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Model: litert-community/FunctionGemma_270M_Mobile_Actions Source: Original Platform
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---
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base_model: google/functiongemma-270m-it
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tags:
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- function-calling
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- mobile-actions
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- gemma
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library_name: transformers
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datasets:
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- google/mobile-actions
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language:
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- en
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license: gemma
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---
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# FunctionGemma 270M for Mobile Actions
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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.
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## Model Description
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**Base Model**: `google/functiongemma-270m-it` - A 270M parameter instruction-tuned model from Google's FunctionGemma family, designed for function calling tasks.
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**Specialization**: Mobile assistant actions including:
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- Calendar event management
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- Email composition and sending
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- Contact creation
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- Flashlight control
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- Wi-Fi settings navigation
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- Map location display
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**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.
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## Supported Functions
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The model is optimized to call these mobile action functions:
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1. **`turn_on_flashlight()`** - Turns the device flashlight on
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2. **`turn_off_flashlight()`** - Turns the device flashlight off
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3. **`create_contact(first_name, last_name, phone_number?, email?)`** - Creates a new contact
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4. **`send_email(to, subject, body?)`** - Sends an email to a recipient
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5. **`show_map(query)`** - Displays a location on the map by name, business, or address
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6. **`open_wifi_settings()`** - Opens the Wi-Fi settings screen
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7. **`create_calendar_event(title, datetime)`** - Creates a calendar event (datetime in ISO format: `YYYY-MM-DDTHH:MM:SS`)
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## Training Details
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### Training Data
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- **Dataset**: [google/mobile-actions](https://huggingface.co/datasets/google/mobile-actions)
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- **Format**: JSONL with prompt-completion pairs
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- **Splits**:
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- Training set: examples with `"metadata": "train"`
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- Evaluation set: examples with `"metadata": "eval"`
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- **Preprocessing**: Converted to TRL prompt-completion format with `completion_only_loss=True`
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### Training Procedure
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Fine-tuned using Hugging Face [TRL (Transformer Reinforcement Learning)](https://huggingface.co/docs/trl) with the `SFTTrainer`.
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**Training Configuration**:
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- **Epochs**: 4
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- **Batch size**: 8 per device
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- **Gradient accumulation steps**: 4
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- **Learning rate**: 5e-5
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- **Scheduler**: Cosine
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- **Max sequence length**: 997 tokens (based on longest example: 897 tokens)
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- **Optimizer**: AdamW (fused)
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- **Precision**: bfloat16
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- **Gradient checkpointing**: Enabled
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- **Completion only loss**: True (trains only on model outputs, not prompts)
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**Training Infrastructure**:
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- **Hardware**: Google Colab A100 GPU
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- **Training time**: ~20 minutes for 2 epochs
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- **Library versions**: transformers==4.57.1, trl==0.25.1, datasets==4.4.1
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### Training Results
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Final metrics after 2 epochs:
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| Step | Training Loss | Validation Loss | Mean Token Accuracy |
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|------|---------------|-----------------|---------------------|
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| 500 | 0.008800 | 0.013452 | 0.996691 |
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The model achieved 99.67% token-level accuracy on the validation set, showing significant improvement over the base model's mobile action capabilities.
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## Intended Use
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This model is designed for:
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- **Mobile AI assistants** that need to execute device actions based on user requests
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- **Voice-controlled mobile applications**
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- **Conversational agents** that interact with mobile device features
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- **On-device AI** applications (can be converted to `.litertlm` format for deployment)
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## How to Use
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### Basic Inference
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
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import json
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# Load model and tokenizer
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model_id = "jprtr/google_mobile_actions"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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device_map="auto",
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attn_implementation="eager",
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torch_dtype="auto",
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)
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# Create pipeline
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pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
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# Define the tools (function schemas)
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tools = [
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{
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"function": {
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"name": "create_calendar_event",
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"description": "Creates a new calendar event.",
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"parameters": {
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"type": "OBJECT",
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"properties": {
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"title": {"type": "STRING", "description": "The title of the event."},
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"datetime": {"type": "STRING", "description": "The date and time in YYYY-MM-DDTHH:MM:SS format."},
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},
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"required": ["title", "datetime"],
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},
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}
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},
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{
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"function": {
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"name": "send_email",
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"description": "Sends an email.",
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"parameters": {
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"type": "OBJECT",
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"properties": {
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"to": {"type": "STRING", "description": "The recipient email address."},
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"subject": {"type": "STRING", "description": "The email subject."},
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"body": {"type": "STRING", "description": "The email body."},
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},
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"required": ["to", "subject"],
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},
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}
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},
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# ... add other function definitions
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]
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# Create messages
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messages = [
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{
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"role": "developer",
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"content": (
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"Current date and time given in YYYY-MM-DDTHH:MM:SS format: 2025-07-10T19:06:29\n"
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"Day of week is Thursday\n"
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"You are a model that can do function calling with the following functions\n"
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),
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},
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{
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"role": "user",
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"content": 'Schedule a "team meeting" tomorrow at 4pm.',
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},
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]
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# Apply chat template
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prompt = tokenizer.apply_chat_template(
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messages,
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tools=tools,
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tokenize=False,
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add_generation_prompt=True,
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)
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# Generate
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output = pipe(prompt, max_new_tokens=200)[0]["generated_text"][len(prompt):].strip()
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print("Model output:", output)
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# Example output: call:create_calendar_event{datetime:2025-07-11T16:00:00,title:team meeting}
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```
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### Parsing Function Calls
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The model outputs function calls in a simple format:
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```
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call:<function_name>{arg1:value1,arg2:value2,...}
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```
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For multiple function calls, they appear sequentially:
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```
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call:create_calendar_event{datetime:2025-07-15T10:30:00,title:Dental Checkup}
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call:send_email{to:user@example.com,subject:Appointment,body:See you there!}
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```
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You can parse these by:
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1. Splitting on `call:` to identify individual function calls
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2. Extracting the function name (text before `{`)
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3. Parsing the arguments block (content within `{}`)
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## Evaluation
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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.
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**Key Observations**:
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- The base FunctionGemma 270M model often fails to call appropriate functions for mobile actions
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- After fine-tuning, the model reliably generates correct function calls with proper argument formatting
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- Token-level accuracy on the validation set: **99.67%**
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## Limitations
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- The model is specialized for the 7 mobile action functions listed above and may not generalize well to other function calling tasks
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- Date/time parsing relies on context provided in the developer message (current date/time must be specified)
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- The model outputs may occasionally include variations in argument formatting that are semantically correct but don't exactly match the expected format
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- This is a 270M parameter model, so while efficient for mobile deployment, it may have lower accuracy than larger models
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## On-Device Deployment
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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.
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The converted model can be deployed on:
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- Android devices via [Google AI Edge](https://ai.google.dev/edge)
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- [AI Edge Gallery app](https://play.google.com/store/apps/details?id=com.google.ai.edge.gallery)
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## Training Notebook
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For full training details, hyperparameter tuning, and evaluation, see the original Colab notebook:
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[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)
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## Citation
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If you use this model, please cite the original FunctionGemma paper and the Google Mobile Actions dataset:
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```bibtex
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@misc{functiongemma2024,
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title={FunctionGemma: Function Calling for Gemma Models},
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author={Google},
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year={2024},
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url={https://huggingface.co/google/functiongemma-270m-it}
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}
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```
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## License
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||||
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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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{
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"<end_of_image>": 262145,
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"<image_soft_token>": 262144
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}
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{%- macro format_parameters(properties, required) -%}
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{%- set standard_keys = ['description', 'type', 'properties', 'required', 'nullable'] -%}
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{%- set ns = namespace(found_first=false) -%}
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{%- for key, value in properties | dictsort -%}
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{%- if key not in standard_keys -%}
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{%- if ns.found_first %},{% endif -%}
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{%- set ns.found_first = true -%}
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{{- key }}:{description:<escape>{{ value['description'] }}<escape>
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{%- if value['type'] | upper == 'STRING' -%}
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{%- if value['enum'] -%}
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,enum:{{ format_argument(value['enum']) }}
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{%- endif -%}
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{%- elif value['type'] | upper == 'OBJECT' -%}
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,properties:{
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{%- if value['properties'] is defined and value['properties'] is mapping -%}
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{{- format_parameters(value['properties'], value['required'] | default([])) -}}
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{%- elif value is mapping -%}
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{{- format_parameters(value, value['required'] | default([])) -}}
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{%- endif -%}
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}
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{%- if value['required'] -%}
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,required:[
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{%- for item in value['required'] | default([]) -%}
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<escape>{{- item -}}<escape>
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{%- if not loop.last %},{% endif -%}
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{%- endfor -%}
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]
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{%- endif -%}
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{%- elif value['type'] | upper == 'ARRAY' -%}
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{%- if value['items'] is mapping and value['items'] -%}
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,items:{
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{%- set ns_items = namespace(found_first=false) -%}
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{%- for item_key, item_value in value['items'] | dictsort -%}
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{%- if item_value is not none -%}
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{%- if ns_items.found_first %},{% endif -%}
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{%- set ns_items.found_first = true -%}
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{%- if item_key == 'properties' -%}
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properties:{
|
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{%- if item_value is mapping -%}
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{{- format_parameters(item_value, value['items']['required'] | default([])) -}}
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{%- endif -%}
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}
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{%- elif item_key == 'required' -%}
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required:[
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{%- for req_item in item_value -%}
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<escape>{{- req_item -}}<escape>
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{%- if not loop.last %},{% endif -%}
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{%- endfor -%}
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]
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{%- elif item_key == 'type' -%}
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{%- if item_value is string -%}
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type:{{ format_argument(item_value | upper) }}
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{%- else -%}
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type:{{ format_argument(item_value | map('upper') | list) }}
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{%- endif -%}
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{%- else -%}
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{{ item_key }}:{{ format_argument(item_value) }}
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{%- endif -%}
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{%- endif -%}
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{%- endfor -%}
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}
|
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{%- endif -%}
|
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{%- endif -%}
|
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,type:<escape>{{ value['type'] | upper }}<escape>}
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{%- endif -%}
|
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{%- endfor -%}
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{%- endmacro -%}
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{% macro format_function_declaration(tool_data) -%}
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declaration:{{- tool_data['function']['name'] -}}
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{description:<escape>{{- tool_data['function']['description'] -}}<escape>
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{%- set params = tool_data['function']['parameters'] -%}
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{%- if params -%}
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,parameters:{
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{%- if params['properties'] -%}
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properties:{ {{- format_parameters(params['properties'], params['required']) -}} },
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{%- endif -%}
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{%- if params['required'] -%}
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required:[
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{%- for item in params['required'] -%}
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<escape>{{- item -}}<escape>
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{{- ',' if not loop.last -}}
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{%- endfor -%}
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],
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{%- endif -%}
|
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{%- if params['type'] -%}
|
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type:<escape>{{- params['type'] | upper -}}<escape>}
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{%- endif -%}
|
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{%- endif -%}
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}
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{%- endmacro -%}
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{% macro format_argument(argument, escape_keys=True) -%}
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{%- if argument is string -%}
|
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{{- '<escape>' + argument + '<escape>' -}}
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{%- elif argument is boolean -%}
|
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{%- if argument -%}
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{{- 'true' -}}
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{%- else -%}
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{{- 'false' -}}
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{%- endif -%}
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{%- elif argument is mapping -%}
|
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{{- '{' -}}
|
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{%- set ns = namespace(found_first=false) -%}
|
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{%- for key, value in argument | dictsort -%}
|
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{%- if ns.found_first %},{% endif -%}
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{%- set ns.found_first = true -%}
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||||
{%- if escape_keys -%}
|
||||
{{- '<escape>' + key + '<escape>' -}}
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||||
{%- else -%}
|
||||
{{- key -}}
|
||||
{%- endif -%}
|
||||
:{{- format_argument(value, escape_keys=escape_keys) -}}
|
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{%- 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 -%}
|
||||
54
config.json
Normal file
54
config.json
Normal file
@@ -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
|
||||
}
|
||||
1
configuration.json
Normal file
1
configuration.json
Normal file
@@ -0,0 +1 @@
|
||||
{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
|
||||
15
generation_config.json
Normal file
15
generation_config.json
Normal file
@@ -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"
|
||||
}
|
||||
3
model.safetensors
Normal file
3
model.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:0ce45dc209d23833095d7eeea6797643bc0bf8851d76b9c2e009a0cf2246229e
|
||||
size 1072419256
|
||||
34
special_tokens_map.json
Normal file
34
special_tokens_map.json
Normal file
@@ -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
|
||||
}
|
||||
}
|
||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:b6b09a0b4a803ad453063ca4bb49a784540e8120004e2450e025df2b27d41fb2
|
||||
size 33384899
|
||||
3
tokenizer.model
Normal file
3
tokenizer.model
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:aa009fcbc3589a9904d30d04834094fea4653c2ac6d2de2cd1262d4f7a50ceb3
|
||||
size 4689144
|
||||
3
tokenizer_config.json
Normal file
3
tokenizer_config.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:c33878647bdc69ce85d4b9aeef953815afd8405f8d85001ab0bbb89aa14d2554
|
||||
size 1155714
|
||||
Reference in New Issue
Block a user