| --- |
| license: gemma |
| library_name: onnx |
| pipeline_tag: text-generation |
| tags: |
| - gemma |
| - expense-tracker |
| - onnx |
| - android |
| language: |
| - en |
| --- |
| |
| # Expense Tracker — Gemma 3 270M ONNX model |
|
|
| Fine-tuned Gemma 3 270M model for extracting expense details from spoken |
| English sentences into structured JSON. Runs fully on-device in the |
| [Expense Tracker](https://github.com/ssgalib/expense-tracker) Android app via |
| ONNX Runtime. |
|
|
| ## Files |
|
|
| | File | Description | |
| |---|---| |
| | `model.onnx` | FP16 ONNX model (537 MB). Tied embedding/`lm_head` share one tensor via a Transpose node to halve size. | |
| | `tokenizer.json` | Full BPE tokenizer (from `tokenizers` library). | |
| | `tokenizer_config.json` | Tokenizer config. | |
| | `special_tokens_map.json` | Special tokens. | |
| | `adapter_config.json` / `adapter_model.safetensors` | Original LoRA adapter (base: `google/gemma-3-270m-it`, r=8, α=16, targets `q_proj`/`v_proj`). | |
|
|
| ## Prompt format |
|
|
| The model was fine-tuned with the standard Gemma chat template: |
|
|
| ``` |
| <bos><start_of_turn>user |
| Extract expense details from the sentence and return JSON only. No explanation. |
| |
| <INPUT_SENTENCE><end_of_turn> |
| <start_of_turn>model |
| ``` |
|
|
| Output (single-bos prompting, greedy decoding, stop at `<end_of_turn>`): |
|
|
| ```json |
| {"item": "eggs", "quantity": "3", "amount": 50, "category": "food"} |
| ``` |
|
|
| ## Categories |
|
|
| `food` · `transport` · `utilities` · `rent` · `medicine` · `education` · `entertainment` · `mobile` |
|
|
| ## Notes on quantization |
|
|
| Dynamic INT8 and weight-only INT4 quantization were both attempted but produce |
| garbage output for this small soft-capped model (immediate `<eos>`/garbage). |
| FP16 is the smallest reliable representation tested. |
|
|
| ## App tokenizer |
|
|
| The Android app does not parse `tokenizer.json` directly. It uses compact |
| binary derivatives (`vocab.bin`, `merges.bin`) generated by |
| `scripts/preprocess_tokenizer.py` from this `tokenizer.json`. See the |
| [app repo](https://github.com/ssgalib/expense-tracker) for details. |
|
|
