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Awesome List
A curated list of awesome LLM frameworks, libraries and software.
GitHub stars and default-branch commits for uhub/awesome-llm.
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Unified Efficient Fine-Tuning of 100+ LLMs & VLMs (ACL 2024)
中文LLaMA&Alpaca大语言模型+本地CPU/GPU训练部署 (Chinese LLaMA & Alpaca LLMs)
A 2.78-trillion-parameter Kimi K3 running inference on a single CPU in 8.24 GB of RAM. Portable C99: no BLAS, no framework, no GPU.
Accessible large language models via k-bit quantization for PyTorch.
An easy-to-use LLMs quantization package with user-friendly apis, based on GPTQ algorithm.
Transformers-compatible library for applying various compression algorithms to LLMs for optimized deployment with vLLM
[ICLR2025, ICML2025, NeurIPS2025 Spotlight] Quantized Attention achieves speedup of 2-5x compared to FlashAttention, without losing end-to-end metrics across language, image, and video models.
Your Cheat Sheet for AI Engineering Interview – Questions and Answers.
SOTA low-bit LLM quantization (INT8/FP8/MXFP8/INT4/MXFP4/NVFP4) & sparsity; leading model compression techniques on PyTorch, TensorFlow, and ONNX Runtime
Build, personalize and control your own LLMs. From data pre-processing to fine-tuning, xTuring provides an easy way to personalize open-source LLMs. Join our discord community: https://discord.gg/TgHXuSJEk6
Mastering Applied AI, One Concept at a Time
Run Mixtral-8x7B models in Colab or consumer desktops