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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
## AutoGPTQ
4-bit GPTQ models reduce VRAM usage by about 75%. So LLaMA-7B fits into a 6GB GPU, and LLaMA-30B fits into a 24GB GPU.
AutoGPTQ is the recommended way to create new quantized models: https://github.com/PanQiWei/AutoGPTQ
## Overview
### Installation
There are two ways of loading GPTQ models in the web UI at the moment:
To load a model quantized with AutoGPTQ in the web UI, you need to first manually install the AutoGPTQ library:
* 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
```
conda activate textgen
git clone https://github.com/PanQiWei/AutoGPTQ.git && cd AutoGPTQ
pip install .
```
* Using AutoGPTQ:
* supports more models
* standardized (no need to guess any parameter)
* is a proper Python library
* no wheels are presently available so it requires manual compilation
* supports loading both triton and cuda models
The last command requires `nvcc` to be installed (see the [instructions below](https://github.com/oobabooga/text-generation-webui/blob/main/docs/GPTQ-models-(4-bit-mode).md#step-0-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 (more on that below).
For CPU offloading:
```
python server.py --autogptq --gpu-memory 3000MiB --model model_name
```
For multi-GPU:
```
python server.py --autogptq --gpu-memory 3000MiB 6000MiB --model model_name
```
### Using LoRAs with AutoGPTQ
Not supported yet.
For creating new quantizations, I recommend using AutoGPTQ: https://github.com/PanQiWei/AutoGPTQ
## GPTQ-for-LLaMa
GPTQ-for-LLaMa is the original adaptation of GPTQ for the LLaMA model. It was made by [@qwopqwop200](https://github.com/qwopqwop200/GPTQ-for-LLaMa) in this repository: https://github.com/qwopqwop200/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
Different branches of GPTQ-for-LLaMa are currently available, including:
@ -109,9 +86,6 @@ 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
@ -183,3 +157,47 @@ 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
To load a model quantized with AutoGPTQ in the web UI, you need to first manually install the AutoGPTQ library:
```
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-0-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.