17fc747f99
* fix: uv leftover docs * fix: docker build failing * chore: doc * fix: remove old pytorch build * fix: stop recommend flash-attn optional, let transformers pull * fix: remove ring flash attention from image * fix: quotes [skip ci] * chore: naming [skip ci]
65 lines
2.2 KiB
Markdown
65 lines
2.2 KiB
Markdown
# Finetune IBM's Granite 4.0 with Axolotl
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[Granite 4.0](https://huggingface.co/collections/ibm-granite/granite-40-language-models) are a family of open source models trained by IBM Research.
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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 Granite4 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 2.9.1 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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uv pip install --no-build-isolation -e '.'
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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/granite4/granite-4.0-tiny-fft.yaml
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```
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This config uses about 40.8GiB VRAM.
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Let us know how it goes. Happy finetuning! 🚀
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### TIPS
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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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### Limitation
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Adapter finetuning does not work at the moment. It would error with
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```bash
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RuntimeError: mat1 and mat2 shapes cannot be multiplied (4096x3072 and 1x1179648)
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```
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In addition, if adapter training works, `lora_target_linear: true` will not work due to:
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```bash
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ValueError: Target module GraniteMoeHybridParallelExperts() is not supported.
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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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- [Granite Docs](https://www.ibm.com/granite/docs/models/granite)
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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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