IST-DASLab / gptqLinks
Code for the ICLR 2023 paper "GPTQ: Accurate Post-training Quantization of Generative Pretrained Transformers".
☆2,254Updated last year
Alternatives and similar repositories for gptq
Users that are interested in gptq are comparing it to the libraries listed below
Sorting:
- [MLSys 2024 Best Paper Award] AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration☆3,431Updated 6 months ago
- [ICML 2023] SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models☆1,600Updated last year
- 4 bits quantization of LLaMA using GPTQ☆3,075Updated last year
- AutoAWQ implements the AWQ algorithm for 4-bit quantization with a 2x speedup during inference. Documentation:☆2,312Updated 8 months ago
- Code for the ICML 2023 paper "SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot".☆866Updated last year
- MII makes low-latency and high-throughput inference possible, powered by DeepSpeed.☆2,092Updated 7 months ago
- [ICML 2024] Break the Sequential Dependency of LLM Inference Using Lookahead Decoding☆1,316Updated 11 months ago
- An easy-to-use LLMs quantization package with user-friendly apis, based on GPTQ algorithm.☆5,028Updated 9 months ago
- [ICLR2024 spotlight] OmniQuant is a simple and powerful quantization technique for LLMs.☆888Updated 2 months ago
- Ongoing research training transformer language models at scale, including: BERT & GPT-2☆2,224Updated 5 months ago
- Ongoing research training transformer language models at scale, including: BERT & GPT-2☆1,433Updated last year
- Medusa: Simple Framework for Accelerating LLM Generation with Multiple Decoding Heads☆2,699Updated last year
- S-LoRA: Serving Thousands of Concurrent LoRA Adapters☆1,897Updated 2 years ago
- [NeurIPS 2023] LLM-Pruner: On the Structural Pruning of Large Language Models. Support Llama-3/3.1, Llama-2, LLaMA, BLOOM, Vicuna, Baich…☆1,105Updated last year
- [ICML 2024] SqueezeLLM: Dense-and-Sparse Quantization☆713Updated last year
- A library for accelerating Transformer models on NVIDIA GPUs, including using 8-bit and 4-bit floating point (FP8 and FP4) precision on H…☆3,132Updated this week
- ☆553Updated last year
- Automatically Discovering Fast Parallelization Strategies for Distributed Deep Neural Network Training☆1,859Updated last week
- FP16xINT4 LLM inference kernel that can achieve near-ideal ~4x speedups up to medium batchsizes of 16-32 tokens.☆1,005Updated last year
- A simple and effective LLM pruning approach.☆847Updated last year
- Kernl lets you run PyTorch transformer models several times faster on GPU with a single line of code, and is designed to be easily hackab…☆1,586Updated last week
- YaRN: Efficient Context Window Extension of Large Language Models☆1,668Updated last year
- Serving multiple LoRA finetuned LLM as one☆1,140Updated last year
- Transformer related optimization, including BERT, GPT☆6,392Updated last year
- A family of open-sourced Mixture-of-Experts (MoE) Large Language Models☆1,654Updated last year
- Automatically split your PyTorch models on multiple GPUs for training & inference☆656Updated 2 years ago
- Fast Inference Solutions for BLOOM☆566Updated last year
- Finetuning Large Language Models on One Consumer GPU in 2 Bits☆734Updated last year
- Official implementation of Half-Quadratic Quantization (HQQ)☆912Updated last month
- Large language models (LLMs) made easy, EasyLM is a one stop solution for pre-training, finetuning, evaluating and serving LLMs in JAX/Fl…☆2,512Updated last year