[ICCV 2023] Efficient Joint Optimization of Layer-Adaptive Weight Pruning in Deep Neural Networks

Python implementation of REMBO built on GPyTorch.

My Implementation of Q-Sparse: All Large Language Models can be Fully Sparsely-Activated

A framework for few-shot evaluation of language models.

Code for paper: "QuIP: 2-Bit Quantization of Large Language Models With Guarantees"

[ACL 2024 Demo] Official GitHub repo for UltraEval: An open source framework for evaluating foundation models.

Code Repository of Evaluating Quantized Large Language Models

GPU operators for sparse tensor operations

The best way to write secure and reliable applications. Write nothing; deploy nowhere.

Code repo for the paper "SpinQuant LLM quantization with learned rotations"

[ACL 2024] A novel QAT with Self-Distillation framework to enhance ultra low-bit LLMs.

Infrastructure to enable deployment of ML models to low-power resource-constrained embedded targets (including microcontrollers and digital signal processors).

Code for Neurips24 paper: QuaRot, an end-to-end 4-bit inference of large language models.

一本 GPT4 生成的单词书📚，超过 8000 个单词分析，涵盖了词义、例句、词根词缀、变形、文化背景、记忆技巧和小故事

深度学习面试宝典（含数学、机器学习、深度学习、计算机视觉、自然语言处理和SLAM等方向）

[ICML 2024] KIVI: A Tuning-Free Asymmetric 2bit Quantization for KV Cache

QAQ: Quality Adaptive Quantization for LLM KV Cache

[NeurIPS 2024] KVQuant: Towards 10 Million Context Length LLM Inference with KV Cache Quantization

Samples for CUDA Developers which demonstrates features in CUDA Toolkit

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

[NeurIPS'23 Oral] Visual Instruction Tuning (LLaVA) built towards GPT-4V level capabilities and beyond.

Official PyTorch implementation of QA-LoRA

[ICML 2024] BiLLM: Pushing the Limit of Post-Training Quantization for LLMs

List of papers related to neural network quantization in recent AI conferences and journals.

Implementation of "BitNet: Scaling 1-bit Transformers for Large Language Models" in pytorch

For releasing code related to compression methods for transformers, accompanying our publications

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