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Unified Efficient Fine-Tuning of 100+ LLMs & VLMs (ACL 2024)

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中文LLaMA&Alpaca大语言模型+本地CPU/GPU训练部署 (Chinese LLaMA & Alpaca LLMs)

Accessible large language models via k-bit quantization for PyTorch.

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.

An easy-to-use LLMs quantization package with user-friendly apis, based on GPTQ algorithm.

Fast inference engine for Transformer models

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.

🚀 Accelerate inference and training of 🤗 Transformers, Diffusers, TIMM and Sentence Transformers with easy to use hardware optimization tools

Your Cheat Sheet for AI Engineering Interview – Questions and Answers.

PyTorch native quantization for training and inference

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

QKeras: a quantization deep learning library for Tensorflow Keras

Legend of Elya — N64 game with a real 6.36M-parameter ternary transformer on the VR4300 MIPS III CPU. Zelda-style dungeon, AI NPCs, byte-level inference at 1.23 tok/s scalar / 2.19 tok/s on the RSP overlay (measured under ares, never on silicon). Built with libdragon.