41 lines
1.4 KiB
Markdown
41 lines
1.4 KiB
Markdown
# Finetune Z.ai's GLM-4.7-Flash with Axolotl
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[GLM-4.7-Flash](https://huggingface.co/zai-org/GLM-4.7-Flash) is a 30B-A3B MoE model.
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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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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/glm4.7-flash/glm4.7-flash-qlora.yaml
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```
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This config uses about X 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 Z.ai team recommends `top_p: 0.95`, `temperature: 1.0`, and `max_new_tokens: 131072`.
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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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## 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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- [GLM-4.7-Flash on HuggingFace](https://huggingface.co/zai-org/GLM-4.7-Flash)
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- [GLM-4.7 Blog](https://z.ai/blog/glm-4.7)
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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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