | 50.42% |\n| BM25 | 26.51% |\n\nWe evaluated the cross-lingual capabilities on the academic benchmark [MLQA](https://github.com/facebookresearch/MLQA/) based on 7 languages (Arabic, Chinese, English, German, Hindi, Spanish, Vietnamese). We consider only evaluation datasets when the query and documents are in different languages. We calculate the average Recall@5 across the 42 different language pairs.\n\n| Open & Commercial Retrieval Models | Average Recall@5 on MLQA dataset with different languages |\n| :---- | :---- |\n| llama-3.2-nv-embedqa-1b-v2 + llama-3.2-nv-rerankqa-1b-v2 | 86.83% |\n| llama-3.2-nv-embedqa-1b-v2 | 79.86% |\n| nv-embedqa-mistral-7b-v2 | 68.38% |\n| BM25 | 13.01% |\n\nWe evaluated the support of long documents on the academic benchmark [Multilingual Long-Document Retrieval (MLDR)](https://huggingface.co/datasets/Shitao/MLDR) built on Wikipedia and mC4, covering 12 typologically diverse languages . The English version has a median length of 2399 tokens and 90th percentile of 7483 tokens using the llama 3.2 tokenizer.\n\n| Open & Commercial Retrieval Models | Average Recall@5 on MLDR |\n| :---- | :---- |\n| llama-3.2-nv-embedqa-1b-v2 + llama-3.2-nv-rerankqa-1b-v2 | 70.69% |\n| llama-3.2-nv-embedqa-1b-v2 | 59.55% |\n| nv-embedqa-mistral-7b-v2 | 43.24% |\n| BM25 | 71.39% |\n\n**Data Collection Method by dataset**:\nUnknown\n\n**Labeling Method by dataset:**\nUnknown\n\n**Properties**\nThe evaluation datasets are based on three [MTEB/BEIR](https://github.com/beir-cellar/beir) TextQA datasets, the TechQA dataset, and MIRACL multilingual retrieval datasets, which are all public datasets. The sizes range between 10,000s up to 5M depending on the dataset.\n\n**Inference**\n**Engine:** TensorRT\n**Test Hardware:**  H100 PCIe/SXM, A100 PCIe/SXM, L40s, L4, and A10G\n\n## **Ethical Considerations**\n\nNVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their supporting model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.\n\nFor more detailed information on ethical considerations for this model, please see the Model Card++ tab for the Explainability, Bias, Safety & Security, and Privacy subcards.\n\nPlease report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).\n\n## Get Help\n\n### Enterprise Support\nGet access to knowledge base articles and support cases or  submit a ticket at the [NVIDIA AI Enterprise Support Services page.](https://www.nvidia.com/en-us/data-center/products/ai-enterprise-suite/support/).\n\n### NVIDIA NIM Documentation\nVisit the [NeMo Retriever docs page](https://docs.nvidia.com/nemo/retriever/index.html) for release documentation, deployment guides and more.",
    "displayName": "Llama 3.2 NeMo Retriever Reranking 1B",
    "explainability": "| Field | Response |\n| ----- | ----- |\n| Intended Application & Domain: | Passage ranking for question and answer retrieval. |\n| Model Type: | Transformer encoder |\n| Intended User: | Generative AI creators working with conversational AI models - most suitable for users who want to improve their multilingual retrieval tasks by reranking a set of candidates for a given question. |\n| Output: | List of Floats (Score/Logit indicating if a passage relevant to a question) |\n| Describe how the model works: | Model provides a score about the likelihood the passage contains the information to answer the question. |\n| Verified to have met prescribed quality standards: | Yes |\n| Performance Metrics: | Accuracy, Throughput, and Latency |\n| Potential Known Risks: | This model does not always guarantee to provide a meaningful ranking of passage(s) for a given question. |\n| Licensing: | The use of this model is governed by the [NVIDIA AI Foundation Models Community License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-community-models-license/)  and Llama 3.2 is licensed under the [Llama 3.2 Community License](https://www.llama.com/llama3_2/license/), Copyright \u00a9 Meta Platforms, Inc. All Rights Reserved. |\n| Technical Limitations | The model\u2019s max sequence length is 8192. Therefore, the longer text inputs should be truncated. |",
    "framework": "Other",
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    "hasSignedVersion": true,
    "isPlaygroundEnabled": false,
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    "isReadOnly": true,
    "labels": [
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        "nvaie:model:nvaie_supported",
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    "logo": "https://developer-blogs.nvidia.com/wp-content/uploads/2024/03/nemo-retriever-graphic.png",
    "modelFormat": "N/A",
    "name": "llama-3.2-nv-rerankqa-1b-v2",
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    "privacy": "| Field | Response |\n| ----- | ----- |\n| Generatable or reverse engineerable personally-identifiable information (PII)? | None |\n| Was consent obtained for any personal data used? | Not Applicable |\n| Personal data used to create this model? | None |\n| How often is the dataset reviewed? | Before Every Release |\n| Is a mechanism in place to honor data subject right of access or deletion of personal data? | N/A |\n| If personal data was collected for the development of the model, was it collected directly by NVIDIA? | Not Applicable |\n| If personal data was collected for the development of the model by NVIDIA, do you maintain or have access to disclosures made to data subjects? | Not Applicable |\n| If personal data collected for the development of this AI model, was it minimized to only what was required? | Not Applicable |\n| Is there provenance for all datasets used in training? | Yes |\n| Does data labeling (annotation, metadata) comply with privacy laws? | Yes |\n| Is data compliant with data subject requests for data correction or removal, if such a request was made? | No, not possible with externally-sourced data. |",
    "productNames": [
        "nim-dev",
        "nv-ai-enterprise"
    ],
    "publicDatasetUsed": {},
    "publisher": "NVIDIA",
    "safetyAndSecurity": "| Field | Response |\n| ----- | ----- |\n| Model Application(s): | Text Reranking for Retrieval |\n| Describe the physical safety impact (if present). | Not Applicable |\n| Use Case Restrictions: | Abide by [NVIDIA AI Foundation Models Community License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-community-models-license/).   |\n| Model and dataset restrictions: | The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. Restrictions enforce dataset access during training, and dataset license constraints adhered to. |",
    "shortDescription": "GPU-accelerated model optimized for providing a probability score that a given passage contains the information to answer a question.",
    "teamName": "nvidia",
    "updatedDate": "2025-09-03T21:30:35.281Z"
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key: PRECISION value: FP16 - key: GPU value: H100-HBM3-80GB - key: COUNT value: 1 - key: NIM VERSION value: 1.8.0 - key: DOWNLOAD SIZE value: 5GB - key: BACKEND value: TENSORRT - key: MODEL TYPE value: TENSORRT - profileId: nim/nvidia/llama-3.2-nv-rerankqa-1b-v2:onnx-precision.fp16-d03bf375 framework: ONNX displayName: Llama 3.2 NV Rerankqa 1B V2 ONNX FP16 ngcMetadata: f7391ddbcb95b2406853526b8e489fedf20083a2420563ca3e65358ff417b10f: model: nvidia/llama-3.2-nv-rerankqa-1b-v2 release: 1.8.0 tags: backend: onnx model_type: onnx precision: fp16 tp: '1' modelFormat: onnx spec: - key: PRECISION value: FP16 - key: COUNT value: 1 - key: NIM VERSION value: 1.8.0 - key: DOWNLOAD SIZE value: 3GB - key: BACKEND value: ONNX - key: MODEL TYPE value: ONNX labels: - Llama - Meta - Chat - NIM - Large Language Model - NVIDIA Validated config: architectures: - Other modelType: llama license: NVIDIA AI Foundation Models Community License - name: Riva ASR Whisper Large v3 displayName: Riva ASR Whisper Large v3 modelHubID: riva-asr-whisper-large-v3 category: Speech type: NGC description: Automatic speech recognition and speech translation model trained on 680K hours of labeled audio. 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    "description": "## Model Overview\n\n### Description:\n\nBoltz-2 NIM is a next-generation structural biology foundation model that shows strong performance for both structure and affinity prediction. Boltz-2 is the first deep learning model to approach the accuracy of free energy perturbation (FEP) methods in predicting binding affinities of small molecules and proteins\u2014achieving strong correlations on benchmarks while being nearly 1000\u00d7 more computationally efficient. Note that binding affinity is not yet available in the NIM, but will be available very soon!\n<br>\nKey Features: <br>\n **Trunk optimizations:** Mixed-precision (bfloat16) and trifast triangle attention cut runtime/memory; enables training with 768-token crops (as in AlphaFold3). <br>\n**Physical quality:** Integrates Boltz-steering at inference (Boltz-2x) to reduce steric clashes and stereochemistry errors without losing accuracy. <br>\n**Controllability:** <br>\n* **Method conditioning:** Steers predictions to resemble X-ray, NMR, or MD-style structures. <br>\n* **Template conditioning + steering:** Uses single or multimeric templates; supports strict template enforcement or soft guidance. <br>\n* **Contact/pocket conditioning:** Accepts distance constraints from experiments or expert priors. <br>\n\n**Affinity module:** PairFormer refines protein\u2013ligand and intra-ligand interactions; predicts both binding likelihood and a continuous affinity on log \u00b5M scale (trained on mixed Ki, Kd, IC50). Output is an IC50-like measure suitable for ranking. <br>\n**Key advances vs Boltz-1/1x:** Faster/more memory-efficient trunk, improved physical plausibility via integrated steering, markedly enhanced controllability, and added affinity prediction head. <br>\n\n\nThis NIM is ready for commercial use.\n<br>\n\n### Third-Party Community Consideration\n\nThis model is not owned or developed by NVIDIA. This model has been developed and built to a third-party\u2019s requirements for this application and use case.\n\n#### License / Terms of Use\n\nGOVERNING TERMS: This trial service is governed by the [NVIDIA API Trial Terms of Service](https://assets.ngc.nvidia.com/products/api-catalog/legal/NVIDIA%20API%20Trial%20Terms%20of%20Service.pdf). Use of this model is governed by the [NVIDIA Community Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-community-models-license/). Additional Information: [MIT](https://github.com/jwohlwend/boltz?tab=MIT-1-ov-file#readme).\n\n\n**You are responsible for ensuring that your use of NVIDIA AI Foundation Models complies with all applicable laws.**\n\n### Deployment Geography\nGlobal\n\n### Use Case\nBoltz-2 NIM enables researchers and commercial entities in the Drug Discovery, Life Sciences, and Digital Biology fields to predict the three-dimensional structure of biomolecular complexes and predict small-molecule binding affinities. Trained on millions of curated experimental datapoints with a novel training strategy tailored for noisy biochemical assay data, Boltz-2 demonstrates robust performance across hit-discovery, hit-to-lead, and lead optimization.\n\n### Release Date\nbuild.nvidia.com September 30, 2025 via [build.nvidia.com](https://build.nvidia.com/mit/boltz2)\n\nNGC September 30, 2025\n\n### References:\n\n```\n@article{wohlwend2024boltz,\n    title = {Boltz-1: Democratizing Biomolecular Interaction Modeling},\n    author = {Wohlwend, Jeremy and Corso, Gabriele and Passaro, Saro and Getz, Noah and Reveiz, Mateo and Leidal, Ken and Swiderski, Wojtek and Atkinson, Liam and Portnoi, Tally and Chinn, Itamar and Silterra, Jacob and Jaakkola, Tommi and Barzilay, Regina},\n    journal = {bioRxiv},\n    year = {2024},\n    doi = {10.1101/2024.11.19.624167},\n    language = \"en\"\n}\n```\n\n<br>\n\n### Model Architecture:\n\n**Architecture Type:** Four components \u2014 trunk, denoising module (with steering), confidence module, and a new affinity module <br>\n**Network Architecture:** PairFormer <br>\n\n**Input Type(s):** Biomolecular sequences (protein, DNA, RNA), ligand SMILES or CCD strings, molecular modifications, structural constraints, conditioning parameters, optional booleans <br>\n**Input Format(s):** Dictionary containing sequence strings, modification records, and constraint parameters <br>\n**Input Parameters:** Sequences (strings), predict_affinity(boolean), modifications (list of residue-specific changes), constraints (dictionary of structural parameters) <br>\n**Other Properties Related to Input:** Maximum sequence length of 4096 residues per chain. Maximum of 12 input polymers. Maximum of 20 input ligands. Passing boolean options such as predict_affinity will increase the runtime of the request. <br>\n**Model Parameters:**\nTables 1 and 2 record some of the hyperparameters of Boltz-2\u2019s architecture, training and inference procedures that differ from Boltz-1\u2019s and were not previously mentioned in the manuscript.  \n\n<table>\n<tr>\n<td>\n\n<b>Table 1:</b> Extra model architecture and training hyperparameters  \nthat differ from Boltz-1 and were not previously mentioned in the manuscript.  \n\n| Parameter | Value |\n|-----------|-------|\n| Max number of MSA sequences during training | 8192 |\n| Template pairwise dim | 64 |\n| Num template blocks | 2 |\n| Training diffusion multiplicity | 32 |\n| bfactor loss weight | 1 \u00d7 10\u207b\u00b3 |\n\n</td>\n<td>\n\n<b>Table 2:</b> Diffusion process hyperparameters  \nthat differ from Boltz-1, with the exception of sigma_min we opted for AlphaFold3\u2019s default hyperparameters, see Abramson et al. (2024) for more details.  \n\n| Parameter   | Value   |\n|-------------|---------|\n| sigma_min   | 0.0001  |\n| rho         | 7       |\n| gamma_0     | 0.8     |\n| gamma_min   | 1.0     |\n| noise_scale | 1.003   |\n| step_scale  | 1.5     |\n\n</td>\n</tr>\n</table>\n\n### Output:\n\n**Output Type(s):** Structure prediction in mmcif format; scores in numeric arrays; runtime metrics as a dictionary <br>\n**Output Format:** mmcif (text file); numeric arrays; scalar numeric values <br>\n**Output Parameters:** 3D atomic coordinates, predicted scores, and metadata <br>\n**Other Properties Related to Output:** All Boltz-2 scores are returned by default. Runtime metrics are optional. <br>\nOur AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA's hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions. <br>\n\n### Software Integration:\n\n**Runtime Engine(s):**\n* PyTorch, TensorRT <br>\n\n**Supported Hardware Microarchitecture Compatibility:** <br>\n* NVIDIA Ampere, NVIDIA Hopper, NVIDIA Lovelace <br>\n\n**[Preferred/Supported] Operating System(s):** <br>\n* [Linux] <br>\n* The integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.\n\n### Model Version(s):\n\nBoltz2 version 1.2 <br>\n\n## Training & Evaluation:\n\n### Training Dataset:\n** Data Modality <br>\n* [Text]\n\n\n**Link:** [Protein Data Bank as used by AlphaFold3](https://github.com/jwohlwend/boltz/blob/main/docs/training.md)  <br>\n** Data Collection Method by dataset <br>\n* Human <br>\n\n** Labeling Method by dataset <br>\n* Human <br>\n\n**Properties:**\nAll Protein Data Bank structures before 2021-09-30 with a resolution of at least 9 Angstroms, preprocessed to match each structure to its sequence. Ligands were processed similarly. All data was cleaned as described in AlphaFold3.\n\n### Evaluation Dataset:\n\n**Link:** [Boltz Evaluation Performed on 744 Structures from the Protein Data Bank](https://github.com/jwohlwend/boltz/blob/main/docs/evaluation.md)  <br>\n** Data Collection Method by dataset <br>\n* Human <br>\n\n** Labeling Method by dataset <br>\n* Hybrid: Human and Automated <br>\n\n**Properties:**\nThe test and validation datasets were generated by extensive filtering of PDB sequences deposited between 2021-09-31 and 2023-01-13. In total, 593 structures passed filters and were used for validation.\n<br>\n\n### Inference:\n\n**Acceleration Engine:** PyTorch, TensorRT <br>\n**Test Hardware:** <br>\n* NVIDIA A100 <br>\n* NVIDIA B200 <br>\n* NVIDIA L40 <br>\n* NVIDIA H100 <br>\n* NVIDIA RTX6000-Ada <br>\n* NVIDIA GB200 <br>\n\n### Ethical Considerations:\n\nNVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications.  When downloaded or used in accordance with our terms of service, developers should work with their supporting model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. Please report model quality, risk, security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).\n\n**You are responsible for ensuring for ensuring the physical properties of model-generated molecules are appropriately evaluated, and comply with applicable safety regulations and ethical standards.**\n\n# Get help\n## Enterprise Support\nGet access to knowledge base articles and support cases or [submit a ticket](https://www.nvidia.com/en-us/data-center/products/ai-enterprise-suite/support/).",
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Both 20B and 120B variants support chain-of-thought reasoning, tuned for different latency and throughput tradeoffs. requireLicense: true licenseAgreements: - label: Use Policy url: https://www.nvidia.com/en-us/agreements/enterprise-software/product-specific-terms-for-ai-products/ - label: License Agreement url: https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-license-agreement/ modelVariants: - variantId: GPT-OSS 120B modelCard: {
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    "description": "# GPT OSS 120B Overview\n\n## Description: <br>\nOpenAI releases the gpt-oss family of open-weight models designed for powerful reasoning, agentic tasks, and versatile developer use cases. The family consists of the:\n- `gpt-oss-120b` \u2014 for production, general purpose, high reasoning use-cases that fits into a single H100 GPU (117B parameters with 5.1B active parameters)\n- `gpt-oss-20b` \u2014 for lower latency, and local or specialized use-cases (21B parameters with 3.6B active parameters).\n\nThe `gpt-oss-120b` model is architecturally designed as a Mixture-of-Experts (MoE) model. This model features SwiGLU activations and learned attention sinks within its architecture. It functions as a reasoning model, supporting capabilities such as chain-of-thought processing, adjustable reasoning effort levels, instruction following, and tool use. This model is text-only for both input and output modalities, enabling enterprises and governments to deploy it on-premises or in private cloud environments for enhanced data security and privacy.\n\nModel Highlights:  \n- **Permissive Apache 2.0 license:** Build freely without copyleft restrictions or patent risk\u2014ideal for experimentation, customization, and commercial deployment.\n- **Configurable reasoning effort:** Easily adjust the reasoning effort (low, medium, high) based on your specific use case and latency needs.\n- **Full chain-of-thought:** Gain complete access to the model's reasoning process, facilitating easier debugging and increased trust in outputs. It's not intended to be shown to end users.\n- **Fine-tunable:** Fully customize models to your specific use case through parameter fine-tuning.\n- **Agentic capabilities:** Use the models' native capabilities for function calling, web browsing, python code execution, and structured outputs.\n\nThis model is ready for commercial/non-commercial use.\n\n\n## Third-Party Community Consideration <br>\nThis model is not owned or developed by NVIDIA. This model has been developed and built to a third-party\u2019s requirements for this application and use case; see link to Non-NVIDIA [gpt-oss-120b model card](https://huggingface.co/openai/gpt-oss-120b).\n\n\n### License and Terms of Use: <br>\nGOVERNING TERMS: The NIM container is governed by the [NVIDIA Software License Agreement](at https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-license-agreement/) and the [Product-Specific Terms for NVIDIA AI Products](https://www.nvidia.com/en-us/agreements/enterprise-software/product-specific-terms-for-ai-products/); and the use of this model is governed by the [NVIDIA Community Model License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-community-models-license/).\nAdditional Information: [Apache License Version 2.0](https://www.apache.org/licenses/LICENSE-2.0).\n\n**You are responsible for ensuring that your use of NVIDIA provided models complies with all applicable laws**\n\n## Get Help\n\n### Enterprise Support\n\nGet access to knowledge base articles and support cases or [submit a ticket](https://www.nvidia.com/en-us/data-center/products/ai-enterprise-suite/support/).\n\n### NVIDIA NIM Documentation\n\nVisit the [NIM Container LLM](https://docs.nvidia.com/nim/large-language-models/latest/introduction.html) page for release documentation, deployment guides and more.\n\n### Deployment Geography:\nGlobal\n\n### Use Case: <br>\nIntended for use as a reasoning model with features like chain-of-thought and adjustable reasoning effort levels. It supports instruction following and tool use, offering transparency, customization, and deployment flexibility for developers, researchers, and startups. Additionally, it enables enterprises and governments to deploy on-premises or in private clouds to ensure data security and privacy.\n\n### Release Date:  <br>\nBuild.NVIDIA.com - 08/05/2025 via [link](https://build.nvidia.com/openai/gpt-oss-120b) <br> \nHugging Face - 08/05/2025 via [link](https://huggingface.co/openai/gpt-oss-120b) <br>\n\n## Reference(s):\n- [OpenAI Cookbook](https://cookbook.openai.com/)\n- [Open AI Coobkbook -- Serving Model with TensorRT-LLM](https://cookbook.openai.com/articles/gpt-oss/run-nvidia)\n\n\n## Model Architecture: <br> \n**Architecture Type:** Transformer <br>\n**Network Architecture:** Mixture-of-Experts (MoE) <br>\n**Total Parameters:** 117B <br>\n**Active Parameters:** 5.7B <br>\n**Vocabulary Size:** 201,088 <br>\n\n\n## Input: <br>\n**Input Type(s):** Text <br>\n**Input Format(s):** String <br>\n**Input Parameters:** One Dimensional (1D) <br>\n**Other Properties Related to Input:** Uses RoPE with a 128k context length, with attention layers alternating between full context and a sliding 128-token window. Includes a learned attention sink per-head. Employs SwiGLU activations in the MoE layers, and the router performs a Top-K operation (K=4) followed by a Sigmoid function. GEMMs in the MoE include a per-expert bias. Utilizes tiktoken for tokenization. Input Context Length (ISL): 128000 <br>\n\n## Output: <br>\n**Output Type(s):** Text <br>\n**Output Format:** String <br>\n**Output Parameters:** One Dimensional (1D) <br>\n**Other Properties Related to Output:** The model is designed to be compatible with the OpenAI Responses API and supports Structured Output. <br> \n\nOur AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems [or name equivalent hardware preference]. By leveraging NVIDIA\u2019s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions. <br>   \n\n## Software Integration: <br>\n**Runtime Engine(s):** <br>\n* NeMo Framework (based on 25.07)<br>\n\n\n**Supported Hardware Microarchitecture Compatibility:** <br>\n* NVIDIA Blackwell: B200 <br>\n* NVIDIA Hopper: H200\n\n\n**Operating System(s):** Linux \n\n## Model Version(s): \n`gpt-oss-120b` v1.0 (August 5, 2025)\n\n\n## Training, Testing, and Evaluation Datasets: <br>   \n### Training Dataset:\n\n* **Training Data Collection:** Undisclosed <br>\n* **Training Labeling:** Undisclosed <br>\n* **Training Properties:** The model has approximately 117 billion parameters. Weights for all layers are in BF16, except for MoE projection weights, which are in MXFP4. The reference implementation currently upcasts all weights to BF16. Activations are expected to be in BF16 or FP8.\n\n\n### Testing Dataset:\n* **Testing Data Collection:** Undisclosed <br>\n* **Testing Labeling:** Undisclosed <br>\n* **Testing Properties:** The model is tested against benchmarks such as MMLU and GPQA, among others including LiveCodeBench, AIME 2024, and MATH-500. \n\n### Evaluation Dataset:\n\n* **Evaluation Data Collection:** Undisclosed <br>\n* **Evaluation Labeling:** Undisclosed <br>\n* **Evaluation Benchmark Score:** \n\n| Benchmark  | gpt-oss-120b | gpt-oss-20b |\n|----------|-----------| -----------|\n| AIME 2024 (no tools) | 95.8   | 92.1 |\n| AIME 2024 (with tools) | 96.6 | 96.0 |\n| AIME 2025 (no tools) | 92.5  | 91.7 |\n| AIME 2025 (with tools) | 97.9 | 98.7 |\n| GPQA Diamond (no tools) | 80.1 | 71.5 |\n| GPQA Diamond (with tools) | 80.9 | 74.2 |\n| HLE (no tools) | 14.9 | 10.9 |\n| HLE (with tools) | 19.0 | 17.3 |\n| MMLU | 90.0 | 85.3 |\n| SWE-Bench Verified | 62.4 | 60.7 |\n| Tau-Bench Retail | 67.8 | 54.4 |\n| Tau-Bench Airline | 49.2 | 38.0 |\n| Aider Polyglot | 44.4 | 34.2 |\n| MMMLU (Average) | 81.3 | 75.6 |\n| HealthBench | 57.6 | 42.5 |\n| HealthBench Hard | 30.0 | 10.8 |\n| HealthBench Consensus | 89.9 | 82.6 |\n| Codeforces (no tools) [elo] | 2463 | 2230 |\n| Codeforces (with tools) [elo] | 2622 | 2516 |\n\nAbove scores were measured for the high reasoning level.\n\n### Safety Results:\n\nThe following evaluations check that the model does not comply with requests for content that is\ndisallowed under OpenAI\u2019s safety policies, including hateful content or illicit advice.\n\n| Category  | gpt-oss-120b | gpt-oss-20b |\n|----------|-----------| -----------|\n| hate (aggregate) | 0.996   | 0.996 |\n| self-harm/intent and selfharm/instructions | 0.995 | 0.984 |\n| personal data/semi restrictive | 0.967  | 0.947 |\n| sexual/exploitative | 1.000 | 0.980 |\n| sexual/minors | 1.000 | 0.971 |\n| illicit/non-violent | 1.000 | 0.983 |\n| illicit/violent | 1.000 | 1.000 |\n| personal data/restricted | 0.996 | 0.978 |\n\n## Inference:\n**Acceleration Engine:** vLLM <br>\n**Test Hardware:** NVIDIA Hopper: B200 <br>\n\n\n## Additional Details\nThe model is released with the native quantization support. Specifically, [MXFP4](https://www.opencompute.org/documents/ocp-microscaling-formats-mx-v1-0-spec-final-pdf) is used for the linear projection weights in the MoE layer. It is stored the MoE tensor in two parts:\n\n- `tensor.blocks` stores the actual fp4 values. Every two values are packed in one `uint8` value.\n- `tensor.scales` stores the block scale. The block scaling is done among the last dimension for all MXFP4 tensors.\n\nAll other tensors are stored in BF16. It is recommended to use BF16 as the activation precision for the model.\n\n## Ethical Considerations:\nNVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications.  When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.  \n\nPlease report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).",
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    "description": "# GPT OSS 20B Overview\n\n## Description: <br>\nOpenAI releases the gpt-oss family of open-weight models designed for powerful reasoning, agentic tasks, and versatile developer use cases. The family consists of the:\n- `gpt-oss-120b` \u2014 for production, general purpose, high reasoning use-cases that fits into a single H100 GPU (117B parameters with 5.1B active parameters)\n- `gpt-oss-20b` \u2014 for lower latency, and local or specialized use-cases (21B parameters with 3.6B active parameters).\n\nThe `gpt-oss-20b` is designed as a Mixture-of-Experts (MoE) model, structurally identical to the larger 117B variant, albeit with different hyperparameters. This model leverages SwiGLU activations and incorporates learned attention sinks within its architecture. Functionally, it serves as a robust reasoning model, supporting advanced capabilities such as chain-of-thought processing, adjustable reasoning effort levels, instruction following, and tool use. It operates strictly with text-only modalities for both input and output. A key strategic benefit is its suitability for enterprises and governments, facilitating on-premises or private cloud deployment to ensure enhanced data security and privacy.\n\nModel Highlights:  \n- **Permissive Apache 2.0 license:** Build freely without copyleft restrictions or patent risk\u2014ideal for experimentation, customization, and commercial deployment.\n- **Configurable reasoning effort:** Easily adjust the reasoning effort (low, medium, high) based on your specific use case and latency needs.\n- **Full chain-of-thought:** Gain complete access to the model's reasoning process, facilitating easier debugging and increased trust in outputs. It's not intended to be shown to end users.\n- **Fine-tunable:** Fully customize models to your specific use case through parameter fine-tuning.\n- **Agentic capabilities:** Use the models' native capabilities for function calling, web browsing, python code execution, and structured outputs.\n\nThis model is ready for commercial/non-commercial use.\n\n## Third-Party Community Consideration <br>\nThis model is not owned or developed by NVIDIA. This model has been developed and built to a third-party\u2019s requirements for this application and use case; see link to Non-NVIDIA [gpt-oss-20b model card](https://huggingface.co/openai/gpt-oss-20b).\n\n### License and Terms of Use: <br>\n\nGOVERNING TERMS: The NIM container is governed by the [NVIDIA Software License Agreement](at https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-license-agreement/) and the [Product-Specific Terms for NVIDIA AI Products](https://www.nvidia.com/en-us/agreements/enterprise-software/product-specific-terms-for-ai-products/); and the use of this model is governed by the [NVIDIA Community Model License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-community-models-license/).\nAdditional Information: [Apache License Version 2.0](https://www.apache.org/licenses/LICENSE-2.0).\n\n**You are responsible for ensuring that your use of NVIDIA provided models complies with all applicable laws**\n\n## Get Help\n\n### Enterprise Support\n\nGet access to knowledge base articles and support cases or [submit a ticket](https://www.nvidia.com/en-us/data-center/products/ai-enterprise-suite/support/).\n\n### NVIDIA NIM Documentation\n\nVisit the [NIM Container LLM](https://docs.nvidia.com/nim/large-language-models/latest/introduction.html) page for release documentation, deployment guides and more.\n\n\n### Deployment Geography:\nGlobal\n\n### Use Case: <br>\nIntended for use as a reasoning model, offering features like chain-of-thought and adjustable reasoning effort levels. It provides comprehensive support for instruction following and tool use, fostering transparency, customization, and deployment flexibility for developers, researchers, and startups. Crucially, it enables enterprises and governments to deploy on-premises or in private clouds, ensuring stringent data security and privacy requirements are met.\n\n### Release Date:  <br>\nBuild.NVIDIA.com - 08/05/2025 via [link](https://build.nvidia.com/openai/gpt-oss-20b) <br> \nHugging Face - 08/05/2025 via [link](https://huggingface.co/openai/gpt-oss-20b) <br>\n\n## Reference(s):\n- [OpenAI Cookbook](https://cookbook.openai.com/)\n- [Open AI Coobkbook -- Serving Model with TensorRT-LLM](https://cookbook.openai.com/articles/gpt-oss/run-nvidia)\n\n\n## Model Architecture: <br> \n**Architecture Type:** Transformer <br>\n**Network Architecture:** Mixture-of-Experts (MoE) <br>\n**Total Parameters:** 20B <br>\n**Active Parameters:** 4B <br>\n**Vocabulary Size:** 201,088 (Utilizes the standard tokenizer used by GPT-4o) <br>\n\n\n## Input: <br>\n**Input Type(s):** Text <br>\n**Input Format(s):** String <br>\n**Input Parameters:** One Dimensional (1D) <br>\n**Other Properties Related to Input:** Uses RoPE with a 128k context length, with attention layers alternating between full context and a sliding 128-token window. Includes a learned attention sink per-head. Employs SwiGLU activations in the MoE layers, and the router performs a Top-K operation (K=4) followed by a Sigmoid function. GEMMs in the MoE include a per-expert bias. Utilizes tiktoken for tokenization. Input Context Length (ISL): 128000 <br>\n\n## Output: <br>\n**Output Type(s):** Text <br>\n**Output Format:** String <br>\n**Output Parameters:** One Dimensional (1D) <br>\n**Other Properties Related to Output:** The model is architected to be compatible with the OpenAI Responses API and supports Structured Output, aligning with key partner expectations for advanced response formatting. <br> \n\nOur AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems [or name equivalent hardware preference]. By leveraging NVIDIA\u2019s hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions. <br>   \n\n## Software Integration: <br>\n**Runtime Engine(s):** <br>\n* NeMo Framework (based on 25.07)<br>\n\n\n**Supported Hardware Microarchitecture Compatibility:** <br>\n* NVIDIA Blackwell: B200 <br>\n* NVIDIA Hopper: H200\n\n\n**Operating System(s):** Linux \n\n## Model Version(s): \n`gpt-oss-20b` v1.0 (August 5, 2025)\n\n\n## Training, Testing, and Evaluation Datasets: <br>   \n### Training Dataset:\n\n* **Training Data Collection:** Undisclosed <br>\n* **Training Labeling:** Undisclosed <br>\n* **Training Properties:** The gpt-oss-20b model has approximately 20 billion total parameters, with approximately 4 billion active parameters per inference. The weights for all layers are in BF16, except for the MoE projection weights, which are in MXFP4. The reference implementation, for initial accuracy validation, currently upcasts all weights to BF16. Activations are expected to be in BF16 or FP8.\n\n\n### Testing Dataset:\n* **Testing Data Collection:** Undisclosed <br>\n* **Testing Labeling:** Undisclosed <br>\n* **Testing Properties:** The model's performance is tested against recognized benchmarks such as MMLU (Massive Multitask Language Understanding) and GPQA (General Purpose Question Answering), alongside other benchmarks including LiveCodeBench, AIME 2024, and MATH-500 \n\n### Evaluation Dataset:\n\n* **Evaluation Data Collection:** Undisclosed <br>\n* **Evaluation Labeling:** Undisclosed <br>\n* **Evaluation Benchmark Score:** \n\n| Benchmark  | gpt-oss-120b | gpt-oss-20b |\n|----------|-----------| -----------|\n| AIME 2024 (no tools) | 95.8   | 92.1 |\n| AIME 2024 (with tools) | 96.6 | 96.0 |\n| AIME 2025 (no tools) | 92.5  | 91.7 |\n| AIME 2025 (with tools) | 97.9 | 98.7 |\n| GPQA Diamond (no tools) | 80.1 | 71.5 |\n| GPQA Diamond (with tools) | 80.9 | 74.2 |\n| HLE (no tools) | 14.9 | 10.9 |\n| HLE (with tools) | 19.0 | 17.3 |\n| MMLU | 90.0 | 85.3 |\n| SWE-Bench Verified | 62.4 | 60.7 |\n| Tau-Bench Retail | 67.8 | 54.4 |\n| Tau-Bench Airline | 49.2 | 38.0 |\n| Aider Polyglot | 44.4 | 34.2 |\n| MMMLU (Average) | 81.3 | 75.6 |\n| HealthBench | 57.6 | 42.5 |\n| HealthBench Hard | 30.0 | 10.8 |\n| HealthBench Consensus | 89.9 | 82.6 |\n| Codeforces (no tools) [elo] | 2463 | 2230 |\n| Codeforces (with tools) [elo] | 2622 | 2516 |\n\nAbove scores were measured for the high reasoning level.\n\n### Safety Results:\n\nThe following evaluations check that the model does not comply with requests for content that is\ndisallowed under OpenAI\u2019s safety policies, including hateful content or illicit advice.\n\n| Category  | gpt-oss-120b | gpt-oss-20b |\n|----------|-----------| -----------|\n| hate (aggregate) | 0.996   | 0.996 |\n| self-harm/intent and selfharm/instructions | 0.995 | 0.984 |\n| personal data/semi restrictive | 0.967  | 0.947 |\n| sexual/exploitative | 1.000 | 0.980 |\n| sexual/minors | 1.000 | 0.971 |\n| illicit/non-violent | 1.000 | 0.983 |\n| illicit/violent | 1.000 | 1.000 |\n| personal data/restricted | 0.996 | 0.978 |\n\n## Inference:\n**Acceleration Engine:** vLLM <br>\n**Test Hardware:** NVIDIA Hopper (H200) <br>\n\n\n## Additional Details\nThe model is released with the native quantization support. Specifically, [MXFP4](https://www.opencompute.org/documents/ocp-microscaling-formats-mx-v1-0-spec-final-pdf) is used for the linear projection weights in the MoE layer. It is stored the MoE tensor in two parts:\n\n- `tensor.blocks` stores the actual fp4 values. Every two values are packed in one `uint8` value.\n- `tensor.scales` stores the block scale. The block scaling is done among the last dimension for all MXFP4 tensors.\n\nAll other tensors are stored in BF16. It is recommended to use BF16 as the activation precision for the model.\n\n## Ethical Considerations:\nNVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications.  When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.  \n\nPlease report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).",
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