c51d6b06c3
* feat: add apertus, glm4v, glm4v_moe cce * fix: arcee docs * feat: add apertus * feat: added vram usage * fix: add apertus note * feat: update doc on apertus xielu * fix: add monkeypatch for xielu activation issue * fix: simplify env * feat: pin commit * feat: add packing * chore: move patch calling * Update examples/apertus/README.md Co-authored-by: salman <salman.mohammadi@outlook.com> * Update examples/apertus/README.md Co-authored-by: salman <salman.mohammadi@outlook.com> * Update examples/apertus/README.md Co-authored-by: salman <salman.mohammadi@outlook.com> --------- Co-authored-by: salman <salman.mohammadi@outlook.com>
57 lines
2.3 KiB
Markdown
57 lines
2.3 KiB
Markdown
# Finetune ArceeAI's AFM with Axolotl
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[Arcee Foundation Models (AFM)](https://huggingface.co/collections/arcee-ai/afm-45b-68823397c351603014963473) are a family of 4.5B parameter open weight 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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Thanks to the team at Arcee.ai for using Axolotl in supervised fine-tuning the AFM model.
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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 AFM 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==23.2 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. Run the finetuning example:
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```bash
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axolotl train examples/arcee/afm-4.5b-qlora.yaml
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```
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This config uses about 7.8GiB 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: 0.95`, `temperature: 0.5`, `top_k: 50`, and `repeat_penalty: 1.1`.
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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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- [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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- [AFM Blog](https://docs.arcee.ai/arcee-foundation-models/introduction-to-arcee-foundation-models)
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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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