Train your own Decision Model with Unsloth | Unsloth Documentation
Fine-tune LLMs like Qwen and Gemma to make decisions with calibrated probabilities.
Machine Learning Tutorial
Summary
The page is a step‑by‑step tutorial for fine‑tuning OpenAI's gpt‑oss models using the Unsloth library. It covers both local and Google Colab workflows, hyperparameter settings, LoRA adapters, and hardware requirements.
Highlights
- Provides a free Colab notebook for quick fine‑tuning of gpt‑oss‑20b and larger models.
- Claims up to 1.5× faster training, 70% less VRAM usage, and 10x longer context lengths versus other FA2 implementations.
- Details hardware requirements for QLoRA (14 GB VRAM) and BF16 LoRA (44 GB VRAM) configurations.
- Explains how to enable 4‑bit loading or full‑precision BF16 mode and adjust max sequence length.
- Includes guidance on monitoring loss, avoiding overfitting, and using LoRA adapters for parameter‑efficient training.
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Context
Audience
Machine learning engineers, data scientists, and AI researchers who want to fine‑tune large language models efficiently on local hardware or free cloud resources
DomainMachine Learning
Formatonline documentation with code snippets, screenshots, and downloadable notebooks
Accessfree online
Topics
Related
Unsloth GitHub repositoryGoogle ColabLoRA fine‑tuning guideQLoRA vs BF16 comparison chartModel card for gpt‑oss‑20b
