AutoGPTQ
lora-llm-qa-g
| AutoGPTQ | lora-llm-qa-g | |
|---|---|---|
| 19 | 2 | |
| 3,806 | 5 | |
| 5.0% | - | |
| 9.3 | 1.9 | |
| 4 days ago | about 1 year ago | |
| Python | TypeScript | |
| MIT License | - |
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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
lora-llm-qa-g
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How to qlora 33B model on a GPU with 24GB of VRAM
I used GPT-3 to generate the list of questions and answers. Here is the source code of the tool I used. https://github.com/mzbac/lora-llm-qa-g
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Fine-tuning the 13B wizard model with a small amount of dataset and achieving GPT-4 level results?
FIY, my dataset -> https://github.com/mzbac/lora-llm-qa-g/blob/main/dataset/combined.json
What are some alternatives?
exllama - A more memory-efficient rewrite of the HF transformers implementation of Llama for use with quantized weights.
qlora - QLoRA: Efficient Finetuning of Quantized LLMs
llama.cpp - LLM inference in C/C++
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
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.
GPTQ-for-LLaMa - 4 bits quantization of LLaMA using GPTQ
self-refine - LLMs can generate feedback on their work, use it to improve the output, and repeat this process iteratively.
ray-llm - RayLLM - LLMs on Ray
transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
catai - UI for 🦙model . Run AI assistant locally ✨
gptq-cuda-api
course - The Hugging Face course on Transformers