axolotl/examples/seed-oss

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. You need to install from main as Seed-OSS is only on nightly or use our latest Docker images.

   Here is an example of how to install from main for pip:

# Ensure you have Pytorch installed (Pytorch 2.6.0 min)
git clone https://github.com/axolotl-ai-cloud/axolotl.git
cd axolotl

uv sync --extra deepspeed
uv pip install flash-attn --no-build-isolation

# 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

• Multi-GPU Training
• Multi-Node Training
• LoRA Optimizations

Related Resources

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