# Awesome local LLM

A curated list of awesome platforms, tools, practices and resources that helps run LLMs locally ## Table of Contents - [Inference platforms](#inference-platforms) - [Inference engines](#inference-engines) - [User Interfaces](#user-interfaces) - [Large Language Models](#large-language-models) - [Explorers, Benchmarks, Leaderboards](#explorers-benchmarks-leaderboards) - [Model providers](#model-providers) - [Specific models](#specific-models) - [General purpose](#general-purpose) - [Coding](#coding) - [Multimodal](#multimodal) - [Image](#image) - [Audio](#audio) - [Retrieval-Augmented Generation](#retrieval-augmented-generation) - [Safeguards](#safeguards) - [Miscellaneous](#miscellaneous) - [Tools](#tools) - [Models](#models) - [Agent Frameworks](#agent-frameworks) - [Model Context Protocol](#model-context-protocol) - [Retrieval-Augmented Generation](#retrieval-augmented-generation-1) - [Coding Agents](#coding-agents) - [Computer Use](#computer-use) - [Browser Automation](#browser-automation) - [Memory Management](#memory-management) - [Testing, Evaluation and Observability](#testing-evaluation-and-observability) - [Research](#research) - [Training and Fine-tuning](#training-and-fine-tuning) - [Security and Sandboxing](#security-and-sandboxing) - [Miscellaneous](#miscellaneous-1) - [Hardware](#hardware) - [Tutorials](#tutorials) - [Models](#models-1) - [Prompt Engineering](#prompt-engineering) - [Context Engineering](#context-engineering) - [Inference](#inference) - [Agents](#agents) - [Retrieval-Augmented Generation](#retrieval-augmented-generation-2) - [Miscellaneous](#miscellaneous-2) - [Communities](#communities) ## Inference platforms - [LM Studio](https://lmstudio.ai/) - discover, download and run local LLMs -

[unsloth](https://github.com/unslothai/unsloth) - unified web UI for training and running open models like Qwen, DeepSeek, and Gemma locally -

[LocalAI](https://github.com/mudler/LocalAI) - the free, open-source alternative to OpenAI, Claude and others -

[jan](https://github.com/menloresearch/jan) - an open source alternative to ChatGPT that runs 100% offline on your computer -

[ChatBox](https://github.com/ChatBoxAI/ChatBox) - user-friendly desktop client app for AI models/LLMs -

[lemonade](https://github.com/lemonade-sdk/lemonade) - a local LLM server with GPU and NPU Acceleration [Back to Table of Contents](#table-of-contents) ## Inference engines -

[ollama](https://github.com/ollama/ollama) - get up and running with LLMs -

[llama.cpp](https://github.com/ggml-org/llama.cpp) - LLM inference in C/C++ -

[vllm](https://github.com/vllm-project/vllm) - a high-throughput and memory-efficient inference and serving engine for LLMs -

[exo](https://github.com/exo-explore/exo) - run your own AI cluster at home with everyday devices -

[BitNet](https://github.com/microsoft/BitNet) - official inference framework for 1-bit LLMs -

[sglang](https://github.com/sgl-project/sglang) - a fast serving framework for large language models and vision language models -

[omlx](https://github.com/jundot/omlx) - LLM inference server with continuous batching & SSD caching for Apple Silicon — managed from the macOS menu bar -

[Strata](https://github.com/Niko1221/Strata) - Qwen3.8-Flash-Next on any consumer hardware -

[Nano-vLLM](https://github.com/GeeeekExplorer/nano-vllm) - a lightweight vLLM implementation built from scratch -

[TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM) - provides users with an easy-to-use Python API to define Large Language Models (LLMs) and supports state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs -

[koboldcpp](https://github.com/LostRuins/koboldcpp) - run GGUF models easily with a KoboldAI UI -

[dynamo](https://github.com/ai-dynamo/dynamo) - a datacenter scale distributed inference serving framework -

[mlx-lm](https://github.com/ml-explore/mlx-lm) - generate text and fine-tune large language models on Apple silicon with MLX -

[mistral.rs](https://github.com/EricLBuehler/mistral.rs) - fast, flexible LLM inference -

[flashinfer](https://github.com/flashinfer-ai/flashinfer) - kernel library for LLM serving -

[LiteRT-LM](https://github.com/google-ai-edge/LiteRT-LM) - Google's production-ready, high-performance, open-source inference framework for deploying Large Language Models on edge devices -

[gpustack](https://github.com/gpustack/gpustack) - simple, scalable AI model deployment on GPU clusters -

[mlx-vlm](https://github.com/Blaizzy/mlx-vlm) - a package for inference and fine-tuning of Vision Language Models (VLMs) on your Mac using MLX -

[mini-sglang](https://github.com/sgl-project/mini-sglang) - a lightweight yet high-performance inference framework for Large Language Models -

[executorch](https://github.com/pytorch/executorch) - on-device AI across mobile, embedded and edge for PyTorch -

[LiteRT](https://github.com/google-ai-edge/litert) - Google's on-device framework for high-performance ML & GenAI deployment on edge platforms, via efficient conversion, runtime, and optimization -

[distributed-llama](https://github.com/b4rtaz/distributed-llama) - connect home devices into a powerful cluster to accelerate LLM inference -

[ik_llama.cpp](https://github.com/ikawrakow/ik_llama.cpp) - llama.cpp fork with additional SOTA quants and improved performance -

[tokenspeed](https://github.com/lightseekorg/tokenspeed) - a speed-of-light LLM inference engine -

[sonar](https://github.com/dphnAI/sonar) - large-scale LLM inference engine based on vLLM -

[FastFlowLM](https://github.com/FastFlowLM/FastFlowLM) - run LLMs on AMD Ryzen™ AI NPUs -

[krasis](https://github.com/brontoguana/krasis) - a Hybrid LLM runtime which focuses on efficient running of larger models on consumer grade VRAM limited hardware -

[llm-scaler](https://github.com/intel/llm-scaler) - run LLMs on Intel Arc™ Pro B60 and B70 GPUs -

[vllm-gfx906](https://github.com/nlzy/vllm-gfx906) - vLLM for AMD gfx906 GPUs, e.g. Radeon VII / MI50 / MI60 [Back to Table of Contents](#table-of-contents) ## User Interfaces -

[Open WebUI](https://github.com/open-webui/open-webui) - User-friendly AI Interface (Supports Ollama, OpenAI API, ...) -

[Lobe Chat](https://github.com/lobehub/lobe-chat) - an open-source, modern design AI chat framework -

[Text generation web UI](https://github.com/oobabooga/text-generation-webui) - LLM UI with advanced features, easy setup, and multiple backend support -

[SillyTavern](https://github.com/SillyTavern/SillyTavern) - LLM Frontend for Power Users -

[Page Assist](https://github.com/n4ze3m/page-assist) - Use your locally running AI models to assist you in your web browsing [Back to Table of Contents](#table-of-contents) ## Large Language Models ### Explorers, Benchmarks, Leaderboards - [Arena](https://arena.ai/) - benchmark & compare the best AI models - [AI Models & API Providers Analysis](https://artificialanalysis.ai/) - understand the AI landscape to choose the best model and provider for your use case - [SWE-rebench](https://swe-rebench.com/) - a continuously evolving and decontaminated benchmark for software engineering LLMs -

[BullshitBench](https://github.com/petergpt/bullshit-benchmark) - measure whether AI models challenge nonsensical prompts instead of confidently answering them - [LLM Explorer](https://llm-explorer.com/) - explore list of the open-source LLM models - [Dubesor LLM Benchmark table](https://dubesor.de/benchtable) - small-scale manual performance comparison benchmark - [oobabooga benchmark](https://oobabooga.github.io/benchmark.html) - a list sorted by size (on disk) for each score - [CyberGym](https://www.cybergym.io/) - evaluating AI agents' real-world cybersecurity capabilities at scale -

[vakra](https://github.com/IBM/vakra) - a benchmark for evaluating multi-hop, multi-source tool-calling in AI agents -

[swe-serve](https://github.com/NVIDIA/swe-serve) - a benchmark for evaluating multi-hop, multi-source tool-calling in AI agents [Back to Table of Contents](#table-of-contents) ### Model providers - [Qwen](https://huggingface.co/Qwen) - powered by Alibaba Cloud -

[Mistral AI](https://huggingface.co/mistralai) - a pioneering French artificial intelligence startup - [Tencent](https://huggingface.co/tencent) - a profile of a Chinese multinational technology conglomerate and holding company - [Unsloth AI](https://huggingface.co/unsloth) - focusing on making AI more accessible to everyone (GGUFs etc.) - [bartowski](https://huggingface.co/bartowski) - providing GGUF versions of popular LLMs - [Beijing Academy of Artificial Intelligence](https://huggingface.co/BAAI) - a private non-profit organization engaged in AI research and development - [Open Thoughts](https://huggingface.co/open-thoughts) - a team of researchers and engineers curating the best open reasoning datasets [Back to Table of Contents](#table-of-contents) ### Specific models #### General purpose - [DeepSeek-V4](https://huggingface.co/collections/deepseek-ai/deepseek-v4) - a collection of the DeepSeek V4 LLMs - [Qwen3.8](https://huggingface.co/collections/Qwen/qwen38) - a collection of the latest generation Qwen LLMs -

[NVIDIA Nemotron v3](https://huggingface.co/collections/nvidia/nvidia-nemotron-v3) - a family of open models from NVIDIA with open weights, training data and recipes, delivering leading efficiency and accuracy for building specialized AI agents -

[Gemma 4](https://huggingface.co/collections/google/gemma-4) - a family of open models built by Google DeepMind, that are multimodal, handling text and image input (with audio supported on small models) and generating text output -

[Mistral Medium 3.5](https://huggingface.co/collections/mistralai/mistral-medium-35) - The first flaship models from Mistral AI handling instruction-following, reasoning, and coding in a single set of opened-weights -

[gpt-oss](https://huggingface.co/collections/openai/gpt-oss-68911959590a1634ba11c7a4) - a collection of open-weight models from OpenAI, designed for powerful reasoning, agentic tasks, and versatile developer use cases -

[gpt-oss-puzzle-88B](https://huggingface.co/nvidia/gpt-oss-puzzle-88B) - a deployment-optimized large language model developed by NVIDIA, derived from OpenAI's gpt-oss-120b - [Hunyuan](https://huggingface.co/collections/tencent/hunyuan-dense-model-6890632cda26b19119c9c5e7) - a collection of Tencent's open-source efficient LLMs designed for versatile deployment across diverse computational environments - [Phi-4](https://huggingface.co/collections/microsoft/phi-4) - a family of small language, multi-modal and reasoning models from Microsoft -

[OpenReasoning-Nemotron](https://huggingface.co/collections/nvidia/openreasoning-nemotron-687730dae0170059860f1f01) - a collection of models from NVIDIA, trained on 5M reasoning traces for math, code and science - [Kimi K2.5](https://huggingface.co/collections/moonshotai/kimi-k25) - a collection of open-source, native multimodal agentic models from Moonshot AI that advances practical capabilities in long-horizon coding, coding-driven design, proactive autonomous execution, and swarm-based task orchestration - [GLM-5.3](https://huggingface.co/collections/zai-org/glm-53) - a Z.ai's flagship model for long-horizon tasks - [Ling-3.0-flash](https://huggingface.co/inclusionAI/Ling-3.0-flash) - a native hybrid reasoning model from inclusionAI, operating with 124B total and 5.1B active parameters - [Granite 4.1](https://huggingface.co/collections/ibm-granite/granite-41-language-models) - efficient language models from IBM for multilingual generation, coding, RAG, and AI assistant workflows - [Ornith-1.5](https://huggingface.co/collections/ornith-ai/ornith-15) - a collection of open-source models for agentic tasks and coding - [EXAONE-4.5](https://huggingface.co/collections/LGAI-EXAONE/exaone-45) - LG's First Open-Weight Vision-Language Model for Industrial Intelligence - [Step-3.7-Flash](https://huggingface.co/collections/stepfun-ai/step-37-flash) - Mixture-of-Experts vision-language model for developers who need to scale agentic workflows that combine perception, search, and reasoning - [Occamy-1.0](https://huggingface.co/Accio-Lab/occamy-1.0) - a compact agentic model purpose-built for real-world co-work - [MiniCPM5](https://huggingface.co/collections/openbmb/minicpm5) - a collection of SOTA on-device LLMs, small yet powerful - [LFM2.5](https://huggingface.co/collections/LiquidAI/lfm25) - a family of hybrid models designed for on-device deployment [Back to Table of Contents](#table-of-contents) #### Coding - [Qwen3-Coder-Next](https://huggingface.co/collections/Qwen/qwen3-coder-next) - a collection of Qwen's open-weight language models designed specifically for coding agents and local development -

[Devstral 2](https://huggingface.co/collections/mistralai/devstral-2) - a couple of agentic LLMs for software engineering tasks, excelling at using tools to explore codebases, edit multiple files, and power SWE Agents -

[Mellum 2.1](https://huggingface.co/collections/JetBrains/mellum21) - an assistant model trained by JetBrain - [MiniMax-M3](https://huggingface.co/MiniMaxAI/MiniMax-M3) - a native multimodal model with 1M context - [MiniMax-M2](https://huggingface.co/collections/MiniMaxAI/minimax-m2) - a collection of SOTA models for real-world dev & agents - [Laguna-S-2.1](https://huggingface.co/poolside/Laguna-S-2.1) - a 118B total parameter Mixture-of-Experts model with 8B activated parameters per token, designed for agentic coding and long-horizon work - [SWE-FastContext](https://huggingface.co/collections/microsoft/swe-fastcontext) - a family of code-search models from Microsoft powering the Explore subagent for coding agents - [AesCode-32B](https://huggingface.co/microsoft/AesCode-32B) & [AesCode-8B](https://huggingface.co/microsoft/AesCode-8B) - models from Microsoft that generates information-rich visual artifacts such as slides, posters, and dashboards as HTML/CSS - [MusaCoder-27B](https://huggingface.co/MooreThreads/MusaCoder-27B) - a code model developed by Moore Threads for PyTorch-to-CUDA/MUSA native kernel generation [Back to Table of Contents](#table-of-contents) #### Multimodal - [Qwen3-Omni](https://huggingface.co/collections/Qwen/qwen3-omni-68d100a86cd0906843ceccbe) - a collection of the natively end-to-end multilingual omni-modal foundation models from Qwen - [GLM-4.6V](https://huggingface.co/collections/zai-org/glm-46v) - a collection of open source multimodal models with native tool use from Zhipu AI - [MiniCPM-o & MiniCPM-V](https://huggingface.co/collections/openbmb/minicpm-o-and-minicpm-v) - multimodal models with leading performance [Back to Table of Contents](#table-of-contents) #### Image - [Qwen-Image-2.1](https://huggingface.co/Qwen/Qwen-Image-2.1) - a unified text-to-image generation and image editing model in the Qwen family - [Qwen3-VL](https://huggingface.co/collections/Qwen/qwen3-vl-68d2a7c1b8a8afce4ebd2dbe) - a collection of the most powerful vision-language models in the Qwen series to date - [GLM-Image](https://huggingface.co/zai-org/GLM-Image) - an image generation model - [Ming-Image-0.1-Design](https://huggingface.co/inclusionAI/Ming-Image-0.1-Design) - a 6B text-to-image model for UI, infographics, posters, and other text-rich visual designs - [Granite Vision](https://huggingface.co/collections/ibm-granite/granite-vision) - multimodal models from IBM built for visual document analysis and image understanding - [HunyuanImage](https://huggingface.co/collections/tencent/hunyuanimage) - a collection of image generation models from Tencent - [HunyuanVideo](https://huggingface.co/collections/tencent/hunyuanvideo) - a collection of video generation models from Tencent - [Youtu-Parsing-Omni](https://huggingface.co/tencent/Youtu-Parsing-Omni) - a model from Tencent that parses documents, images, charts, geometry, audio and video into structured JSON - [Vidi](https://huggingface.co/collections/bytedance-research/vidi) - a collection of models for multimodal video understanding and creation - [FastVLM](https://huggingface.co/collections/apple/fastvlm-68ac97b9cd5cacefdd04872e) - a collection of VLMs with efficient vision encoding from Apple - [MiniCPM-V 4.7](https://huggingface.co/collections/openbmb/minicpm-v-47) - a collection of models with single-image, multi-image, and video understanding capabilities - [LFM2.5-VL](https://huggingface.co/collections/LiquidAI/lfm25-vl) - a collection of vision-language models, designed for on-device deployment [Back to Table of Contents](#table-of-contents) #### Audio -

[whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) - a state-of-the-art model for automatic speech recognition (ASR) and speech translation from OpenAI -

[Nemotron Speech](https://huggingface.co/collections/nvidia/nemotron-speech) - a collection of open, state-of-the-art, production‑ready enterprise speech models from NVIDIA for ASR, TTS, Speaker Diarization and S2SOpenAI -

[NVIDIA NemotronLabs VoiceChat 11B](https://huggingface.co/nvidia/NVIDIA-NemotronLabs-VoiceChat-11B) - a 11B end-to-end, real-time speech full duplex (FD) model from NVIDIA for conversational AI that jointly performs streaming speech understanding and speech generation - [Qwen3-ASR](https://huggingface.co/collections/Qwen/qwen3-asr) - a collection of models that support language identification and ASR for 52 languages and dialects - [Qwen3-TTS](https://huggingface.co/collections/Qwen/qwen3-tts) - a collection of TTS models that cover 10 major languages as well as multiple dialectal voice profiles to meet global application needs - [Granite Speech](https://huggingface.co/collections/ibm-granite/granite-speech) - a collection of compact and efficient speech-language models from IBM, specifically designed for multilingual automatic speech recognition (ASR) and bidirectional automatic speech translation (AST) -

[Voxtral-Small-24B-2507](https://huggingface.co/mistralai/Voxtral-Small-24B-2507) - an enhancement of Mistral Small 3, incorporating state-of-the-art audio input capabilities while retaining best-in-class text performance -

[Voxtral-Mini-4B-Realtime-2602](https://huggingface.co/mistralai/Voxtral-Mini-4B-Realtime-2602) - a multilingual, realtime speech-transcription model and among the first open-source solutions to achieve accuracy comparable to offline systems with a delay of <500ms -

[Voxtral-4B-TTS-2603](https://huggingface.co/mistralai/Voxtral-4B-TTS-2603) - frontier, open-weights text-to-speech model that’s fast, instantly adaptable, and produces lifelike speech for voice agents - [chatterbox](https://huggingface.co/ResembleAI/chatterbox) - first production-grade open-source TTS model - [VibeVoice](https://huggingface.co/collections/microsoft/vibevoice-68a2ef24a875c44be47b034f) - a collection of frontier text-to-speech models from Microsoft - [Kitten TTS](https://huggingface.co/KittenML/models) - a collection of open-source realistic text-to-speech models designed for lightweight deployment and high-quality voice synthesis -

[Streaming Sortformer Diarizer 4spk v2.1](https://huggingface.co/nvidia/diar_streaming_sortformer_4spk-v2.1) - a streaming version of a novel end-to-end neural model for speaker diarization from NVIDIA [Back to Table of Contents](#table-of-contents) #### Retrieval-Augmented Generation -

[Nemotron RAG](https://huggingface.co/collections/nvidia/nemotron-rag) - a set of tools to build retrieval-augmented generation (RAG) systems, improve search and ranking accuracy, and extract structured data from complex docs -

[EmbeddingGemma 2](https://huggingface.co/google/embeddinggemma-2) - an open multimodal embedding model built by Google DeepMind which maps text (incl. code), images, video, and audio inputs—and combinations thereof—into a single, unified 768-dimensional vector space - [Qwen3-Embedding](https://huggingface.co/collections/Qwen/qwen3-embedding) - a collection of the latest proprietary Qwen models, specifically designed for text embedding and ranking tasks - [Qwen3-VL-Embedding](https://huggingface.co/collections/Qwen/qwen3-vl-embedding) - an addition to the Qwen embedding models, specifically designed for multimodal information retrieval and cross-modal understanding - [Qwen3-Reranker](https://huggingface.co/collections/Qwen/qwen3-vl-reranker) - a collection of the latest proprietary Qwen models, engineered to refine embedding results - [Qwen3-VL-Reranker](https://huggingface.co/collections/Qwen/qwen3-vl-reranker) - an addition to the Qwen embedding models, specifically designed for multimodal information retrieval and cross-modal understanding [Back to Table of Contents](#table-of-contents) #### Safeguards -

[Shieldstral 1.0 3B](https://huggingface.co/mistralai/Shieldstral-1.0-3B) - a compact 3B-parameter, policy-adaptive multimodal safety classifier - [Granite Guardian](https://huggingface.co/collections/ibm-granite/granite-guardian) - a collection of safety models from IBM for detecting risks, toxicity, and hallucinations in LLM workflows - [Qwen3Guard](https://huggingface.co/collections/Qwen/qwen3guard-68d2729abbfae4716f3343a1) - a collection of safety moderation models built upon Qwen3 -

[NemoGuard](https://huggingface.co/collections/nvidia/nemoguard) - a collection of models from NVIDIA for content safety, topic-following and security guardrails -

[Nemotron-3.5-Content-Safety](https://huggingface.co/nvidia/Nemotron-3.5-Content-Safety) - a small language model (SLM) that uses Google's Gemma-3-4B-it as the base and is fine-tuned by NVIDIA on multimodal, multilingual, and reasoning-oriented content-safety datasets -

[Privasis](https://huggingface.co/collections/nvidia/privasis) - a collection of lightweight text-sanitization models from NVIDIA designed to remove or abstract sensitive information from text according to a user-provided sanitization instruction - [SingGuard](https://huggingface.co/collections/inclusionAI/singguard) - a collection of policy-adaptive multimodal LLM Guardrails with dynamic reasoning - [HARC](https://huggingface.co/collections/microsoft/harc) - a family of safety-aligned instruction models from Microsoft trained with HARC -

[gpt-oss-safeguard](https://huggingface.co/collections/openai/gpt-oss-safeguard) - a collection of safety reasoning models built-upon gpt-oss from OpenAI -

[privacy-filter](https://huggingface.co/openai/privacy-filter) - a bidirectional token-classification model from OpenAI for personally identifiable information (PII) detection and masking in text - [AprielGuard](https://huggingface.co/ServiceNow-AI/AprielGuard) - a safeguard model designed to detect and mitigate both safety risks and security threats in LLM interactions [Back to Table of Contents](#table-of-contents) #### Miscellaneous - [Decision 3.0](https://huggingface.co/collections/vllm-sr/decision-30) - a collection of Open multimodal foundation decision models - [GLiNER2.5](https://huggingface.co/collections/fastino/gliner25-models) - a collection of models for schema-driven information extraction and classification - [clef](https://huggingface.co/Cloudflare/clef) - a multimodal model from Cloudflare that turns a state and a schema of typed questions into decisions - [d1-3B](https://huggingface.co/LiquidAI/d1-3B) & [d1-omni-600M](https://huggingface.co/LiquidAI/d1-omni-600M) - open source decision models - [Intern-S2](https://huggingface.co/collections/internlm/intern-s2) - a collection of multimodal foundation models for scientific intelligence and long-horizon agents - [Holo4](https://huggingface.co/collections/Hcompany/holo4) - a collection of Visual Language Models for computer/mobile use, tool calls, and code - [Marco-MoE](https://huggingface.co/collections/AIDC-AI/marco-moe) - a suit of multilingual MoE models with highly-sparse architectures - [OUI-1](https://huggingface.co/thesysdev/OUI-1) - a diffusion model built for generative UI -

[Nemotron-Orchestrator-8B](https://huggingface.co/nvidia/Nemotron-Orchestrator-8B) - a state-of-the-art 8B orchestration model designed to solve complex, multi-turn agentic tasks by coordinating a diverse set of expert models and tools - [Arch-Router-1.5B](https://huggingface.co/katanemo/Arch-Router-1.5B) - the fastest LLM router model that aligns to subjective usage preferences - [Waypoint](https://huggingface.co/collections/Overworld/waypoint) - a collection of real-time interactive video world models - [Hunyuan3D](https://huggingface.co/collections/tencent/hunyuan3d) - a collection of everything related (models, datasets etc.) to 3D assets generation from Tencent - [Hunyuan-GameCraft-1.0](https://huggingface.co/tencent/Hunyuan-GameCraft-1.0) - a novel framework for high-dynamic interactive video generation in game environments - [void-model](https://huggingface.co/netflix/void-model) - a model from Netflix that removes objects from videos along with all interactions they induce on the scene — not just secondary effects like shadows and reflections, but physical interactions like objects falling when a person is removed [Back to Table of Contents](#table-of-contents) ## Tools ### Models -

[llmfit](https://github.com/AlexsJones/llmfit) - hundreds of models & providers, one command to find what runs on your hardware -

[outlines](https://github.com/dottxt-ai/outlines) - structured outputs for LLMs -

[llama-swap](https://github.com/mostlygeek/llama-swap) - reliable model swapping for any local OpenAI compatible server - llama.cpp, vllm, etc. -

[llguidance](https://github.com/guidance-ai/llguidance) - super-fast structured outputs [Back to Table of Contents](#table-of-contents) ### Agent Frameworks -

[AutoGPT](https://github.com/Significant-Gravitas/AutoGPT) - a powerful platform that allows you to create, deploy, and manage continuous AI agents that automate complex workflows -

[langflow](https://github.com/langflow-ai/langflow) - a powerful tool for building and deploying AI-powered agents and workflows -

[langchain](https://github.com/langchain-ai/langchain) - build context-aware reasoning applications -

[pi](https://github.com/earendil-works/pi) - AI agent toolkit: coding agent CLI, unified LLM API, TUI & web UI libraries, Slack bot, vLLM pods -

[anything-llm](https://github.com/Mintplex-Labs/anything-llm) - the all-in-one Desktop & Docker AI application with built-in RAG, AI agents, No-code agent builder, MCP compatibility, and more -

[autogen](https://github.com/microsoft/autogen) - a programming framework for agentic AI -

[crewAI](https://github.com/crewAIInc/crewAI) - a framework for orchestrating role-playing, autonomous AI agents -

[Flowise](https://github.com/FlowiseAI/Flowise) - build AI agents, visually -

[llama_index](https://github.com/run-llama/llama_index) - the leading framework for building LLM-powered agents over your data -

[agno](https://github.com/agno-agi/agno) - a full-stack framework for building Multi-Agent Systems with memory, knowledge and reasoning -

[sim](https://github.com/simstudioai/sim) - open-source platform to build and deploy AI agent workflows -

[openai-agents-python](https://github.com/openai/openai-agents-python) - a lightweight, powerful framework for multi-agent workflows -

[t3code](https://github.com/pingdotgg/t3code) - an agent harness control surface that enables control of the agents on your machine with a best-in-class mobile app -

[NemoClaw](https://github.com/NVIDIA/NemoClaw) - run OpenClaw more securely inside NVIDIA OpenShell with managed inference -

[pydantic-ai](https://github.com/pydantic/pydantic-ai) - a Python agent framework designed to help you quickly, confidently, and painlessly build production grade applications and workflows with Generative AI -

[SuperAGI](https://github.com/TransformerOptimus/SuperAGI) - an open-source framework to build, manage and run useful Autonomous AI Agents -

[camel](https://github.com/camel-ai/camel) - the first and the best multi-agent framework -

[agent-framework](https://github.com/microsoft/agent-framework) - a framework for building, orchestrating and deploying AI agents and multi-agent workflows with support for Python and .NET -

[superset](https://github.com/superset-sh/superset) - an agentic IDE to orchestrate 100+ coding agents in parallel -

[txtai](https://github.com/neuml/txtai) - all-in-one open-source AI framework for semantic search, LLM orchestration and language model workflows -

[archgw](https://github.com/katanemo/archgw) - a high-performance proxy server that handles the low-level work in building agents: like applying guardrails, routing prompts to the right agent, and unifying access to LLMs, etc. -

[genkit](https://github.com/genkit-ai/genkit) - open-source framework for building AI-powered apps in JavaScript, Go, and Python, built and used in production by Google -

[emdash](https://github.com/generalaction/emdash) - a desktop app for running AI coding agents in parallel -

[ClaraVerse](https://github.com/badboysm890/ClaraVerse) - privacy-first, fully local AI workspace with Ollama LLM chat, tool calling, agent builder, Stable Diffusion, and embedded n8n-style automation -

[NeMo-Agent-Toolkit](https://github.com/NVIDIA/NeMo-Agent-Toolkit) - an open-source library for efficiently connecting and optimizing teams of AI agents -

[ragbits](https://github.com/deepsense-ai/ragbits) - building blocks for rapid development of GenAI applications [Back to Table of Contents](#table-of-contents) ### Model Context Protocol -

[mindsdb](https://github.com/mindsdb/mindsdb) - federated query engine for AI - the only MCP Server you'll ever need -

[playwright-mcp](https://github.com/microsoft/playwright-mcp) - Playwright MCP server -

[github-mcp-server](https://github.com/github/github-mcp-server) - GitHub's official MCP Server -

[chrome-devtools-mcp](https://github.com/ChromeDevTools/chrome-devtools-mcp) - Chrome DevTools for coding agents -

[n8n-mcp](https://github.com/czlonkowski/n8n-mcp) - a MCP for Claude Desktop / Claude Code / Windsurf / Cursor to build n8n workflows for you -

[awslabs/mcp](https://github.com/awslabs/mcp) - AWS MCP Servers — helping you get the most out of AWS, wherever you use MCP -

[mcp-atlassian](https://github.com/sooperset/mcp-atlassian) - MCP server for Atlassian tools (Confluence, Jira) -

[dbhub](https://github.com/bytebase/dbhub) - zero-dependency, token-efficient database MCP server for Postgres, MySQL, SQL Server, MariaDB, SQLite [Back to Table of Contents](#table-of-contents) ### Retrieval-Augmented Generation -

[pathway](https://github.com/pathwaycom/pathway) - Python ETL framework for stream processing, real-time analytics, LLM pipelines and RAG -

[LightRAG](https://github.com/HKUDS/LightRAG) - simple and fast RAG -

[graphrag](https://github.com/microsoft/graphrag) - a modular graph-based RAG system -

[onyx](https://github.com/onyx-dot-app/onyx) - the AI platform connected to your company's docs, apps, and people -

[graphiti](https://github.com/getzep/graphiti) - build real-time knowledge graphs for AI Agents -

[haystack](https://github.com/deepset-ai/haystack) - AI orchestration framework to build customizable, production-ready LLM applications, best suited for building RAG, question answering, semantic search or conversational agent chatbots -

[vanna](https://github.com/vanna-ai/vanna) - an open-source Python RAG framework for SQL generation and related functionality -

[claude-context](https://github.com/zilliztech/claude-context) - make entire codebase the context for any coding agent -

[pipeshub-ai](https://github.com/pipeshub-ai/pipeshub-ai) - a fully extensible and explainable workplace AI platform for enterprise search and workflow automation [Back to Table of Contents](#table-of-contents) ### Coding Agents -

[opencode](https://github.com/sst/opencode) - a AI coding agent built for the terminal -

[zed](https://github.com/zed-industries/zed) - a next-generation code editor designed for high-performance collaboration with humans and AI -

[OpenHands](https://github.com/All-Hands-AI/OpenHands) - a platform for software development agents powered by AI -

[cline](https://github.com/cline/cline) - autonomous coding agent right in your IDE, capable of creating/editing files, executing commands, using the browser, and more with your permission every step of the way -

[goose](https://github.com/block/goose) - an open-source, extensible AI agent that goes beyond code suggestions -

[aider](https://github.com/Aider-AI/aider) - AI pair programming in your terminal -

[continue](https://github.com/continuedev/continue) - create, share, and use custom AI code assistants with our open-source IDE extensions and hub of models, rules, prompts, docs, and other building blocks -

[tabby](https://github.com/TabbyML/tabby) - an open-source GitHub Copilot alternative, set up your own LLM-powered code completion server -

[void](https://github.com/voideditor/void) - an open-source Cursor alternative, use AI agents on your codebase, checkpoint and visualize changes, and bring any model or host locally -

[crush](https://github.com/charmbracelet/crush) - the glamourous AI coding agent for your favourite terminal -

[kilocode](https://github.com/Kilo-Org/kilocode) - open source AI coding assistant for planning, building, and fixing code -

[Roo-Code](https://github.com/RooCodeInc/Roo-Code) - a whole dev team of AI agents in your code editor -

[humanlayer](https://github.com/humanlayer/humanlayer) - the best way to get AI coding agents to solve hard problems in complex codebases -

[openchamber](https://github.com/openchamber/openchamber) - Agentic Development Environment based on OpenCode AI agent -

[99](https://github.com/ThePrimeagen/99) - neovim AI agent done right -

[ProxyAI](https://github.com/carlrobertoh/ProxyAI) - the leading open-source AI copilot for JetBrains [Back to Table of Contents](#table-of-contents) ### Computer Use -

[open-interpreter](https://github.com/OpenInterpreter/open-interpreter) - a natural language interface for computers -

[cua](https://github.com/trycua/cua) - the Docker Container for Computer-Use AI Agents -

[OmniParser](https://github.com/microsoft/OmniParser) - a simple screen parsing tool towards pure vision based GUI agent -

[openwork](https://github.com/different-ai/openwork) - an open-source alternative to Claude Cowork, powered by OpenCode -

[Agent-S](https://github.com/simular-ai/Agent-S) - an open agentic framework that uses computers like a human -

[self-operating-computer](https://github.com/OthersideAI/self-operating-computer) - a framework to enable multimodal models to operate a computer -

[OpenRoom](https://github.com/MiniMax-AI/OpenRoom) - a browser-based desktop where AI Agent operates every app through natural language, from MiniMaxAI [Back to Table of Contents](#table-of-contents) ### Browser Automation -

[firecrawl](https://github.com/mendableai/firecrawl) - turn entire websites into LLM-ready markdown or structured data -

[browser-use](https://github.com/browser-use/browser-use) - make websites accessible for AI agents -

[puppeteer](https://github.com/puppeteer/puppeteer) - a JavaScript API for Chrome and Firefox -

[playwright](https://github.com/microsoft/playwright) - a framework for Web Testing and Automation -

[stagehand](https://github.com/browserbase/stagehand) - the AI Browser Automation Framework -

[nanobrowser](https://github.com/nanobrowser/nanobrowser) - open-source Chrome extension for AI-powered web automation -

[BrowserSkill](https://github.com/Tencent/BrowserSkill) - CLI + extension for browser automation across any shell-capable AI agent [Back to Table of Contents](#table-of-contents) ### Memory Management -

[mem0](https://github.com/mem0ai/mem0) - universal memory layer for AI Agents -

[mempalace](https://github.com/milla-jovovich/mempalace) - the highest-scoring AI memory system ever benchmarked -

[supermemory](https://github.com/supermemoryai/supermemory) - memory engine and app that is extremely fast, scalable -

[cognee](https://github.com/topoteretes/cognee) - memory for AI Agents in 5 lines of code -

[TencentDB-Agent-Memory](https://github.com/TencentCloud/tencentdb-agent-memory) - a team-level memory hub for AI Agents -

[letta](https://github.com/letta-ai/letta) - the stateful agents framework with memory, reasoning, and context management -

[LMCache](https://github.com/LMCache/LMCache) - supercharge your LLM with the fastest KV Cache Layer -

[memU](https://github.com/NevaMind-AI/memU) - an open-source memory framework for AI companions -

[reasoning-bank](https://github.com/google-research/reasoning-bank) - a memory mechanism for agents that learns from both successful and failed trajectories, with reasoning stored as memory content [Back to Table of Contents](#table-of-contents) ### Testing, Evaluation and Observability -

[langfuse](https://github.com/langfuse/langfuse) - an open-source LLM engineering platform: LLM Observability, metrics, evals, prompt management, playground, datasets. Integrates with OpenTelemetry, Langchain, OpenAI SDK, LiteLLM, and more -

[opik](https://github.com/comet-ml/opik) - debug, evaluate, and monitor your LLM applications, RAG systems, and agentic workflows with comprehensive tracing, automated evaluations, and production-ready dashboards -

[openllmetry](https://github.com/traceloop/openllmetry) - an open-source observability for your LLM application, based on OpenTelemetry -

[giskard](https://github.com/Giskard-AI/giskard) - an open-source evaluation & testing for AI & LLM systems -

[agenta](https://github.com/Agenta-AI/agenta) - an open-source LLMOps platform: prompt playground, prompt management, LLM evaluation, and LLM observability all in one place -

[Evaluator](https://github.com/NVIDIA-NeMo/evaluator) - open-source library for scalable, reproducible evaluation of AI models and benchmarks [Back to Table of Contents](#table-of-contents) ### Research -

[open-notebook](https://github.com/lfnovo/open-notebook) - an open-source implementation of Notebook LM with more flexibility and features -

[Perplexica](https://github.com/ItzCrazyKns/Perplexica) - an open-source alternative to Perplexity AI, the AI-powered search engine -

[gpt-researcher](https://github.com/assafelovic/gpt-researcher) - an LLM based autonomous agent that conducts deep local and web research on any topic and generates a long report with citations -

[SurfSense](https://github.com/MODSetter/SurfSense) - an open-source alternative to NotebookLM / Perplexity / Glean -

[RD-Agent](https://github.com/microsoft/RD-Agent) - automate the most critical and valuable aspects of the industrial R&D process -

[local-deep-researcher](https://github.com/langchain-ai/local-deep-researcher) - fully local web research and report writing assistant -

[local-deep-research](https://github.com/LearningCircuit/local-deep-research) - an AI-powered research assistant for deep, iterative research -

[maestro](https://github.com/murtaza-nasir/maestro) - an AI-powered research application designed to streamline complex research tasks [Back to Table of Contents](#table-of-contents) ### Training and Fine-tuning -

[heretic](https://github.com/p-e-w/heretic) - fully automatic censorship removal for language models -

[sentence-transformers](https://github.com/huggingface/sentence-transformers) - a Python library for using and training embedding and reranker models for applications like retrieval augmented generation, semantic search, and more -

[trl](https://github.com/huggingface/trl) - train transformer language models with reinforcement learning -

[OpenRLHF](https://github.com/OpenRLHF/OpenRLHF) - an easy-to-use, high-performance open-source RLHF framework built on Ray, vLLM, ZeRO-3 and HuggingFace Transformers, designed to make RLHF training simple and accessible -

[slime](https://github.com/THUDM/slime) - an LLM post-training framework for RL Scaling -

[Kiln](https://github.com/kiln-ai/kiln) - the easiest tool for fine-tuning LLM models, synthetic data generation, and collaborating on datasets -

[miles](https://github.com/radixark/miles) - an enterprise-facing reinforcement learning framework for LLM and VLM post-training, forked from and co-evolving with slime -

[OpenEnv](https://github.com/meta-pytorch/OpenEnv) - an interface library for RL post training with environments -

[RL](https://github.com/nvidia-nemo/rl) - scalable toolkit for efficient model reinforcement -

[augmentoolkit](https://github.com/e-p-armstrong/augmentoolkit) - train an open-source LLM on new facts -

[Gym](https://github.com/NVIDIA-NeMo/gym) - evaluate and improve models and agents using environments -

[SpecForge](https://github.com/sgl-project/SpecForge) - train speculative decoding models effortlessly and port them smoothly to SGLang serving [Back to Table of Contents](#table-of-contents) ### Security and Sandboxing -

[garak](https://github.com/NVIDIA/garak) - the LLM vulnerability scanner from NVIDIA -

[OpenShell](https://github.com/NVIDIA/OpenShell) - the safe, private runtime for autonomous AI agents from NVIDIA -

[Guardrails](https://github.com/NVIDIA-NeMo/Guardrails) - an open-source toolkit from NVIDIA for easily adding programmable guardrails to LLM-based conversational systems -

[CubeSandbox](https://github.com/TencentCloud/CubeSandbox) - instant, concurrent, secure & lightweight sandbox for AI agents [Back to Table of Contents](#table-of-contents) ### Miscellaneous -

[context7](https://github.com/upstash/context7) - up-to-date code documentation for LLMs and AI code editors -

[deepwiki-open](https://github.com/AsyncFuncAI/deepwiki-open) - open source DeepWiki: AI-powered wiki generator for GitHub/Gitlab/Bitbucket repositories -

[cai](https://github.com/aliasrobotics/cai) - Cybersecurity AI (CAI), the framework for AI Security -

[speakr](https://github.com/murtaza-nasir/speakr) - a personal, self-hosted web application designed for transcribing audio recordings -

[presenton](https://github.com/presenton/presenton) - an open-source AI presentation generator and API -

[OmniGen2](https://github.com/VectorSpaceLab/OmniGen2) - exploration to advanced multimodal generation -

[4o-ghibli-at-home](https://github.com/TheAhmadOsman/4o-ghibli-at-home) - a powerful, self-hosted AI photo stylizer built for performance and privacy -

[Observer](https://github.com/Roy3838/Observer) - local open-source micro-agents that observe, log and react, all while keeping your data private and secure -

[mobile-use](https://github.com/minitap-ai/mobile-use) - a powerful, open-source AI agent that controls your Android or IOS device using natural language -

[gabber](https://github.com/gabber-dev/gabber) - build AI applications that can see, hear, and speak using your screens, microphones, and cameras as inputs -

[promptcat](https://github.com/sevenreasons/promptcat) - a zero-dependency prompt manager/catalog/library in a single HTML file [Back to Table of Contents](#table-of-contents) ## Hardware -

[Alex Ziskind](https://www.youtube.com/@AZisk) - tests of pcs, laptops, gpus etc. capable of running LLMs -

[Digital Spaceport](https://www.youtube.com/@DigitalSpaceport) - reviews of various builds designed for LLM inference -

[Donato Capitella](https://www.youtube.com/@donatocapitella) - practical and insightful tutorials on running LLMs locally -

[JetsonHacks](https://www.youtube.com/@JetsonHacks) - information about developing on NVIDIA Jetson Development Kits -

[Miyconst](https://www.youtube.com/@Miyconst) - tests of various types of hardware capable of running LLMs - [Kolosal - LLM Memory calculator](https://www.kolosal.ai/memory-calculator) - estimate the RAM requirements of any GGUF model instantly - [LLM Inference VRAM & GPU Requirement Calculator](https://app.linpp2009.com/en/llm-gpu-memory-calculator) - calculate how many GPUs you need to deploy LLMs -

[ZLUDA](https://github.com/vosen/ZLUDA) - CUDA on non-NVIDIA GPUs - [Strix Halo AI Toolboxes](https://strix-halo-toolboxes.com/) - toolboxes for GenAI on AMD Ryzen AI MAX+: containerized environments for LLMs, Image Generation, and Fine-tuning - [Strix Halo Wiki](https://strixhalo.wiki/) - a website to gather important information and practical guides for systems powered by AMD Ryzen AI MAX and MAX+ processors -

[ai-notes](https://github.com/paudley/ai-notes) - random AI notes for working with local models or playing around with random machine learning bits [Back to Table of Contents](#table-of-contents) ## Tutorials ### Models -

[Let's reproduce GPT-2 (124M)](https://www.youtube.com/watch?v=l8pRSuU81PU) -

[nanochat](https://github.com/karpathy/nanochat) - a full-stack implementation of an LLM like ChatGPT in a single, clean, minimal, hackable, dependency-lite codebase, designed to run on a single 8XH100 node via scripts like speedrun.sh, that run the entire pipeline start to end -

[Knowledge Distillation: How LLMs train each other](https://www.youtube.com/watch?v=jrJKRYAdh7I) -

[gguf-docs](https://github.com/iuliaturc/gguf-docs) - Docs for GGUF quantization (unofficial) -

[Embarrassingly Simple Self-Distillation Improves Code Generation](https://arxiv.org/pdf/2604.01193) [Back to Table of Contents](#table-of-contents) ### Prompt Engineering -

[Prompt Engineering Guide](https://github.com/dair-ai/Prompt-Engineering-Guide) - guides, papers, lecture, notebooks and resources for prompt engineering -

[Prompt Engineering by NirDiamant](https://github.com/NirDiamant/Prompt_Engineering) - a comprehensive collection of tutorials and implementations for Prompt Engineering techniques, ranging from fundamental concepts to advanced strategies -

[Prompting guide 101](https://services.google.com/fh/files/misc/gemini-for-google-workspace-prompting-guide-101.pdf) - a quick-start handbook for effective prompts by Google -

[Prompt Engineering by Google](https://drive.google.com/file/d/1AbaBYbEa_EbPelsT40-vj64L-2IwUJHy/view) - prompt engineering by Google -

[Prompt Engineering by Anthropic](https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/overview) - prompt engineering by Anthropic -

[Prompt Engineering Interactive Tutorial](https://github.com/anthropics/courses/blob/master/prompt_engineering_interactive_tutorial/README.md) - Prompt Engineering Interactive Tutorial by Anthropic -

[Real world prompting](https://github.com/anthropics/courses/blob/master/real_world_prompting/README.md) - real world prompting tutorial by Anthropic -

[Prompt evaluations](https://github.com/anthropics/courses/blob/master/prompt_evaluations/README.md) - prompt evaluations course by Anthropic -

[system-prompts-and-models-of-ai-tools](https://github.com/x1xhlol/system-prompts-and-models-of-ai-tools) - a collection of system prompts extracted from AI tools -

[system_prompts_leaks](https://github.com/asgeirtj/system_prompts_leaks) - a collection of extracted System Prompts from popular chatbots like ChatGPT, Claude & Gemini -

[Prompt from Codex](https://github.com/openai/codex/blob/main/codex-rs/core/prompt.md) - Prompt used to steer behavior of OpenAI's Codex [Back to Table of Contents](#table-of-contents) ### Context Engineering -

[Context-Engineering](https://github.com/davidkimai/Context-Engineering) - a frontier, first-principles handbook inspired by Karpathy and 3Blue1Brown for moving beyond prompt engineering to the wider discipline of context design, orchestration, and optimization -

[Awesome-Context-Engineering](https://github.com/Meirtz/Awesome-Context-Engineering) - a comprehensive survey on Context Engineering: from prompt engineering to production-grade AI systems [Back to Table of Contents](#table-of-contents) ### Inference -

[vLLM Production Stack](https://github.com/vllm-project/production-stack) - vLLM’s reference system for K8S-native cluster-wide deployment with community-driven performance optimization [Back to Table of Contents](#table-of-contents) ### Agents -

[superpowers](https://github.com/obra/superpowers) - an agentic skills framework & software development methodology that works -

[GenAI Agents](https://github.com/NirDiamant/GenAI_Agents) - tutorials and implementations for various Generative AI Agent techniques -

[500+ AI Agent Projects](https://github.com/ashishpatel26/500-AI-Agents-Projects) - a curated collection of AI agent use cases across various industries -

[12-Factor Agents](https://github.com/humanlayer/12-factor-agents) - principles for building reliable LLM applications -

[Agents towards production](https://github.com/NirDiamant/agents-towards-production) - end-to-end, code-first tutorials covering every layer of production-grade GenAI agents, guiding you from spark to scale with proven patterns and reusable blueprints for real-world launches -

[agents.md](https://github.com/agentsmd/agents.md) - a simple, open format for guiding coding agents -

[Agent Skills](https://github.com/agentskills/agentskills) - a simple, open format for giving agents new capabilities and expertise -

[skills](https://github.com/huggingface/skills) - Hugging Face Skills are definitions for AI/ML tasks like dataset creation, model training and evaluation -

[LLM Agents & Ecosystem Handbook](https://github.com/oxbshw/LLM-Agents-Ecosystem-Handbook) - one-stop handbook for building, deploying, and understanding LLM agents with 60+ skeletons, tutorials, ecosystem guides, and evaluation tools -

[601 real-world gen AI use cases](https://cloud.google.com/transform/101-real-world-generative-ai-use-cases-from-industry-leaders) - 601 real-world gen AI use cases from the world's leading organizations by Google -

[A practical guide to building agents](https://cdn.openai.com/business-guides-and-resources/a-practical-guide-to-building-agents.pdf) - a practical guide to building agents by OpenAI [Back to Table of Contents](#table-of-contents) ### Retrieval-Augmented Generation -

[Pathway AI Pipelines](https://github.com/pathwaycom/llm-app) - ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data -

[RAG Techniques](https://github.com/NirDiamant/RAG_Techniques) - various advanced techniques for Retrieval-Augmented Generation (RAG) systems -

[Controllable RAG Agent](https://github.com/NirDiamant/Controllable-RAG-Agent) - an advanced Retrieval-Augmented Generation (RAG) solution for complex question answering that uses sophisticated graph based algorithm to handle the tasks -

[LangChain RAG Cookbook](https://github.com/lokeswaran-aj/langchain-rag-cookbook) - a collection of modular RAG techniques, implemented in LangChain + Python [Back to Table of Contents](#table-of-contents) ### Miscellaneous -

[local-llm](https://github.com/jamesob/local-llm) - everything jamesob knows about running LLMs locally - [Self-hosted AI coding that just works](https://www.reddit.com/r/LocalLLaMA/comments/1lt4y1z/selfhosted_ai_coding_that_just_works/) [Back to Table of Contents](#table-of-contents) ## Communities -

[LocalLLaMA](https://www.reddit.com/r/LocalLLaMA) -

[LLMDevs](https://www.reddit.com/r/LLMDevs) -

[LocalLLM](https://www.reddit.com/r/LocalLLM) -

[LocalAIServers](https://www.reddit.com/r/LocalAIServers/) -

[GenAI monitor](https://t.me/genaimon) - monitoring updates & fresh releases related to LLMs, diffusion models and Generative AI [Back to Table of Contents](#table-of-contents) # Contributing We welcome contributions! Please see [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines on how to get started.