mirror of
https://github.com/oobabooga/text-generation-webui.git
synced 2024-11-25 09:19:23 +01:00
143 lines
5.0 KiB
Markdown
143 lines
5.0 KiB
Markdown
In 4-bit mode, models are loaded with just 25% of their regular VRAM usage. So LLaMA-7B fits into a 6GB GPU, and LLaMA-30B fits into a 24GB GPU.
|
|
|
|
This is possible thanks to [@qwopqwop200](https://github.com/qwopqwop200/GPTQ-for-LLaMa)'s adaptation of the GPTQ algorithm for LLaMA: https://github.com/qwopqwop200/GPTQ-for-LLaMa
|
|
|
|
GPTQ is a clever quantization algorithm that lightly reoptimizes the weights during quantization so that the accuracy loss is compensated relative to a round-to-nearest quantization. See the paper for more details: https://arxiv.org/abs/2210.17323
|
|
|
|
## GPTQ-for-LLaMa branches
|
|
|
|
Different branches of GPTQ-for-LLaMa are available:
|
|
|
|
| Branch | Comment |
|
|
|----|----|
|
|
| [Old CUDA branch (recommended)](https://github.com/oobabooga/GPTQ-for-LLaMa/) | The fastest branch, works on Windows and Linux. |
|
|
| [Up-to-date triton branch](https://github.com/qwopqwop200/GPTQ-for-LLaMa) | Slightly more precise than the old CUDA branch from 13b upwards, significantly more precise for 7b. 2x slower for small context size and only works on Linux. |
|
|
| [Up-to-date CUDA branch](https://github.com/qwopqwop200/GPTQ-for-LLaMa/tree/cuda) | As precise as the up-to-date triton branch, 10x slower than the old cuda branch for small context size. |
|
|
|
|
Overall, I recommend using the old CUDA branch. It is included by default in the one-click-installer for this web UI.
|
|
|
|
## Installation
|
|
|
|
### Step 0: install nvcc
|
|
|
|
```
|
|
conda activate textgen
|
|
conda install -c conda-forge cudatoolkit-dev
|
|
```
|
|
|
|
The command above takes some 10 minutes to run and shows no progress bar or updates along the way.
|
|
|
|
See this issue for more details: https://github.com/oobabooga/text-generation-webui/issues/416#issuecomment-1475078571
|
|
|
|
### Step 1: install GPTQ-for-LLaMa
|
|
|
|
Clone the GPTQ-for-LLaMa repository into the `text-generation-webui/repositories` subfolder and install it:
|
|
|
|
```
|
|
mkdir repositories
|
|
cd repositories
|
|
git clone https://github.com/oobabooga/GPTQ-for-LLaMa.git -b cuda
|
|
cd GPTQ-for-LLaMa
|
|
python setup_cuda.py install
|
|
```
|
|
|
|
You are going to need to have a C++ compiler installed into your system for the last command. On Linux, `sudo apt install build-essential` or equivalent is enough.
|
|
|
|
If you want to you to use the up-to-date CUDA or triton branches instead of the old CUDA branch, use these commands:
|
|
|
|
```
|
|
cd repositories
|
|
rm -r GPTQ-for-LLaMa
|
|
pip uninstall -y quant-cuda
|
|
git clone https://github.com/qwopqwop200/GPTQ-for-LLaMa.git -b cuda
|
|
...
|
|
```
|
|
|
|
```
|
|
cd repositories
|
|
rm -r GPTQ-for-LLaMa
|
|
pip uninstall -y quant-cuda
|
|
git clone https://github.com/qwopqwop200/GPTQ-for-LLaMa.git -b triton
|
|
...
|
|
```
|
|
|
|
|
|
https://github.com/qwopqwop200/GPTQ-for-LLaMa
|
|
|
|
### Step 2: get the pre-converted weights
|
|
|
|
* Converted without `group-size` (better for the 7b model): https://github.com/oobabooga/text-generation-webui/pull/530#issuecomment-1483891617
|
|
* Converted with `group-size` (better from 13b upwards): https://github.com/oobabooga/text-generation-webui/pull/530#issuecomment-1483941105
|
|
|
|
⚠️ The tokenizer files in the sources above may be outdated. Make sure to obtain the universal LLaMA tokenizer as described [here](https://github.com/oobabooga/text-generation-webui/blob/main/docs/LLaMA-model.md#option-1-pre-converted-weights).
|
|
|
|
### Step 3: Start the web UI:
|
|
|
|
For the models converted without `group-size`:
|
|
|
|
```
|
|
python server.py --model llama-7b-4bit
|
|
```
|
|
|
|
For the models converted with `group-size`:
|
|
|
|
```
|
|
python server.py --model llama-13b-4bit-128g
|
|
```
|
|
|
|
The command-line flags `--wbits` and `--groupsize` are automatically detected based on the folder names, but you can also specify them manually like
|
|
|
|
```
|
|
python server.py --model llama-13b-4bit-128g --wbits 4 --groupsize 128
|
|
```
|
|
|
|
## CPU offloading
|
|
|
|
It is possible to offload part of the layers of the 4-bit model to the CPU with the `--pre_layer` flag. The higher the number after `--pre_layer`, the more layers will be allocated to the GPU.
|
|
|
|
With this command, I can run llama-7b with 4GB VRAM:
|
|
|
|
```
|
|
python server.py --model llama-7b-4bit --pre_layer 20
|
|
```
|
|
|
|
This is the performance:
|
|
|
|
```
|
|
Output generated in 123.79 seconds (1.61 tokens/s, 199 tokens)
|
|
```
|
|
|
|
## Using LoRAs in 4-bit mode
|
|
|
|
At the moment, this feature is not officially supported by the relevant libraries, but a patch exists and is supported by this web UI: https://github.com/johnsmith0031/alpaca_lora_4bit
|
|
|
|
In order to use it:
|
|
|
|
1. Make sure that your requirements are up to date:
|
|
|
|
```
|
|
cd text-generation-webui
|
|
pip install -r requirements.txt --upgrade
|
|
```
|
|
|
|
2. Clone `johnsmith0031/alpaca_lora_4bit` into the repositories folder:
|
|
|
|
```
|
|
cd text-generation-webui/repositories
|
|
git clone https://github.com/johnsmith0031/alpaca_lora_4bit
|
|
```
|
|
|
|
⚠️ I have tested it with the following commit specifically: `2f704b93c961bf202937b10aac9322b092afdce0`
|
|
|
|
3. Install https://github.com/sterlind/GPTQ-for-LLaMa with this command:
|
|
|
|
```
|
|
pip install git+https://github.com/sterlind/GPTQ-for-LLaMa.git@lora_4bit
|
|
```
|
|
|
|
4. Start the UI with the `--monkey-patch` flag:
|
|
|
|
```
|
|
python server.py --model llama-7b-4bit-128g --listen --lora tloen_alpaca-lora-7b --monkey-patch
|
|
```
|