Supervised Fine-Tuning
A technique for adapting pre-trained language models to specific tasks or domains by updating model weights using labeled input-output pairs. Involves training on a curated dataset to align model behavior with desired outputs while preserving base capabilities.
Key Implementation Details
- Uses hugging-face’s TRL library for efficient supervised fine-tuning (SFT) pipelines
- Requires labeled dataset matching target task (e.g., persona embodiment, domain-specific language)
- Typically involves incremental weight updates rather than full retraining
Example: Fine-Tuning OSS-20B
- Demonstrated in fahd-mirza’s tutorial for training OSS-20B to embody a specific persona using a small custom dataset
- System: Ubuntu 22.04 LTS
- Process: Custom dataset → hugging-face SFT pipeline → Persona-aligned weights
2026 04 14 Fahd Mirza fine tuning weights of OSS 20B
Source Notes
- 2026-04-23: https://www.youtube.com/watch?v=LRvXsQhOlD0 - This video provides a comprehensive, step-by-step tutorial on how to fine-tune OpenAI’s GPT-OSS-20B open-weight model using a custom dataset. The primary goal is to train the model to understand and embody a specific persona (the c (Fahd Mirza fine tuning weights of OSS 20B)
- 2026-04-14: # Fahd Mirza - fine tuning weights of OSS-20B --- --- https://www.youtube.com/watch?v=LRvXsQhOlD0 - This video provides a comprehensive, step-by-step tutorial on how to fine-tune OpenAI’s [[entities/gpt-oss-20b| (Fahd Mirza - fine tuning weights of OSS-20B)