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
- Leverages Parameter-Efficient Fine-Tuning (PEFT) techniques, such as Low-Rank Adaptation (LoRA), to address significant computational and memory costs associated with full fine-tuning
- LoRA freezes pre-trained model weights and injects trainable rank decomposition matrices into each layer of the Transformer architecture, significantly reducing the number of trainable parameters
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: [[concepts/custom-dataset|