39 lines
1.5 KiB
Markdown
39 lines
1.5 KiB
Markdown
# Finetune ArceeAI's Trinity with Axolotl
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[Trinity](https://huggingface.co/collections/arcee-ai/trinity) is a family of open weight MoE models trained by Arcee.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 main from the [installation guide](https://docs.axolotl.ai/docs/installation.html#sec-edge-build).
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2. Run the finetuning example:
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```bash
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axolotl train examples/trinity/trinity-nano-preview-qlora.yaml
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```
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This config uses about 24.9 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 Arcee.ai team recommends `top_p: 0.75`, `temperature: 0.15`, `top_k: 50`, and `min_p: 0.06`.
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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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- [Trinity Blog](https://www.arcee.ai/blog/the-trinity-manifesto)
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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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