Fine-tune gpt-oss with Unsloth

OpenAI's gpt-oss models have achieved state-of-the-art performance in text, reasoning, math, and code, with gpt-oss-120b and gpt-oss-20b models outperforming their predecessors while being more VRAM efficient, enabling their use on free platforms like Colab. The Unsloth team has introduced enhancements to the models, including faster fine-tuning and reduced VRAM requirements, and has uploaded various versions on Hugging Face. The Harmony library has been released for improved parsing and tokenization, offering an alternative to existing chat templates. Additionally, the team has been working on making the gpt-oss more efficient by implementing custom training functions and addressing issues with the MXFP4 format, which currently lacks support for training. Unsloth's optimizations allow for training the models with significantly lower VRAM usage compared to other methods, and collaboration with the Falcon and LiquidAI teams has expanded support to their latest models.

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