Awesome GitHub Repositories4-Bit Quantization Tools

FlexLLMGen is an inference engine and runtime designed to run large language models on a single GPU by combining weight compression with tensor offloading. It reduces model weight memory usage by approximately 70% through 4-bit quantization, and stores model parameters, attention cache, and hidden states across GPU, CPU, and disk to fit models larger than available GPU memory. The project distinguishes itself through a throughput-oriented batching approach that processes multiple generation requests together in large batches to maximize throughput on a single GPU. It also supports distributed

Lit-llama is a PyTorch-based implementation framework for the LLaMA language model, providing a system for pre-training, fine-tuning, and high-performance inference. It includes a pre-training pipeline for creating foundational language models from scratch and tools for running pretrained weights to generate natural text and predict sequences. The project provides specialized toolkits for parameter-efficient fine-tuning using low-rank adaptation and lightweight adapters. It also includes a quantization library that reduces model memory footprints through four-bit and eight-bit precision to en

Baichuan-7B is an open-source 7 billion parameter bilingual Transformer model designed for text generation and few-shot learning across Chinese and English. It is built on a large Transformer architecture trained on a bilingual corpus, enabling it to produce coherent text in both languages from a single model. The model incorporates several optimization techniques that distinguish it from standard large language models. It uses rotary position embeddings that can extrapolate to longer sequences than seen during training, allowing context extension beyond the original 4096-token training lengt

AutoGPTQ 是一个模型压缩框架，旨在减少大语言模型的内存占用并提高推理速度。它利用 GPTQ 算法压缩模型权重，使这些模型能够在 VRAM 有限的硬件上运行。 该工具包提供了一个架构量化流水线，支持为各种神经网络架构集成自定义模型类。它包含一个带有优化内核的混合精度推理引擎，可在部署期间加速矩阵乘法。 该框架涵盖了从校准和量化到下游准确性评估的完整权重压缩工作流。这些工具通过在基准任务上比较量化模型与原始权重的输出来衡量性能损失。

AutoGPTQ 是一个模型压缩工具包和训练后量化框架，旨在减少大语言模型的内存占用。它利用 GPTQ 算法压缩神经网络权重，降低硬件要求并减少 VRAM 使用量。 该项目通过提供优化内核来提高 Token 生成速度，从而充当推理加速器。它具有模型架构扩展性，允许通过可配置模式将量化能力添加到新的模型结构中。 该框架涵盖了全面的量化流水线，包括层级权重压缩、基于校准的缩放估计以及特定精度的内存映射。它还包括用于模型性能评估的系统，以衡量量化对语言和摘要任务准确性的影响。

Nunchaku is a 4-bit model quantization library and diffusion model inference engine designed to run large-scale neural networks on consumer GPUs. It functions as a GPU-accelerated optimizer that reduces VRAM usage and increases inference speed through weight compression and memory management. The project utilizes low-rank weight decomposition and SVD weight quantization to compress models to four-bit precision while maintaining visual fidelity. It employs kernel-level operator fusion to minimize data movement and hardware-aware precision mapping to adjust numerical precision based on the unde