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---
base_model: google/functiongemma-270m-it
tags:
- function-calling
- mobile-actions
- gemma
library_name: transformers
datasets:
- google/mobile-actions
language:
- en
license: gemma
---
# FunctionGemma 270M for Mobile Actions
This model is a fine-tuned version of [google/functiongemma-270m-it](https://huggingface.co/google/functiongemma-270m-it) specialized for mobile assistant actions. It has been trained on the [google/mobile-actions](https://huggingface.co/datasets/google/mobile-actions) dataset to perform structured function calling for common mobile device tasks.
## Model Description
**Base Model**: `google/functiongemma-270m-it` - A 270M parameter instruction-tuned model from Google's FunctionGemma family, designed for function calling tasks.
**Specialization**: Mobile assistant actions including:
- Calendar event management
- Email composition and sending
- Contact creation
- Flashlight control
- Wi-Fi settings navigation
- Map location display
**Training Objective**: The model learns to emit structured function calls in the format `call:<function_name>{arg1:value1,arg2:value2,...}` instead of natural language responses.
## Supported Functions
The model is optimized to call these mobile action functions:
1. **`turn_on_flashlight()`** - Turns the device flashlight on
2. **`turn_off_flashlight()`** - Turns the device flashlight off
3. **`create_contact(first_name, last_name, phone_number?, email?)`** - Creates a new contact
4. **`send_email(to, subject, body?)`** - Sends an email to a recipient
5. **`show_map(query)`** - Displays a location on the map by name, business, or address
6. **`open_wifi_settings()`** - Opens the Wi-Fi settings screen
7. **`create_calendar_event(title, datetime)`** - Creates a calendar event (datetime in ISO format: `YYYY-MM-DDTHH:MM:SS`)
## Training Details
### Training Data
- **Dataset**: [google/mobile-actions](https://huggingface.co/datasets/google/mobile-actions)
- **Format**: JSONL with prompt-completion pairs
- **Splits**:
- Training set: examples with `"metadata": "train"`
- Evaluation set: examples with `"metadata": "eval"`
- **Preprocessing**: Converted to TRL prompt-completion format with `completion_only_loss=True`
### Training Procedure
Fine-tuned using Hugging Face [TRL (Transformer Reinforcement Learning)](https://huggingface.co/docs/trl) with the `SFTTrainer`.
**Training Configuration**:
- **Epochs**: 4
- **Batch size**: 8 per device
- **Gradient accumulation steps**: 4
- **Learning rate**: 5e-5
- **Scheduler**: Cosine
- **Max sequence length**: 997 tokens (based on longest example: 897 tokens)
- **Optimizer**: AdamW (fused)
- **Precision**: bfloat16
- **Gradient checkpointing**: Enabled
- **Completion only loss**: True (trains only on model outputs, not prompts)
**Training Infrastructure**:
- **Hardware**: Google Colab A100 GPU
- **Training time**: ~20 minutes for 2 epochs
- **Library versions**: transformers==4.57.1, trl==0.25.1, datasets==4.4.1
### Training Results
Final metrics after 2 epochs:
| Step | Training Loss | Validation Loss | Mean Token Accuracy |
|------|---------------|-----------------|---------------------|
| 500 | 0.008800 | 0.013452 | 0.996691 |
The model achieved 99.67% token-level accuracy on the validation set, showing significant improvement over the base model's mobile action capabilities.
## Intended Use
This model is designed for:
- **Mobile AI assistants** that need to execute device actions based on user requests
- **Voice-controlled mobile applications**
- **Conversational agents** that interact with mobile device features
- **On-device AI** applications (can be converted to `.litertlm` format for deployment)
## How to Use
### Basic Inference
```python
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
import json
# Load model and tokenizer
model_id = "jprtr/google_mobile_actions"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
device_map="auto",
attn_implementation="eager",
torch_dtype="auto",
)
# Create pipeline
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
# Define the tools (function schemas)
tools = [
{
"function": {
"name": "create_calendar_event",
"description": "Creates a new calendar event.",
"parameters": {
"type": "OBJECT",
"properties": {
"title": {"type": "STRING", "description": "The title of the event."},
"datetime": {"type": "STRING", "description": "The date and time in YYYY-MM-DDTHH:MM:SS format."},
},
"required": ["title", "datetime"],
},
}
},
{
"function": {
"name": "send_email",
"description": "Sends an email.",
"parameters": {
"type": "OBJECT",
"properties": {
"to": {"type": "STRING", "description": "The recipient email address."},
"subject": {"type": "STRING", "description": "The email subject."},
"body": {"type": "STRING", "description": "The email body."},
},
"required": ["to", "subject"],
},
}
},
# ... add other function definitions
]
# Create messages
messages = [
{
"role": "developer",
"content": (
"Current date and time given in YYYY-MM-DDTHH:MM:SS format: 2025-07-10T19:06:29\n"
"Day of week is Thursday\n"
"You are a model that can do function calling with the following functions\n"
),
},
{
"role": "user",
"content": 'Schedule a "team meeting" tomorrow at 4pm.',
},
]
# Apply chat template
prompt = tokenizer.apply_chat_template(
messages,
tools=tools,
tokenize=False,
add_generation_prompt=True,
)
# Generate
output = pipe(prompt, max_new_tokens=200)[0]["generated_text"][len(prompt):].strip()
print("Model output:", output)
# Example output: call:create_calendar_event{datetime:2025-07-11T16:00:00,title:team meeting}
```
### Parsing Function Calls
The model outputs function calls in a simple format:
```
call:<function_name>{arg1:value1,arg2:value2,...}
```
For multiple function calls, they appear sequentially:
```
call:create_calendar_event{datetime:2025-07-15T10:30:00,title:Dental Checkup}
call:send_email{to:user@example.com,subject:Appointment,body:See you there!}
```
You can parse these by:
1. Splitting on `call:` to identify individual function calls
2. Extracting the function name (text before `{`)
3. Parsing the arguments block (content within `{}`)
## Evaluation
The model was evaluated on the held-out test set from the mobile-actions dataset. Evaluation metrics compare exact string matching of the model's function call outputs against ground truth labels.
**Key Observations**:
- The base FunctionGemma 270M model often fails to call appropriate functions for mobile actions
- After fine-tuning, the model reliably generates correct function calls with proper argument formatting
- Token-level accuracy on the validation set: **99.67%**
## Limitations
- The model is specialized for the 7 mobile action functions listed above and may not generalize well to other function calling tasks
- Date/time parsing relies on context provided in the developer message (current date/time must be specified)
- The model outputs may occasionally include variations in argument formatting that are semantically correct but don't exactly match the expected format
- This is a 270M parameter model, so while efficient for mobile deployment, it may have lower accuracy than larger models
## On-Device Deployment
The model can be converted to `.litertlm` format for on-device deployment using `ai-edge-torch`. See the [training notebook](https://colab.research.google.com/github/google-gemini/gemma-cookbook/blob/main/FunctionGemma/%5BFunctionGemma%5DFinetune_FunctionGemma_270M_for_Mobile_Actions_with_Hugging_Face.ipynb) for conversion instructions.
The converted model can be deployed on:
- Android devices via [Google AI Edge](https://ai.google.dev/edge)
- [AI Edge Gallery app](https://play.google.com/store/apps/details?id=com.google.ai.edge.gallery)
## Training Notebook
For full training details, hyperparameter tuning, and evaluation, see the original Colab notebook:
[Finetune FunctionGemma 270M for Mobile Actions](https://colab.research.google.com/github/google-gemini/gemma-cookbook/blob/main/FunctionGemma/%5BFunctionGemma%5DFinetune_FunctionGemma_270M_for_Mobile_Actions_with_Hugging_Face.ipynb)
## Citation
If you use this model, please cite the original FunctionGemma paper and the Google Mobile Actions dataset:
```bibtex
@misc{functiongemma2024,
title={FunctionGemma: Function Calling for Gemma Models},
author={Google},
year={2024},
url={https://huggingface.co/google/functiongemma-270m-it}
}
```
## License
This model is released under the Gemma license. See the [Gemma Terms of Use](https://ai.google.dev/gemma/terms) for details.

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"<end_of_image>": 262145,
"<image_soft_token>": 262144
}

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{%- macro format_parameters(properties, required) -%}
{%- set standard_keys = ['description', 'type', 'properties', 'required', 'nullable'] -%}
{%- set ns = namespace(found_first=false) -%}
{%- for key, value in properties | dictsort -%}
{%- if key not in standard_keys -%}
{%- if ns.found_first %},{% endif -%}
{%- set ns.found_first = true -%}
{{- key }}:{description:<escape>{{ value['description'] }}<escape>
{%- if value['type'] | upper == 'STRING' -%}
{%- if value['enum'] -%}
,enum:{{ format_argument(value['enum']) }}
{%- endif -%}
{%- elif value['type'] | upper == 'OBJECT' -%}
,properties:{
{%- if value['properties'] is defined and value['properties'] is mapping -%}
{{- format_parameters(value['properties'], value['required'] | default([])) -}}
{%- elif value is mapping -%}
{{- format_parameters(value, value['required'] | default([])) -}}
{%- endif -%}
}
{%- if value['required'] -%}
,required:[
{%- for item in value['required'] | default([]) -%}
<escape>{{- item -}}<escape>
{%- if not loop.last %},{% endif -%}
{%- endfor -%}
]
{%- endif -%}
{%- elif value['type'] | upper == 'ARRAY' -%}
{%- if value['items'] is mapping and value['items'] -%}
,items:{
{%- set ns_items = namespace(found_first=false) -%}
{%- for item_key, item_value in value['items'] | dictsort -%}
{%- if item_value is not none -%}
{%- if ns_items.found_first %},{% endif -%}
{%- set ns_items.found_first = true -%}
{%- if item_key == 'properties' -%}
properties:{
{%- if item_value is mapping -%}
{{- format_parameters(item_value, value['items']['required'] | default([])) -}}
{%- endif -%}
}
{%- elif item_key == 'required' -%}
required:[
{%- for req_item in item_value -%}
<escape>{{- req_item -}}<escape>
{%- if not loop.last %},{% endif -%}
{%- endfor -%}
]
{%- elif item_key == 'type' -%}
{%- if item_value is string -%}
type:{{ format_argument(item_value | upper) }}
{%- else -%}
type:{{ format_argument(item_value | map('upper') | list) }}
{%- endif -%}
{%- else -%}
{{ item_key }}:{{ format_argument(item_value) }}
{%- endif -%}
{%- endif -%}
{%- endfor -%}
}
{%- endif -%}
{%- endif -%}
,type:<escape>{{ value['type'] | upper }}<escape>}
{%- endif -%}
{%- endfor -%}
{%- endmacro -%}
{% macro format_function_declaration(tool_data) -%}
declaration:{{- tool_data['function']['name'] -}}
{description:<escape>{{- tool_data['function']['description'] -}}<escape>
{%- set params = tool_data['function']['parameters'] -%}
{%- if params -%}
,parameters:{
{%- if params['properties'] -%}
properties:{ {{- format_parameters(params['properties'], params['required']) -}} },
{%- endif -%}
{%- if params['required'] -%}
required:[
{%- for item in params['required'] -%}
<escape>{{- item -}}<escape>
{{- ',' if not loop.last -}}
{%- endfor -%}
],
{%- endif -%}
{%- if params['type'] -%}
type:<escape>{{- params['type'] | upper -}}<escape>}
{%- endif -%}
{%- endif -%}
}
{%- endmacro -%}
{% macro format_argument(argument, escape_keys=True) -%}
{%- if argument is string -%}
{{- '<escape>' + argument + '<escape>' -}}
{%- elif argument is boolean -%}
{%- if argument -%}
{{- 'true' -}}
{%- else -%}
{{- 'false' -}}
{%- endif -%}
{%- elif argument is mapping -%}
{{- '{' -}}
{%- set ns = namespace(found_first=false) -%}
{%- for key, value in argument | dictsort -%}
{%- if ns.found_first %},{% endif -%}
{%- set ns.found_first = true -%}
{%- if escape_keys -%}
{{- '<escape>' + key + '<escape>' -}}
{%- else -%}
{{- key -}}
{%- endif -%}
:{{- format_argument(value, escape_keys=escape_keys) -}}
{%- endfor -%}
{{- '}' -}}
{%- elif argument is sequence -%}
{{- '[' -}}
{%- for item in argument -%}
{{- format_argument(item, escape_keys=escape_keys) -}}
{%- if not loop.last %},{% endif -%}
{%- endfor -%}
{{- ']' -}}
{%- else -%}
{{- argument -}}
{%- endif -%}
{%- endmacro -%}
{{ bos_token }}
{%- set ns = namespace(prev_message_type=None) -%}
{#- Tool Declarations -#}
{%- set loop_messages = messages -%}
{%- if tools or messages[0]['role'] == 'system' or messages[0]['role'] == 'developer' -%}
{{- '<start_of_turn>developer\n' -}}
{%- if messages[0]['role'] == 'system' or messages[0]['role'] == 'developer' -%}
{%- if messages[0]['content'] is string -%}
{{- messages[0]['content'] | trim -}}
{%- elif messages[0]['content'] is sequence -%}
{%- for item in messages[0]['content'] -%}
{%- if item['type'] == 'text' -%}
{{- item['text'] | trim -}}
{%- endif -%}
{%- endfor -%}
{%- endif -%}
{%- set loop_messages = messages[1:] -%}
{%- endif -%}
{%- if tools -%}
{%- for tool in tools %}
{{- '<start_function_declaration>' -}}
{{- format_function_declaration(tool) | trim }}
{{- '<end_function_declaration>' -}}
{%- endfor %}
{%- endif -%}
{{- '<end_of_turn>\n' }}
{%- endif %}
{#- Loop through messages. -#}
{%- for message in loop_messages -%}
{%- if (message['role'] == 'assistant') -%}
{#- Rename "assistant" to "model". -#}
{%- set role = "model" -%}
{%- else -%}
{%- set role = message['role'] -%}
{%- endif -%}
{%- if role != 'tool' -%}
{%- if ns.prev_message_type != 'tool_response' -%}
{{- '<start_of_turn>' + role + '\n' }}
{%- endif -%}
{%- set ns.prev_message_type = None -%}
{%- if 'content' in message and message['content'] is not none -%}
{%- if message['content'] is string -%}
{{ message['content'] | trim }}
{%- elif message['content'] is sequence -%}
{%- for item in message['content'] -%}
{%- if item['type'] == 'image' -%}
{{ '<start_of_image>' }}
{%- elif item['type'] == 'text' -%}
{{ item['text'] | trim }}
{%- endif -%}
{%- endfor -%}
{%- else -%}
{{ raise_exception("Invalid content type in user/assistant message") }}
{%- endif -%}
{%- set ns.prev_message_type = 'content' -%}
{%- endif -%}
{%- if 'tool_calls' in message and message['tool_calls'] and message['tool_calls'] is iterable -%}
{#- Tool Calls -#}
{%- for tool_call in message['tool_calls'] -%}
{% set function = tool_call['function'] %}
{{- '<start_function_call>call:' + function['name'] + '{' -}}
{%- if 'arguments' in function -%}
{%- if function['arguments'] is mapping -%}
{%- set ns = namespace(found_first=false) -%}
{%- for key, value in function['arguments'] | dictsort -%}
{%- if ns.found_first %},{% endif -%}
{%- set ns.found_first = true -%}
{{- key -}}:{{- format_argument(value, escape_keys=False) -}}
{%- endfor -%}
{%- elif function['arguments'] is string -%}
{# This handles string-JSON, just in case #}
{{ function['arguments'] }}
{%- endif %}
{%- endif -%}
{{- '}<end_function_call>' -}}
{%- endfor -%}
{%- if loop.last -%}
{{ '<start_function_response>' }}
{%- endif -%}
{%- set ns.prev_message_type = 'tool_call' -%}
{%- endif -%}
{%- else -%}
{#- Tool Responses -#}
{%- if 'content' in message and message['content'] -%}
{%- if message['content'] is mapping -%}
{%- if 'name' in message['content'] and 'response' in message['content'] -%}
{{ '<start_function_response>response:' + message['content']['name'] | trim + '{' }}
{%- set response_ns = namespace(found_first=false) -%}
{%- for key, value in message['content']['response'] | dictsort -%}
{%- if response_ns.found_first %},{% endif -%}
{%- set response_ns.found_first = true -%}
{{- key -}}:{{- format_argument(value, escape_keys=False) -}}
{%- endfor -%}
{{- '}<end_function_response>' -}}
{%- elif 'name' in message -%}
{{ '<start_function_response>response:' + message['name'] | trim + '{' }}
{%- set response_ns = namespace(found_first=false) -%}
{%- for key, value in message['content'] | dictsort -%}
{%- if response_ns.found_first %},{% endif -%}
{%- set response_ns.found_first = true -%}
{{- key -}}:{{- format_argument(value, escape_keys=False) -}}
{%- endfor -%}
{{- '}<end_function_response>' -}}
{%- else -%}
{{ raise_exception("Invalid tool response mapping: must contain 'name' and 'response' keys, or 'name' must be in the message.") }}
{%- endif -%}
{%- elif message['content'] is string -%}
{%- if 'name' in message -%}
{{ '<start_function_response>response:' + message['name'] | trim + '{value:' + format_argument(message['content'], escape_keys=False) + '}<end_function_response>' }}
{%- else -%}
{{ raise_exception("Invalid tool response: 'name' must be provided.") }}
{%- endif -%}
{%- elif message['content'] is sequence -%}
{%- for item in message['content'] -%}
{%- if item is mapping -%}
{%- if 'name' in item and 'response' in item -%}
{{ '<start_function_response>response:' + item['name'] | trim + '{' }}
{%- set response_ns = namespace(found_first=false) -%}
{%- for key, value in item['response'] | dictsort -%}
{%- if response_ns.found_first %},{% endif -%}
{%- set response_ns.found_first = true -%}
{{- key -}}:{{- format_argument(value, escape_keys=False) -}}
{%- endfor -%}
{{- '}<end_function_response>' -}}
{%- elif 'name' in message -%}
{{ '<start_function_response>response:' + message['name'] | trim + '{' }}
{%- set response_ns = namespace(found_first=false) -%}
{%- for key, value in item | dictsort -%}
{%- if response_ns.found_first %},{% endif -%}
{%- set response_ns.found_first = true -%}
{{- key -}}:{{- format_argument(value, escape_keys=False) -}}
{%- endfor -%}
{{- '}<end_function_response>' -}}
{%- else -%}
{{ raise_exception("Invalid tool response mapping: must contain 'name' and 'response' keys, or 'name' must be in the message.") }}
{%- endif -%}
{%- else -%}
{{ raise_exception("Invalid tool response message: multiple responses must all be mappings") }}
{%- endif -%}
{%- endfor -%}
{%- else -%}
{{ raise_exception("Invalid content type in tool message: must be mapping, sequence of mappings, or string.") }}
{%- endif -%}
{%- endif -%}
{%- set ns.prev_message_type = 'tool_response' -%}
{%- endif -%}
{%- if ns.prev_message_type not in ['tool_call', 'tool_response'] -%}
{{ '<end_of_turn>\n' }}
{%- endif -%}
{%- endfor -%}
{%- if add_generation_prompt -%}
{%- if ns.prev_message_type != 'tool_response' -%}
{{- '<start_of_turn>model\n' -}}
{%- endif -%}
{%- endif -%}

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{
"_sliding_window_pattern": 6,
"architectures": [
"Gemma3ForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"attn_logit_softcapping": null,
"bos_token_id": 2,
"dtype": "float32",
"eos_token_id": 1,
"final_logit_softcapping": null,
"head_dim": 256,
"hidden_activation": "gelu_pytorch_tanh",
"hidden_size": 640,
"initializer_range": 0.02,
"intermediate_size": 2048,
"layer_types": [
"sliding_attention",
"sliding_attention",
"sliding_attention",
"sliding_attention",
"sliding_attention",
"full_attention",
"sliding_attention",
"sliding_attention",
"sliding_attention",
"sliding_attention",
"sliding_attention",
"full_attention",
"sliding_attention",
"sliding_attention",
"sliding_attention",
"sliding_attention",
"sliding_attention",
"full_attention"
],
"max_position_embeddings": 32768,
"model_type": "gemma3_text",
"num_attention_heads": 4,
"num_hidden_layers": 18,
"num_key_value_heads": 1,
"pad_token_id": 0,
"query_pre_attn_scalar": 256,
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