README.md

---
base_model: google/functiongemma-270m-it
library_name: transformers.js
model_name: functiongemma-tasky-onnx
tags:
- onnx
- transformers.js
- text-generation
- function-calling
- task-management
---

# FunctionGemma Tasky (ONNX)

This repository contains an ONNX export of `functiongemma-tasky`, a fine-tuned variant of `google/functiongemma-270m-it` trained for task/todo function-calling. It targets Transformers.js and includes both full precision and Q4 quantized weights.

## Files

- `onnx/model.onnx`: full-precision weights (fp32)
- `onnx/model_q4.onnx`: 4-bit quantized weights (q4)

## Usage (Transformers.js)

```javascript
import { pipeline } from '@huggingface/transformers';

// Q4 (smaller, faster)
const pipe = await pipeline('text-generation', 'REPLACE_WITH_HF_REPO', {
  dtype: 'q4',
});

const out = await pipe('Add a task to call Alice tomorrow at 9am', {
  max_new_tokens: 128,
});
console.log(out[0].generated_text);
```

To load full precision instead:

```javascript
const pipe = await pipeline('text-generation', 'REPLACE_WITH_HF_REPO', {
  dtype: 'fp32',
});
```

Transformers.js expects ONNX weights under an `onnx/` subfolder, which this repo provides.

## Training summary

- Base model: `google/functiongemma-270m-it`
- Fine-tuning data: synthetic task/todo function-calling prompts, mixed English/Italian, includes user-style typos
- Eval success rate: ~99.5% on a 1500/500 train/eval split

## Notes

- Quantized models trade some accuracy for faster inference and smaller size.
- Outputs may not be strict JSON; validate and post-process if needed.