Duplicate from litert-community/FunctionGemma_270M_Mobile_Actions

.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
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+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
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+*.pt filter=lfs diff=lfs merge=lfs -text
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+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
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+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

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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.

added_tokens.json

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

chat_template.jinja

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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 -%}

config.json

@@ -0,0 +1,54 @@
+{
+  "_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,
+  "rms_norm_eps": 1e-06,
+  "rope_local_base_freq": 10000.0,
+  "rope_scaling": null,
+  "rope_theta": 1000000.0,
+  "sliding_window": 512,
+  "transformers_version": "4.57.1",
+  "use_bidirectional_attention": false,
+  "use_cache": true,
+  "vocab_size": 262144
+}

generation_config.json

@@ -0,0 +1,15 @@
+{
+  "bos_token_id": 2,
+  "cache_implementation": "hybrid",
+  "do_sample": true,
+  "eos_token_id": [
+    1,
+    1,
+    50,
+    106
+  ],
+  "pad_token_id": 0,
+  "top_k": 64,
+  "top_p": 0.95,
+  "transformers_version": "4.57.1"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0ce45dc209d23833095d7eeea6797643bc0bf8851d76b9c2e009a0cf2246229e
+size 1072419256

special_tokens_map.json

@@ -0,0 +1,34 @@
+{
+  "boi_token": "<start_of_image>",
+  "bos_token": {
+    "content": "<bos>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eoi_token": "<end_of_image>",
+  "eos_token": {
+    "content": "<eos>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "image_token": "<image_soft_token>",
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sfr_token": "<start_function_response>",
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b6b09a0b4a803ad453063ca4bb49a784540e8120004e2450e025df2b27d41fb2
+size 33384899

tokenizer.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:aa009fcbc3589a9904d30d04834094fea4653c2ac6d2de2cd1262d4f7a50ceb3
+size 4689144