b71482cec5
* feat: add hunyuan cce support
* feat: update cce docs
* feat: add multipack support for granite and hunyuan
* feat: add hunyuan docs and example config
* feat: update readme instructions to include CCE installation
* fix: chat template log appearing despite tokenizer already having template
* feat: add vram usage
* fix: remove duplicate cce install
* fix: use latest commit of PR in case rebased/pushed
* Revert "fix: use latest commit of PR in case rebased/pushed"
This reverts commit 8b60aa00de.
* feat: update doc as upstream merged
86 lines
3.1 KiB
Markdown
86 lines
3.1 KiB
Markdown
# Finetune HunYuan with Axolotl
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Tencent released a family of opensource models called HunYuan with varying parameter scales of 0.5B, 1.8B, 4B, and 7B scale for both Pre-trained and Instruct variants. The models can be found at [HuggingFace](https://huggingface.co/collections/tencent/hunyuan-dense-model-6890632cda26b19119c9c5e7). 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 HunYuan 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/hunyuan/hunyuan-v1-dense-qlora.yaml
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```
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This config uses about 4.7 GB VRAM.
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Let us know how it goes. Happy finetuning! 🚀
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### Dataset
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HunYuan Instruct models can choose to enter a slow think or fast think pattern. For best performance on fine-tuning their Instruct models, your dataset should be adjusted to match their pattern.
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```python
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# fast think pattern
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messages = [
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{"role": "system", "content": "You are a helpful assistant."},
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{"role": "user", "content": "/no_think What color is the sun?" },
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{"role": "assistant", "content": "<think>\n\n</think>\n<answer>\nThe sun is yellow.\n</answer>"}
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]
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# slow think pattern
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messages = [
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{"role": "system", "content": "You are a helpful assistant."},
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{"role": "user", "content": "/no_think What color is the sun?" },
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{"role": "assistant", "content": "<think>\nThe user is asking about the color of the sun. I need to ...\n</think>\n<answer>\nThe sun is yellow.\n</answer>"}
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]
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```
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### TIPS
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- For inference, the official Tencent team recommends
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```json
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{
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"do_sample": true,
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"top_k": 20,
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"top_p": 0.8,
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"repetition_penalty": 1.05,
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"temperature": 0.7
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}
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```
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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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- [Tencent HunYuan Blog](https://hunyuan.tencent.com/)
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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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