axolotl/examples/seed-oss/README.md

Finetune ByteDance's Seed-OSS with Axolotl

Seed-OSS are a series of 36B parameter open source models trained by ByteDance's Seed Team.

This guide shows how to fine-tune it with Axolotl with multi-turn conversations and proper masking.

Getting started

1. Install Axolotl following the installation guide.

   Here is an example of how to install from pip:

   # Ensure you have a compatible version of Pytorch installed
   uv pip install --no-build-isolation 'axolotl>=0.16.1'
   
   # Install Cut Cross Entropy
   python scripts/cutcrossentropy_install.py | sh
   

2. Run the finetuning example:

axolotl train examples/seed-oss/seed-oss-36b-qlora.yaml

This config uses about 27.7 GiB VRAM.

Let us know how it goes. Happy finetuning! 🚀

TIPS

• For inference, the official Seed Team recommends top_p=0.95 and temperature=1.1.
• You can run a full finetuning by removing the adapter: qlora and load_in_4bit: true from the config.
• Read more on how to load your own dataset at docs.
• The dataset format follows the OpenAI Messages format as seen here.

Optimization Guides

Please check the Optimizations doc.

Related Resources

• ByteDance Seed Website
• Axolotl Docs
• Axolotl Website
• Axolotl GitHub
• Axolotl Discord