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| 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) | |
| ``` | |
| You can also use multiple GPUs with `pre_layer` if using the oobabooga fork of GPTQ, eg `--pre_layer 30 60` will load a LLaMA-30B model half onto your first GPU and half onto your second, or `--pre_layer 20 40` will load 20 layers onto GPU-0, 20 layers onto GPU-1, and 20 layers offloaded to CPU. | |
| ## 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 | |
| ``` | |