Unsloth Datasets

name: unsloth-datasets description: Standardizing and formatting datasets for Unsloth, including chat template conversion and synthetic data generation (triggers: chat templates, ShareGPT, Alpaca, conversation_extension, add_new_tokens, standardize_sharegpt, formatting_prompts_func).

Overview

Unsloth-datasets provides tools to prepare and optimize data for fine-tuning. Key features include standardizing external datasets (like ShareGPT/Alpaca) into a unified format, synthetically extending single-turn data into multi-turn conversations, and handling custom special tokens.

When to Use

• When converting raw datasets from diverse sources (Hugging Face, ShareGPT) into Unsloth-compatible formats.
• When you only have single-turn data but want the model to learn multi-turn conversation logic.
• When adding new domain-specific tokens (e.g., <THINKING>) to a model.

Decision Tree

1. Is your dataset in ShareGPT format?
   • Yes: Use standardize_sharegpt().
2. Do you have only single-turn data but want multi-turn performance?
   • Yes: Use the conversation_extension parameter.
3. Are you adding new tokens?
   • Yes: Call add_new_tokens() BEFORE calling get_peft_model().

Workflows

1. Standardizing External Datasets: Import standardize_sharegpt, apply it to the dataset to map roles (e.g., 'human/gpt'), and apply the chat template using formatting_prompts_func.
2. Adding Custom Domain Tokens: Load model/tokenizer, use add_new_tokens to update matrices, and THEN initialize PEFT adapters to ensure new weights are covered.
3. Custom Chat Template Design: Define a Jinja2 template and explicit EOS token, then pass them as a tuple to get_chat_template.

Non-Obvious Insights

• Applying the wrong chat template (e.g., Llama-3 template on a Mistral model) is a leading cause of poor fine-tuning performance.
• The conversation_extension tool creates synthetic multi-turn interactions by randomly merging single-turn rows, improving the model's contextual memory.
• The order of operations is critical: Adding tokens after initializing LoRA will result in new tokens not being trained by the adapters.

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
• "Users must call add_new_tokens BEFORE get_peft_model to properly resize embedding matrices and LoRA adapters." Source

Scripts

• scripts/unsloth-datasets_tool.py: Python tool for standardization and token addition.
• scripts/unsloth-datasets_tool.js: JS template for ShareGPT data mapping.

Dependencies

• unsloth
• datasets
• jinja2

References

• references/README.md