Seed-OSS

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
pip3 install packaging setuptools wheel ninja
pip3 install --no-build-isolation 'axolotl[flash-attn]>=0.12.0'

# 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.