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* feat: move to uv first * fix: update doc to uv first * fix: merge dev/tests into uv pyproject * fix: update docker docs to match current config * fix: migrate examples to readme * fix: add llmcompressor to conflict * feat: rec uv sync with lockfile for dev/ci * fix: update docker docs to clarify how to use uv images * chore: docs * fix: use system python, no venv * fix: set backend cpu * fix: only set for installing pytorch step * fix: remove unsloth kernel and installs * fix: remove U in tests * fix: set backend in deps too * chore: test * chore: comments * fix: attempt to lock torch * fix: workaround torch cuda and not upgraded * fix: forgot to push * fix: missed source * fix: nightly upstream loralinear config * fix: nightly phi3 long rope not work * fix: forgot commit * fix: test phi3 template change * fix: no more requirements * fix: carry over changes from new requirements to pyproject * chore: remove lockfile per discussion * fix: set match-runtime * fix: remove unneeded hf hub buildtime * fix: duplicate cache delete on nightly * fix: torchvision being overridden * fix: migrate to uv images * fix: leftover from merge * fix: simplify base readme * fix: update assertion message to be clearer * chore: docs * fix: change fallback for cicd script * fix: match against main exactly * fix: peft 0.19.1 change * fix: e2e test * fix: ci * fix: e2e test
70 lines
2.5 KiB
Markdown
70 lines
2.5 KiB
Markdown
# Finetune Gemma-3n with Axolotl
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Gemma-3n is a family of multimodal models from Google found on [HuggingFace](https://huggingface.co/collections/google/gemma-3n-685065323f5984ef315c93f4). This guide shows how to fine-tune it with Axolotl.
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## Getting started
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1. Install Axolotl following the [installation guide](https://docs.axolotl.ai/docs/installation.html).
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Here is an example of how to install from pip:
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```bash
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# Ensure you have Pytorch installed (Pytorch 2.6.0 min)
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uv pip install --no-build-isolation 'axolotl[flash-attn]>=0.12.0'
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```
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2. In addition to Axolotl's requirements, Gemma-3n requires:
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```bash
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uv pip install timm==1.0.17
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# for loading audio data
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uv pip install librosa==0.11.0
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```
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3. Download sample dataset files
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```bash
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# for text + vision + audio only
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wget https://huggingface.co/datasets/Nanobit/text-vision-audio-2k-test/resolve/main/African_elephant.jpg
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wget https://huggingface.co/datasets/Nanobit/text-vision-audio-2k-test/resolve/main/En-us-African_elephant.oga
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```
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4. Run the finetuning example:
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```bash
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# text only
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axolotl train examples/gemma3n/gemma-3n-e2b-qlora.yml
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# text + vision
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axolotl train examples/gemma3n/gemma-3n-e2b-vision-qlora.yml
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# text + vision + audio
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axolotl train examples/gemma3n/gemma-3n-e2b-vision-audio-qlora.yml
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```
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Let us know how it goes. Happy finetuning! 🚀
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WARNING: The loss and grad norm will be much higher than normal. We suspect this to be inherent to the model as of the moment. If anyone would like to submit a fix for this, we are happy to take a look.
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### TIPS
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- You can run a full finetuning 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 text 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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- The multimodal dataset format follows the OpenAI multi-content Messages format as seen [here](https://docs.axolotl.ai/docs/multimodal.html#dataset-format).
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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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- [Gemma 3n Blog](https://ai.google.dev/gemma/docs/gemma-3n)
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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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