lxe/README.md

## README.md

      
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              README.md
            
          
    How to get oobabooga/text-generation-webui running on Windows or Linux with LLaMa-30b 4bit mode via GPTQ-for-LLaMa on an RTX 3090 start to finish.

This guide actually works well for linux too. Just don't bother with the powershell envs


Download prerequisites

Download and install miniconda
(Windows Only) Download and install Visual Studio 2019 Build Tools

Click on the latest BuildTools link, Select Desktop Environment with C++ when installing)


(Windows Only) Open the Conda Powershell.

Alternatively, open the regular PowerShell and activate the Conda environment:
pwsh -ExecutionPolicy ByPass -NoExit -Command "& ~\miniconda3\shell\condabin\conda-hook.ps1 ; conda activate ~\miniconda3"'

Sometimes for some reason the GPTQ compilation fails if 'cl' is not in the path. You can try using the x64 Native Tools Command Prompt for VS 2019 shell instead or, load both conda and VS build tools shell like this:
cmd /k '"C:\Program Files (x86)\Microsoft Visual Studio\2019\BuildTools\VC\Auxiliary\Build\vcvars64.bat" && pwsh -ExecutionPolicy ByPass -NoExit -Command "& ~\miniconda3\shell\condabin\conda-hook.ps1 ; conda activate ~\miniconda3"'


You'll need the CUDA compiler and torch that matches the version in order to build the GPTQ extesions which allows for 4 bit prequantized models. Create a conda env and install python, cuda, and torch that matches the cuda version, as well as ninja for fast compilation
conda create -n tgwui
conda activate tgwui
conda install python=3.10
Installing pytorch and cuda is the hardest part of machine learning
I've come up with this install line from the following sources:

https://pytorch.org/get-started/locally/#start-locally
https://docs.nvidia.com/cuda/cuda-installation-guide-microsoft-windows/index.html#installing-previous-cuda-releases

conda install cuda pytorch torchvision torchaudio pytorch-cuda=11.7 -c pytorch -c nvidia/label/cuda-11.7.0
python -c 'import torch; print(torch.cuda.is_available())'


Download text-generation-webui and GPTQ-for-LLaMa
git clone https://github.com/oobabooga/text-generation-webui.git
cd text-generation-webui
mkdir repositories
cd repositories
git clone https://github.com/qwopqwop200/GPTQ-for-LLaMa.git
cd GPTQ-for-LLaMa


Build and install gptq package and CUDA kernel (you should be in the GPTQ-for-LLaMa directory)
pip install ninja
python setup_cuda.py install


Install the text-generation-webui dependencies
cd ../..
pip install -r requirements.txt


Download the 13b model from huggingface
python download-model.py decapoda-research/llama-13b-hf

This will take some time. After it's done, rename the folder to llama-13b
The llama-13b prequantized is available here. Download the llama-13b-4bit.pt file and place it in models directory, alongside the llama-13b folder.


Run the text-generation-webui with llama-13b to test it out
python server.py --cai-chat --load-in-4bit --model llama-13b --no-stream


Download the hf version 30b model from huggingface
python download-model.py decapoda-research/llama-30b-hf

You can download the pre-quantized 4 bit versions of the model here.
Alternatively, you'll need to quantize it yourself using GPTQ-for-LLaMa (this will take a while):
cd ../repositories/GPTQ-for-LLaMa
pip install datasets
HUGGING_FACE_HUB_TOKEN={your huggingface token} CUDA_VISIBLE_DEVICES=0 python llama.py ../../models/llama-30b-hf c4 --wbits 4 --save llama-30b-4bit.pt

Place the llama30b-4bit.pt in models in models directory, alongside the llama-30b folder.


Run the text-generation-webui with llama-30b
python server.py --cai-chat --load-in-4bit --model llama-30b --no-stream