[whatmodel.sh](../../README.md) / Llama 3.1 Nemotron 51B Instruct # Llama 3.1 Nemotron 51B Instruct NVIDIA · released 2024-09 · 51.5B params · nvidia-open-model-license **score 52.6** ±1.7 · #325 of 732 · from 6 benchmark results across 2 sources Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only. ## What it's good at _{● marks Llama 3.1 Nemotron 51B Instruct among every model on that task or test, worst to best.}

task            vs the others          rank  counts  tests
chat & writing  ▁▁▁▃▆▄●█▆▇▇▃   #247 of 402*     31%      1
math            ▁▁▃▅●▆▄█▄▂▁▁   #272 of 431*     21%      1

chat & writing · #247 of 402 · 31% of its score

Beats [Mistral Small 4 (119B-A6.5B)](mistral-small-4.md), [Qwen2.5 Coder 32B Instruct](qwen2-5-coder-32b.md), [Ministral 3 14B Reasoning 2512](ministral-3-14b-reasoning.md) here, though they rank higher overall. Trails [Flan-T5 XXL](flan-t5-xxl.md), [Granite 4.0 H 1B](granite-4-0-h-1b.md), [LFM2.5 1.2B Instruct](lfm2-5-1-2b-instruct.md) here, though they rank lower overall.

test            result  vs the others in its run    difficulty  counts  why
LMArena (text)  1286.7  ▁▁▁▂▃▃●▆▆▇█▃ beats 116/385  avg 1260       31%  blunt

math · #272 of 431 · 21% of its score

Beats [GPT-4 Turbo (2024-04-09)](gpt-4-turbo-2024-04-09.md), [Molmo 2 8B](molmo-2-8b.md), [Llama 3.3 70B Instruct](llama-3-3-70b.md) here, though they rank higher overall. Trails [DeepSeek R1 Distill Qwen 1.5B](deepseek-r1-distill-qwen-1-5b.md), [Gemma 7B (base)](gemma-7b-base.md), [CodeGemma 7B (v1.0)](codegemma-7b.md) here, though they rank lower overall.

test           result  vs the others in its run    difficulty           counts  why
GSM8K (maker)   91.4%  ▂▂▂▂▃▄▅▇██●▄ beats 183/206  very easy · avg 87%     21%  sharp · small run

other tests (4)

test                   result  vs the others in its run    difficulty        counts  why
ARC Challenge (maker)   69.2%  ▄▁▁▂▁▄█▇●▂▃▂ beats 116/140  medium · avg 66%     16%  sharp · at its level · small run
MMLU (maker)            80.2%  ▁▁▁▁▁▂▂▅██●▄ beats 168/214  easy · avg 76%       15%  at its level · small run
WinoGrande (maker)      84.5%  ▁▁▁▁▂▁▁▅▇█▃● beats 129/131  easy · avg 83%      8.6%  at its level · small run
HellaSwag (maker)       85.6%  ▁▁▁▂▂▂▄▆█▇●▂ beats 128/135  easy · avg 85%      7.0%  small run

## How the score was worked out Each result implies a score on its own, after adjusting for how hard that test is and who ran it. The model's score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its *counts* column above; here it is added up by who ran the tests. [Method](../method.md)

source                                    results  share of the score
Puzzle: Distillation-Based NAS for Infe…        5   68% ▇▇▇▇▇▇▇▇
LMArena leaderboard dataset                     1   31% ▇▇▇▇
prior: pull toward the middle                        1% ▇
weighted average                                   = 52.6

_{Licences: Puzzle: Distillation-Based NAS for Inference-Optimized LLMs ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)) · LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/))} ## How to run it ### llama.cpp (recommended) 1. Install llama.cpp. Homebrew and winget installs name the commands llama-cli and llama-server instead. [Other ways to install](https://github.com/ggml-org/llama.cpp/blob/master/docs/install.md) ```sh # macOS / Linux curl -LsSf https://llama.app/install.sh | sh # Windows irm https://llama.app/install.ps1 | iex ``` 2. Run Llama 3.1 Nemotron 51B Instruct (Q4_K_M, needs ~31.04 GB, from [huggingface.co/bartowski](https://huggingface.co/bartowski/Llama-3_1-Nemotron-51B-Instruct-GGUF/tree/f2bea9ce4c76fe6c623c641adbd2f795c1c00313)) ```sh llama cli -hf bartowski/Llama-3_1-Nemotron-51B-Instruct-GGUF:Q4_K_M ``` 3. Or start a local server and open the address it prints (http://localhost:9931) ```sh llama serve -hf bartowski/Llama-3_1-Nemotron-51B-Instruct-GGUF:Q4_K_M ``` Other versions: Q8_0 (~54.73 GB) `llama cli -hf bartowski/Llama-3_1-Nemotron-51B-Instruct-GGUF:Q8_0` ### LM Studio 1. Install LM Studio. Open the app once before using the lms command. [Download it (macOS / Windows / Linux)](https://lmstudio.ai/download) 2. Run Llama 3.1 Nemotron 51B Instruct (Q4_K_M, needs ~31.04 GB, from [huggingface.co/bartowski](https://huggingface.co/bartowski/Llama-3_1-Nemotron-51B-Instruct-GGUF/tree/f2bea9ce4c76fe6c623c641adbd2f795c1c00313)) ```sh lms get https://huggingface.co/bartowski/Llama-3_1-Nemotron-51B-Instruct-GGUF@Q4_K_M -y ``` 3. Then run lms ls to see its name, and lms chat followed by that name Other versions: Q8_0 (~54.73 GB) `lms get https://huggingface.co/bartowski/Llama-3_1-Nemotron-51B-Instruct-GGUF@Q8_0 -y` ### Ollama 1. Install Ollama. [Download it (macOS / Windows)](https://ollama.com/download) ```sh # Linux curl -fsSL https://ollama.com/install.sh | sh ``` 2. Run Llama 3.1 Nemotron 51B Instruct (Q4_K_M, needs ~31.04 GB, from [huggingface.co/bartowski](https://huggingface.co/bartowski/Llama-3_1-Nemotron-51B-Instruct-GGUF/tree/f2bea9ce4c76fe6c623c641adbd2f795c1c00313)) ```sh ollama run hf.co/bartowski/Llama-3_1-Nemotron-51B-Instruct-GGUF:Q4_K_M ``` Model repository and documentation: [huggingface.co/nvidia](https://huggingface.co/nvidia/Llama-3_1-Nemotron-51B-Instruct) [interactive version](https://whatmodel.sh/model/llama-3-1-nemotron-51b) · [JSON](https://whatmodel.sh/api?format=json&all=1)