SOTA low-bit LLM quantization (INT8/FP8/INT4/FP4/NF4) & sparsity; leading model compression techniques on TensorFlow, PyTorch, and ONNX Runtime
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Updated
Jun 12, 2024 - Python
SOTA low-bit LLM quantization (INT8/FP8/INT4/FP4/NF4) & sparsity; leading model compression techniques on TensorFlow, PyTorch, and ONNX Runtime
AIMET is a library that provides advanced quantization and compression techniques for trained neural network models.
Train, Evaluate, Optimize, Deploy Computer Vision Models via OpenVINO™
Unify Efficient Fine-Tuning of 100+ LLMs
AIMET GitHub pages documentation
On-device LLM Inference Powered by X-Bit Quantization
Model Compression/Inference Made Easy
🤗 Optimum Intel: Accelerate inference with Intel optimization tools
Fast inference engine for Transformer models
Neural Network Compression Framework for enhanced OpenVINO™ inference
This is the official PyTorch implementation of "LLM-QBench: A Benchmark Towards the Best Practice for Post-training Quantization of Large Language Models", and also an efficient LLM compression tool with various advanced compression methods, supporting multiple inference backends.
SOTA Weight-only Quantization Algorithm for LLMs. This is official implementation of "Optimize Weight Rounding via Signed Gradient Descent for the Quantization of LLMs"
Model Compression Toolkit (MCT) is an open source project for neural network model optimization under efficient, constrained hardware. This project provides researchers, developers, and engineers advanced quantization and compression tools for deploying state-of-the-art neural networks.
Official code of the ICML24 paper: "Winner-takes-all learners are geometry-aware conditional density estimators"
🚀 Accelerate training and inference of 🤗 Transformers and 🤗 Diffusers with easy to use hardware optimization tools
Implementation for the different ML tasks on Kaggle platform with GPUs.
[CVPR 2024 Highlight] This is the official PyTorch implementation of "TFMQ-DM: Temporal Feature Maintenance Quantization for Diffusion Models".
PEFT is a wonderful tool that enables training a very large model in a low resource environment. Quantization and PEFT will enable widespread adoption of LLM.
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