How to fine-tune gpt-oss-120b with Baseten and Axolotl

This post details how to fine-tune the gpt-oss-120b model using Baseten's Training platform and the Axolotl fine-tuning runtime. It outlines the prerequisites, including data quality and evaluation, and provides specific configuration examples for both Baseten's `config.py` (defining compute, caching, and checkpointing) and Axolotl's YAML file (specifying base model, dataset, and training parameters like flash attention and gradient checkpointing). It also demonstrates launching the training job via the `truss train push` command and deploying checkpoints for production.