unsloth-sft

name	unsloth-sft
description	Supervised fine-tuning using SFTTrainer, instruction formatting, and multi-turn dataset preparation with triggers like sft, instruction tuning, chat templates, sharegpt, alpaca, conversation_extension, and SFTTrainer.

Overview

Supervised Fine-Tuning (SFT) in Unsloth focuses on training models to follow instructions using specific formats. It provides tools for chat template mapping, multi-turn conversation synthesis via conversation_extension, and optimized dataset processing.

When to Use

• When training models on instruction-response datasets (e.g., Alpaca).
• When developing multi-turn conversational agents.
• When you need to standardize various dataset formats (ShareGPT, OpenAI) for training.

Decision Tree

1. Is your dataset single-turn?
   • Yes: Use conversation_extension to synthetically create multi-turn samples.
   • No: Map columns using standardize_sharegpt.
2. Are you training on Windows?
   • Yes: Set dataset_num_proc = 1 in SFTConfig.
   • No: Use multiple processes for faster mapping.
3. Want to increase multi-turn accuracy?
   • Yes: Enable masking of inputs to train on completions only.

Workflows

Chat Template Implementation

1. Select a template (e.g., 'chatml', 'llama-3.1') using get_chat_template(tokenizer, chat_template='...').
2. Map dataset columns using the mapping parameter (e.g., mapping = {'role' : 'from', 'content' : 'value'}).
3. Apply the formatting function to the dataset using dataset.map with batched=True.

Multi-turn Data Preparation

1. Load a standard single-turn dataset like Alpaca.
2. Use standardize_sharegpt(dataset) to unify the role and content keys.
3. Apply conversation_extension=N to randomly concatenate N rows into single interactive samples.

Non-Obvious Insights

• Training on completions only (masking out inputs) significantly increases accuracy, particularly for multi-turn conversations where input context is repetitive.
• Standardizing datasets to ShareGPT format before mapping is the most robust way to ensure compatibility with Unsloth's internal formatting kernels.
• On Windows, dataset_num_proc must be 1; otherwise, the multi-processing overhead or library incompatibilities will cause trainer crashes.

Evidence

• "We introduced the conversation_extension parameter, which essentially selects some random rows in your single turn dataset, and merges them into 1 conversation!" Source
• "Training on completions only (masking out inputs) increases accuracy by quite a bit, especially for multi-turn conversational finetunes!" Source

Scripts

• scripts/unsloth-sft_tool.py: Python tool for formatting datasets into ShareGPT/ChatML format.
• scripts/unsloth-sft_tool.js: JavaScript logic for mapping Alpaca-style datasets to conversation formats.

Dependencies

• unsloth
• trl
• datasets

References

• [[references/README.md]]