372f664c63
* feat: add pos id to flex attention for packing part 1 * feat: update to include sliding window mask patch * fix: suppress MatMul8bitLt: inputs will be cast from warnings * fix: remove redundant flex attention patch * chore: update olmo docs * feat: add validator patch for cross entropy
39 lines
1.5 KiB
Markdown
39 lines
1.5 KiB
Markdown
# Finetune Allenai's Olmo 3 with Axolotl
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[Olmo 3](https://huggingface.co/collections/allenai/olmo-3) are a family of 7B and 32B models open source models trained by The Allen Institute for Artificial Intelligence.
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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).
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2. Install [Cut Cross Entropy](https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy) to reduce training VRAM usage.
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3. Run the finetuning example:
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```bash
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axolotl train examples/olmo3/olmo3-7b-qlora.yaml
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```
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This uses about 11.3 GiB VRAM. Let us know how it goes. Happy finetuning! 🚀
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### TIPS
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- The example config can be re-used for Olmo and Olmo 2.
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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 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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## Optimization Guides
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Please check the [Optimizations doc](https://docs.axolotl.ai/docs/optimizations.html).
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## Related Resources
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- [Olmo 3 Blog](https://allenai.org/blog/olmo3)
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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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