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84 lines
3.4 KiB
Markdown
84 lines
3.4 KiB
Markdown
This is an example of fine-tuning a LLM model to use with [LocalAI](https://github.com/mudler/LocalAI) written by [@mudler](https://github.com/mudler).
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Specifically, this example shows how to use [axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) to fine-tune a LLM model to consume with LocalAI as a `gguf` model.
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A notebook is provided that currently works on _very small_ datasets on Google colab on the free instance. It is far from producing good models, but it gives a sense of how to use the code to use with a better dataset and configurations, and how to use the model produced with LocalAI. [](https://colab.research.google.com/github/mudler/LocalAI/blob/master/examples/e2e-fine-tuning/notebook.ipynb)
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## Requirements
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For this example you will need at least a 12GB VRAM of GPU and a Linux box.
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The notebook is tested on Google Colab with a Tesla T4 GPU.
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## Clone this directory
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Clone the repository and enter the example directory:
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```bash
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git clone http://github.com/mudler/LocalAI
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cd LocalAI/examples/e2e-fine-tuning
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```
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## Install dependencies
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```bash
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# Install axolotl and dependencies
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git clone https://github.com/OpenAccess-AI-Collective/axolotl && pushd axolotl && git checkout 797f3dd1de8fd8c0eafbd1c9fdb172abd9ff840a && popd #0.3.0
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pip install packaging
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pushd axolotl && pip install -e '.[flash-attn,deepspeed]' && popd
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# https://github.com/oobabooga/text-generation-webui/issues/4238
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pip install https://github.com/Dao-AILab/flash-attention/releases/download/v2.3.0/flash_attn-2.3.0+cu117torch2.0cxx11abiFALSE-cp310-cp310-linux_x86_64.whl
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```
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Configure accelerate:
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```bash
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accelerate config default
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```
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## Fine-tuning
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We will need to configure axolotl. In this example is provided a file to use `axolotl.yaml` that uses openllama-3b for fine-tuning. Copy the `axolotl.yaml` file and edit it to your needs. The dataset needs to be next to it as `dataset.json`. The format used is `completion` which is a list of JSON objects with a `text` field with the full text to train the LLM with.
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If you have a big dataset, you can pre-tokenize it to speedup the fine-tuning process:
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```bash
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# Optional pre-tokenize (run only if big dataset)
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python -m axolotl.cli.preprocess axolotl.yaml
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```
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Now we are ready to start the fine-tuning process:
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```bash
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# Fine-tune
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accelerate launch -m axolotl.cli.train axolotl.yaml
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```
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After we have finished the fine-tuning, we merge the Lora base with the model:
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```bash
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# Merge lora
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python3 -m axolotl.cli.merge_lora axolotl.yaml --lora_model_dir="./qlora-out" --load_in_8bit=False --load_in_4bit=False
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```
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And we convert it to the gguf format that LocalAI can consume:
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```bash
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# Convert to gguf
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git clone https://github.com/ggerganov/llama.cpp.git
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pushd llama.cpp && make LLAMA_CUBLAS=1 && popd
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# We need to convert the pytorch model into ggml for quantization
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# It crates 'ggml-model-f16.bin' in the 'merged' directory.
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pushd llama.cpp && python convert.py --outtype f16 \
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../qlora-out/merged/pytorch_model-00001-of-00002.bin && popd
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# Start off by making a basic q4_0 4-bit quantization.
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# It's important to have 'ggml' in the name of the quant for some
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# software to recognize it's file format.
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pushd llama.cpp && ./quantize ../qlora-out/merged/ggml-model-f16.gguf \
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../custom-model-q4_0.bin q4_0
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```
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Now you should have ended up with a `custom-model-q4_0.bin` file that you can copy in the LocalAI models directory and use it with LocalAI.
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