AutoGPTQ
ray-llm
AutoGPTQ | ray-llm | |
---|---|---|
19 | 4 | |
3,806 | 1,146 | |
5.0% | 4.5% | |
9.3 | 8.6 | |
4 days ago | 9 days ago | |
Python | Python | |
MIT License | Apache License 2.0 |
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AutoGPTQ
- Setting up LLAMA2 70B Chat locally
- Experience of setting up LLAMA 2 70B Chat locally
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GPT-4 Details Leaked
Deploying the 60B version is a challenge though and you might need to apply 4-bit quantization with something like https://github.com/PanQiWei/AutoGPTQ or https://github.com/qwopqwop200/GPTQ-for-LLaMa . Then you can improve the inference speed by using https://github.com/turboderp/exllama .
If you prefer to use an "instruct" model à la ChatGPT (i.e. that does not need few-shot learning to output good results) you can use something like this: https://huggingface.co/TheBloke/Wizard-Vicuna-30B-Uncensored...
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Loader Types
AutoGPTQ: an attempt at standardizing GPTQ-for-LLaMa and turning it into a library that is easier to install and use, and that supports more models. https://github.com/PanQiWei/AutoGPTQ
- WizardLM-33B-V1.0-Uncensored
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Any help converting an interesting .bin model to 4 bit 128g GPTQ? Bloke?
Just use the script: https://github.com/PanQiWei/AutoGPTQ/blob/main/examples/quantization/quant_with_alpaca.py
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LLM.int8(): 8-Bit Matrix Multiplication for Transformers at Scale
In the wild, people tend to use GTPQ quantization for pure GPU inference: https://github.com/PanQiWei/AutoGPTQ
And ggml's quant for CPU inference with some offload, which just got updated to a more GPTQ-like method days ago: https://github.com/ggerganov/llama.cpp/pull/1684
Some other runtimes like Apache TVM also have their own quant implementations: https://github.com/mlc-ai/mlc-llm
For training, 4-bit bitsandbytes is SOTA, as far as I know.
TBH I'm not sure why this November paper is being linked. Few are running 8 bit models when they could fit a better 3-5 bit model in the same memory pool.
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Introducing Basaran: self-hosted open-source alternative to the OpenAI text completion API
Instead of integrating GPTQ-for-Lllama, use AutoGPTQ instead.
- AutoGPTQ - An easy-to-use LLMs quantization package with user-friendly apis, based on GPTQ algorithm
ray-llm
- Aviary: Compare Open Source LLMs for cost, latency and quality
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[N] Aviary: Comparing Open Source LLMs for cost, latency and quality
Aviary is a open source utility to compare leading OSS LLMs. https://aviary.anyscale.com/
- Anyscale's Aviary is a dashboard for evaluating Open Source LLMs
- Aviary simplifies OSS LLM eval and deployment
What are some alternatives?
exllama - A more memory-efficient rewrite of the HF transformers implementation of Llama for use with quantized weights.
Cornucopia-LLaMA-Fin-Chinese - 聚宝盆(Cornucopia): 中文金融系列开源可商用大模型,并提供一套高效轻量化的垂直领域LLM训练框架(Pretraining、SFT、RLHF、Quantize等)
llama.cpp - LLM inference in C/C++
safe-rlhf - Safe RLHF: Constrained Value Alignment via Safe Reinforcement Learning from Human Feedback
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
AtomGPT - 中英文预训练大模型,目标与ChatGPT的水平一致
basaran - Basaran is an open-source alternative to the OpenAI text completion API. It provides a compatible streaming API for your Hugging Face Transformers-based text generation models.
HugNLP - CIKM2023 Best Demo Paper Award. HugNLP is a unified and comprehensive NLP library based on HuggingFace Transformer. Please hugging for NLP now!😊
GPTQ-for-LLaMa - 4 bits quantization of LLaMA using GPTQ
Ray - Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
self-refine - LLMs can generate feedback on their work, use it to improve the output, and repeat this process iteratively.
pinferencia - Python + Inference - Model Deployment library in Python. Simplest model inference server ever.