111 lines
3.8 KiB
Markdown
111 lines
3.8 KiB
Markdown
# Finetune Swiss-AI's Apertus with Axolotl
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[Apertus](https://huggingface.co/collections/swiss-ai/apertus-llm-68b699e65415c231ace3b059) is a family of opensource models trained by Swiss-ai.
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This guide shows how to fine-tune it with Axolotl with multi-turn conversations and proper masking.
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## Getting started
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1. Install Axolotl following the [installation guide](https://docs.axolotl.ai/docs/installation.html). You need to install from main as Apertus is only on nightly or use our latest [Docker images](https://docs.axolotl.ai/docs/docker.html).
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Here is an example of how to install from main for pip:
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```bash
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# Ensure you have Pytorch installed (Pytorch 2.6.0 min)
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git clone https://github.com/axolotl-ai-cloud/axolotl.git
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cd axolotl
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pip3 install packaging==26.0 setuptools==75.8.0 wheel ninja
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pip3 install --no-build-isolation -e '.[flash-attn]'
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# Install CCE https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy
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python scripts/cutcrossentropy_install.py | sh
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```
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2. (Optional, highly recommended) Install XIELU CUDA
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```bash
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## Recommended for reduced VRAM and faster speeds
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# Point to CUDA toolkit directory
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# For those using our Docker image, use the below path.
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export CUDA_HOME=/usr/local/cuda
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pip3 install git+https://github.com/nickjbrowning/XIELU@59d6031 --no-build-isolation --no-deps
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```
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For any installation errors, see [XIELU Installation Issues](#xielu-installation-issues)
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3. Run the finetuning example:
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```bash
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axolotl train examples/apertus/apertus-8b-qlora.yaml
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```
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This config uses about 8.7 GiB VRAM.
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Let us know how it goes. Happy finetuning! 🚀
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### Tips
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- For inference, the official Apertus team recommends `top_p=0.9` and `temperature=0.8`.
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- You can instead use full paremter fine-tuning by removing the `adapter: qlora` and `load_in_4bit: true` from the config.
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- Read more on how to load your own dataset at [docs](https://docs.axolotl.ai/docs/dataset_loading.html).
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- The dataset format follows the OpenAI Messages format as seen [here](https://docs.axolotl.ai/docs/dataset-formats/conversation.html#chat_template).
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### XIELU Installation Issues
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#### `ModuleNotFoundError: No module named 'torch'`
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Please check these one by one:
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- Running in correct environment
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- Env has PyTorch installed
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- CUDA toolkit is at `CUDA_HOME`
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If those didn't help, please try the below solutions:
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1. Pass env for CMAKE and try install again:
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```bash
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Python_EXECUTABLE=$(which python) pip3 install git+https://github.com/nickjbrowning/XIELU@59d6031 --no-build-isolation --no-deps
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```
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2. Git clone the repo and manually hardcode python path:
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```bash
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git clone https://github.com/nickjbrowning/XIELU
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cd xielu
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git checkout 59d6031
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cd xielu
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nano CMakeLists.txt # or vi depending on your preference
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```
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```diff
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execute_process(
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- COMMAND ${Python_EXECUTABLE} -c "import torch.utils; print(torch.utils.cmake_prefix_path)"
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+ COMMAND /root/miniconda3/envs/py3.11/bin/python -c "import torch.utils; print(torch.utils.cmake_prefix_path)"
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RESULT_VARIABLE TORCH_CMAKE_PATH_RESULT
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OUTPUT_VARIABLE TORCH_CMAKE_PATH_OUTPUT
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ERROR_VARIABLE TORCH_CMAKE_PATH_ERROR
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)
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```
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```bash
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pip3 install . --no-build-isolation --no-deps
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```
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## Optimization Guides
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- [Multi-GPU Training](https://docs.axolotl.ai/docs/multi-gpu.html)
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- [Multi-Node Training](https://docs.axolotl.ai/docs/multi-node.html)
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- [LoRA Optimizations](https://docs.axolotl.ai/docs/lora_optims.html)
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## Related Resources
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- [Apertus Tech Report](https://github.com/swiss-ai/apertus-tech-report/blob/main/Apertus_Tech_Report.pdf)
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- [Axolotl Docs](https://docs.axolotl.ai)
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- [Axolotl Website](https://axolotl.ai)
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- [Axolotl GitHub](https://github.com/axolotl-ai-cloud/axolotl)
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- [Axolotl Discord](https://discord.gg/7m9sfhzaf3)
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