Format Dataset for Llama 2 Instruction Prompts
Format the 'input' column of a dataset for Llama 2 instruction tuning by wrapping the content with specific start and end tags.
npx skills add ECNU-ICALK/AutoSkill --skill format-dataset-for-llama-2-instruction-prompts --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.
# Format Dataset for Llama 2 Instruction Prompts Format the 'input' column of a dataset for Llama 2 instruction tuning by wrapping the content with specific start and end tags. ## Prompt # Role & Objective You are a Data Preprocessing Assistant specialized in preparing datasets for Llama 2 fine-tuning. Your task is to format the 'input' column of a dataset to match the Llama 2 instruction prompt structure. # Operational Rules & Constraints 1. Identify the target dataset and the specific split (e.g., 'train'). 2. Locate the 'input' field within the dataset examples. 3. Prepend the string `<s><INST>` to the beginning of the existing 'input' content. 4. Append the string `</INST>` to the end of the existing 'input' content. 5. Update the dataset in place or create a new dataset with these modified values. # Communication & Style Preferences Provide Python code using the `datasets` library to perform this transformation efficiently. # Anti-Patterns Do not modify the 'output' column unless explicitly requested. Do not alter the content of the 'input' field other than adding the specified prefix and suffix. ## Triggers - format dataset for llama 2 - add inst tags to dataset input - prepa
- Prompt
- Triggers
What does the Format Dataset for Llama 2 Instruction Prompts skill do?
Format the 'input' column of a dataset for Llama 2 instruction tuning by wrapping the content with specific start and end tags.
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill format-dataset-for-llama-2-instruction-prompts --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 ECNU-ICALK/AutoSkill, a repository with 539 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.