A list of papers, docs, codes about model quantization. This repo is aimed to provide the info for model quantization research, we are continuously improving the project. Welcome to PR the works (papers, repositories) that are missed by the repo.

A curated list for Efficient Large Language Models

Inferflow is an efficient and highly configurable inference engine for large language models (LLMs).

模型压缩的小白入门教程

This repository contains notebooks that show the usage of TensorFlow Lite for quantizing deep neural networks.

A list of papers, docs, codes about efficient AIGC. This repo is aimed to provide the info for efficient AIGC research, including language and vision, we are continuously improving the project. Welcome to PR the works (papers, repositories) that are missed by the repo.

[WINNER! 🏆] Psychopathology FER Assistant. Because mental health matters. My project submission for #TFWorld TF 2.0 Challenge at Devpost.

[ICML 2023] This project is the official implementation of our accepted ICML 2023 paper BiBench: Benchmarking and Analyzing Network Binarization.

[NeurIPS 2023 Spotlight] This project is the official implementation of our accepted NeurIPS 2023 (spotlight) paper QuantSR: Accurate Low-bit Quantization for Efficient Image Super-Resolution.

Chat to LLaMa 2 that also provides responses with reference documents over vector database. Locally available model using GPTQ 4bit quantization.

The official implementation of the ICML 2023 paper OFQ-ViT

A tutorial of model quantization using TensorFlow

PyTorch implementation of "BiDense: Binarization for Dense Prediction," A binary neural network for dense prediction tasks.

Quantization is a technique to reduce the computational and memory costs of running inference by representing the weights and activations with low-precision data types like 8-bit integer (int8) instead of the usual 32-bit floating point (float32).

Annotated Notebooks to dive into Self-Attention, In-Context Learning, RAG, Knowledge-Graphs, Fine-Tuning, Model Optimization, and many more.

Unlocking the Power of Generative AI: In-Context Learning, Instruction Fine-Tuning and Reinforcement Learning Fine-Tuning.

This project explores generating high-quality images using depth maps and conditioning techniques like Canny edges, leveraging Stable Diffusion and ControlNet models. It focuses on optimizing image generation with different aspect ratios, inference steps to balance speed and quality.

Fine-tuning Pretrained Deep Learning Models to Classify Low Quality Images of Land Vehicles. - Ajustement de modèles de deep learning préentraînés pour classifier des images faible qualité de véhicules terrestres.

Unofficial implementation of NCNet using flax and jax