Tasky

About the model

This model is a fine-tuned function-calling assistant for a todo/task application. It maps user requests to one of four tools and produces valid tool arguments according to the schema in AI-TRAINING-TOOLS.md.

  • Base model: google/functiongemma-270m-it
  • Primary languages: English and Italian (with light spelling errors/typos to mimic real users)
  • Task: Structured tool selection + argument generation

Intended Use

Use this model to translate natural language task requests into tool calls for:

  • create_tasks
  • search_tasks
  • update_tasks
  • delete_tasks

It is designed for task/todo management workflows and should be paired with strict validation of tool arguments before execution.

Example

Input (user):

Aggiungi un task per pagare la bolletta della luce domani mattina

Expected output (model):

{
  "tool_name": "create_tasks",
  "tool_arguments": "{\"tasks\":[{\"content\":\"pagare la bolletta della luce\",\"dueDate\":\"2026-01-13T09:00:00.000Z\"}]}"
}

## Training Data

Synthetic, bilingual tool-calling data built from the tool schema, including:

- Multiple phrasings and paraphrases
- Mixed English/Italian prompts
- Light typos and user mistakes in user_content
- Broad coverage of optional parameters

Splits:

- Train: 1,500 examples
- Eval: 500 examples

## Training Procedure

- Fine-tuning on synthetic tool-calling samples
- Deduplicated examples
- Balanced coverage of all tools and key parameters

## Evaluation

Reported success rate: 99.5% on the 500‑example eval split vs 0% base model.
Success was measured as exact match on the predicted tool name and the JSON arguments after normalization.

## Limitations

- Trained for a specific tool schema; not a general-purpose assistant.
- Outputs may include incorrect or incomplete tool arguments; validate before execution.
- Language coverage is strongest in English and Italian.
- Synthetic data may not capture all real-world user phrasing or ambiguity.
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