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Update GPTQ-models-(4-bit-mode).md

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docs/GPTQ-models-(4-bit-mode).md
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@ -6,14 +6,6 @@ GPTQ is a clever quantization algorithm that lightly reoptimizes the weights dur
There are two ways of loading GPTQ models in the web UI at the moment:
* Using GPTQ-for-LLaMa directly:
* faster CPU offloading
* faster multi-GPU inference
* supports loading LoRAs using a monkey patch
* included by default in the one-click installers
* requires you to manually figure out the wbits/groupsize/model_type parameters for the model to be able to load it
* supports either only cuda or only triton depending on the branch
* Using AutoGPTQ:
* supports more models
* standardized (no need to guess any parameter)
@ -21,8 +13,59 @@ There are two ways of loading GPTQ models in the web UI at the moment:
* ~no wheels are presently available so it requires manual compilation~
* supports loading both triton and cuda models
* Using GPTQ-for-LLaMa directly:
* faster CPU offloading
* faster multi-GPU inference
* supports loading LoRAs using a monkey patch
* requires you to manually figure out the wbits/groupsize/model_type parameters for the model to be able to load it
* supports either only cuda or only triton depending on the branch
For creating new quantizations, I recommend using AutoGPTQ: https://github.com/PanQiWei/AutoGPTQ
## AutoGPTQ
### Installation
No additional steps are necessary as AutoGPTQ is already in the `requirements.txt` for the webui. If you still want or need to install it manually for whatever reason, these are the commands:
```
conda activate textgen
git clone https://github.com/PanQiWei/AutoGPTQ.git && cd AutoGPTQ
pip install .
```
The last command requires `nvcc` to be installed (see the [instructions above](https://github.com/oobabooga/text-generation-webui/blob/main/docs/GPTQ-models-(4-bit-mode).md#step-1-install-nvcc)).
### Usage
When you quantize a model using AutoGPTQ, a folder containing a filed called `quantize_config.json` will be generated. Place that folder inside your `models/` folder and load it with the `--autogptq` flag:
```
python server.py --autogptq --model model_name
```
Alternatively, check the `autogptq` box in the "Model" tab of the UI before loading the model.
### Offloading
In order to do CPU offloading or multi-gpu inference with AutoGPTQ, use the `--gpu-memory` flag. It is currently somewhat slower than offloading with the `--pre_layer` option in GPTQ-for-LLaMA.
For CPU offloading:
```
python server.py --autogptq --gpu-memory 3000MiB --model model_name
```
For multi-GPU inference:
```
python server.py --autogptq --gpu-memory 3000MiB 6000MiB --model model_name
```
### Using LoRAs with AutoGPTQ
Not supported yet.
## GPTQ-for-LLaMa
GPTQ-for-LLaMa is the original adaptation of GPTQ for the LLaMA model. It was made possible by [@qwopqwop200](https://github.com/qwopqwop200/GPTQ-for-LLaMa): https://github.com/qwopqwop200/GPTQ-for-LLaMa
@ -108,23 +151,21 @@ These are models that you can simply download and place in your `models` folder.
### Starting the web UI:
Use the `--gptq-for-llama` flag.
For the models converted without `group-size`:
```
python server.py --model llama-7b-4bit
python server.py --model llama-7b-4bit --gptq-for-llama
```
For the models converted with `group-size`:
```
python server.py --model llama-13b-4bit-128g
python server.py --model llama-13b-4bit-128g --gptq-for-llama --wbits 4 --groupsize 128
```
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
```
The command-line flags `--wbits` and `--groupsize` are automatically detected based on the folder names in many cases.
### CPU offloading
@ -171,46 +212,4 @@ pip install git+https://github.com/sterlind/GPTQ-for-LLaMa.git@lora_4bit
python server.py --model llama-7b-4bit-128g --listen --lora tloen_alpaca-lora-7b --monkey-patch
```
## AutoGPTQ
### Installation
No additional steps are necessary as AutoGPTQ is already in the `requirements.txt` for the webui. If you still want or need to install it manually for whatever reason, these are the commands:
```
conda activate textgen
git clone https://github.com/PanQiWei/AutoGPTQ.git && cd AutoGPTQ
pip install .
```
The last command requires `nvcc` to be installed (see the [instructions above](https://github.com/oobabooga/text-generation-webui/blob/main/docs/GPTQ-models-(4-bit-mode).md#step-1-install-nvcc)).
### Usage
When you quantize a model using AutoGPTQ, a folder containing a filed called `quantize_config.json` will be generated. Place that folder inside your `models/` folder and load it with the `--autogptq` flag:
```
python server.py --autogptq --model model_name
```
Alternatively, check the `autogptq` box in the "Model" tab of the UI before loading the model.
### Offloading
In order to do CPU offloading or multi-gpu inference with AutoGPTQ, use the `--gpu-memory` flag. It is currently somewhat slower than offloading with the `--pre_layer` option in GPTQ-for-LLaMA.
For CPU offloading:
```
python server.py --autogptq --gpu-memory 3000MiB --model model_name
```
For multi-GPU inference:
```
python server.py --autogptq --gpu-memory 3000MiB 6000MiB --model model_name
```
### Using LoRAs with AutoGPTQ
Not supported yet.