---
license: gemma
base_model: google/gemma-4-E2B-it
base_model_relation: finetune
library_name: transformers
pipeline_tag: text-generation
datasets:
- google/mobile-actions
tags:
- gemma4
- mobile-actions
- function-calling
- tool-use
- transformers
- trl
- lora
---

# Gemma 4 E2B Mobile Actions, 200-Sample Fine-Tune

Main model card: [`google/gemma-4-E2B-it`](https://huggingface.co/google/gemma-4-E2B-it)

This repository provides an experimental Gemma 4 E2B checkpoint fine-tuned on
[`google/mobile-actions`](https://huggingface.co/datasets/google/mobile-actions)
for mobile action function calling.

The model was trained locally on an Apple M1 Pro with LoRA, then merged back
into the base model for direct Transformers inference.

## What It Does

The model converts natural language mobile assistant requests into tool calls,
for example:

```text
<|tool_call>call:show_map{query:<|"|>Patisserie Valerie at 208 Kensington High Street, London, W8 7RG<|"|>}<tool|>
```

Supported functions come from the `google/mobile-actions` dataset, including:

- `create_calendar_event`
- `create_contact`
- `show_map`
- `open_wifi_settings`
- `send_email`
- `turn_on_flashlight`
- `turn_off_flashlight`

## Performance

Evaluation was run on 200 held-out examples from `google/mobile-actions`
(`metadata == "eval"`).

| Metric | Score |
| --- | ---: |
| Format valid rate | 94.0% |
| Function name accuracy | 94.0% |
| Required arguments present | 92.0% |
| Exact match | 85.0% |

### Per-Function Results

| Function | N | Format | Name | Required | Exact |
| --- | ---: | ---: | ---: | ---: | ---: |
| `create_contact` | 43 | 88.4% | 88.4% | 88.4% | 86.0% |
| `create_calendar_event` | 42 | 90.5% | 90.5% | 81.0% | 78.6% |
| `show_map` | 38 | 97.4% | 97.4% | 97.4% | 68.4% |
| `open_wifi_settings` | 20 | 90.0% | 90.0% | 90.0% | 90.0% |
| `send_email` | 20 | 100.0% | 100.0% | 100.0% | 95.0% |
| `turn_on_flashlight` | 20 | 100.0% | 100.0% | 100.0% | 100.0% |
| `turn_off_flashlight` | 17 | 100.0% | 100.0% | 100.0% | 100.0% |

## Training

| Setting | Value |
| --- | --- |
| Base model | `google/gemma-4-E2B-it` |
| Dataset | `google/mobile-actions` |
| Training samples | 200 |
| Epochs | 1 |
| LoRA rank / alpha | 16 / 32 |
| Batch / accumulation | 1 / 16 |
| Max sequence length | 1024 |
| Precision | bfloat16 |
| Hardware | Apple M1 Pro, MPS |
| Training time | 35m47s including merge |

The adapter was trained with TRL `SFTTrainer` and PEFT LoRA. The final weights
in this repository are the merged model weights.

## Usage

```python
import torch
from transformers import AutoModelForCausalLM, AutoProcessor, pipeline

model_id = "YOUR_USERNAME/gemma4-e2b-mobile-actions-200"

processor = AutoProcessor.from_pretrained(model_id)
tokenizer = processor.tokenizer
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    dtype=torch.bfloat16,
    device_map="auto",
)

tools = [{
    "type": "function",
    "function": {
        "name": "show_map",
        "description": "Shows a location on the map.",
        "parameters": {
            "type": "object",
            "properties": {"query": {"type": "string"}},
            "required": ["query"],
        },
    },
}]

messages = [
    {"role": "system", "content": "You are a mobile assistant that calls tools."},
    {"role": "user", "content": "Show me Patisserie Valerie on Kensington High Street."},
]

prompt = processor.apply_chat_template(
    messages,
    tools=tools,
    tokenize=False,
    add_generation_prompt=True,
)

generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
print(generator(prompt, max_new_tokens=160, do_sample=False)[0]["generated_text"])
```

## Examples

This repository includes runnable examples:

```bash
python examples/run_transformers.py \
  --model-id ClarkBear/gemma4-e2b-mobile-actions-200 \
  --prompt "Turn on the flashlight"
```

Example prompts are available in `examples/prompts.jsonl`.

## Limitations

This is an experimental small-data fine-tune.

- It was trained on only 200 examples.
- It may emit extra tool calls after the first valid tool call.
- `show_map` exact match is sensitive to address string formatting.
- Calendar events remain harder than simple device actions because they require
  title and datetime extraction.
- This checkpoint is not compiled to LiteRT-LM or `.litertlm`.

## Fine-Tune and Compile Notes

This repository contains a merged Hugging Face Transformers checkpoint. To
deploy on-device with LiteRT-LM, an additional conversion and packaging step is
required.

The local training scripts and evaluation code are maintained in the companion
project used to create this checkpoint.