Unify Efficient Fine-Tuning of 100+ LLMs
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Updated
May 29, 2024 - Python
Unify Efficient Fine-Tuning of 100+ LLMs
SOTA Weight-only Quantization Algorithm for LLMs. This is official implementation of "Optimize Weight Rounding via Signed Gradient Descent for the Quantization of LLMs"
🤗 Optimum Intel: Accelerate inference with Intel optimization tools
🚀 Accelerate training and inference of 🤗 Transformers and 🤗 Diffusers with easy to use hardware optimization tools
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.
A collection of hand on notebook for LLMs practitioner
Fast inference engine for Transformer models
SOTA low-bit LLM quantization (INT8/FP8/INT4/FP4/NF4) & sparsity; leading model compression techniques on TensorFlow, PyTorch, and ONNX Runtime
Train, Evaluate, Optimize, Deploy Computer Vision Models via OpenVINO™
Lightweight Python PIL-libimagequant/pngquant interface with autonomous lib look-up.
AIMET is a library that provides advanced quantization and compression techniques for trained neural network models.
On-device LLM Inference Powered by X-Bit Quantization
A Python package for extending the official PyTorch that can easily obtain performance on Intel platform
Self-Created Tools to convert ONNX files (NCHW) to TensorFlow/TFLite/Keras format (NHWC). The purpose of this tool is to solve the massive Transpose extrapolation problem in onnx-tensorflow (onnx-tf). I don't need a Star, but give me a pull request.
Brevitas: neural network quantization in PyTorch
Dataflow compiler for QNN inference on FPGAs
Neural Network Compression Framework for enhanced OpenVINO™ inference
Official implementation of Half-Quadratic Quantization (HQQ)
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