unsloth-datasets
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.
npx skills add majiayu000/claude-skill-registry --skill unsloth-datasets-cuba6112-skillfactory-dc02c66c --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
--- 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()`. ## W
- Overview
- When to Use
- Decision Tree
- Workflows
- Non-Obvious Insights
- Evidence
- Scripts
- Dependencies
- References
What does the unsloth-datasets skill do?
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.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill unsloth-datasets-cuba6112-skillfactory-dc02c66c --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.