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Model: nguyenthanhthuan/Llama_3.2_1B_Intruct_Tool_Calling Source: Original Platform
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README.md
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README.md
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---
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base_model:
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- meta-llama/Llama-3.2-1B-Instruct
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language:
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- en
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- vi
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license: apache-2.0
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tags:
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- text-generation-inference
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- transformers
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- unsloth
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- llama
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- trl
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- Ollama
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- Tool-Calling
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datasets:
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- nguyenthanhthuan/function-calling-sharegpt
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---
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# Function Calling Llama Model Version 1
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## Overview
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A specialized fine-tuned version of the **`meta-llama/Llama-3.2-1B-Instruct`** model enhanced with function/tool calling capabilities. The model leverages the **`hiyouga/glaive-function-calling-v2-sharegpt`** dataset for training.
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## Model Specifications
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* **Base Architecture**: meta-llama/Llama-3.2-1B-Instruct
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* **Primary Language**: English (Function/Tool Calling), Vietnamese
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* **Licensing**: Apache 2.0
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* **Primary Developer**: nguyenthanhthuan_banhmi
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* **Key Capabilities**: text-generation-inference, transformers, unsloth, llama, trl, Ollama, Tool-Calling
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## Getting Started
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### Prerequisites
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Method 1:
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1. Install [Ollama](https://ollama.com/)
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2. Install required Python packages:
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```bash
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pip install langchain pydantic torch langchain-ollama
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```
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Method 2:
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1. Click use this model
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2. Click Ollama
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### Installation Steps
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1. Clone the repository
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2. Navigate to the project directory
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3. Create the model in Ollama:
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```bash
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ollama create <model_name> -f <path_to_modelfile>
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```
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## Implementation Guide
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### Model Initialization
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```python
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from langchain_ollama import ChatOllama
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# Initialize model instance
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llm = ChatOllama(model="<model_name>")
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```
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### Basic Usage Example
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```python
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# Arithmetic computation example
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query = "What is 3 * 12? Also, what is 11 + 49?"
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response = llm.invoke(query)
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print(response.content)
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# Output:
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# 1. 3 times 12 is 36.
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# 2. 11 plus 49 is 60.
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```
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### Advanced Function Calling (English Recommended)
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#### Basic Arithmetic Tools
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```python
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from pydantic import BaseModel, Field
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class add(BaseModel):
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"""Addition operation for two integers."""
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a: int = Field(..., description="First integer")
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b: int = Field(..., description="Second integer")
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class multiply(BaseModel):
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"""Multiplication operation for two integers."""
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a: int = Field(..., description="First integer")
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b: int = Field(..., description="Second integer")
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# Tool registration
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tools = [add, multiply]
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llm_tools = llm.bind_tools(tools)
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# Execute query
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response = llm_tools.invoke(query)
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print(response.content)
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# Output:
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# {"type":"function","function":{"name":"multiply","arguments":[{"a":3,"b":12}]}}
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# {"type":"function","function":{"name":"add","arguments":[{"a":11,"b":49}}]}}
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```
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#### Complex Tool Integration
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```python
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from pydantic import BaseModel, Field
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from typing import List, Optional
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class SendEmail(BaseModel):
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"""Send an email to specified recipients."""
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to: List[str] = Field(..., description="List of email recipients")
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subject: str = Field(..., description="Email subject")
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body: str = Field(..., description="Email content/body")
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cc: Optional[List[str]] = Field(None, description="CC recipients")
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attachments: Optional[List[str]] = Field(None, description="List of attachment file paths")
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class WeatherInfo(BaseModel):
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"""Get weather information for a specific location."""
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city: str = Field(..., description="City name")
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country: Optional[str] = Field(None, description="Country name")
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units: str = Field("celsius", description="Temperature units (celsius/fahrenheit)")
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class SearchWeb(BaseModel):
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"""Search the web for given query."""
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query: str = Field(..., description="Search query")
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num_results: int = Field(5, description="Number of results to return")
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language: str = Field("en", description="Search language")
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class CreateCalendarEvent(BaseModel):
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"""Create a calendar event."""
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title: str = Field(..., description="Event title")
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start_time: str = Field(..., description="Event start time (ISO format)")
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end_time: str = Field(..., description="Event end time (ISO format)")
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description: Optional[str] = Field(None, description="Event description")
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attendees: Optional[List[str]] = Field(None, description="List of attendee emails")
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class TranslateText(BaseModel):
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"""Translate text between languages."""
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text: str = Field(..., description="Text to translate")
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source_lang: str = Field(..., description="Source language code (e.g., 'en', 'es')")
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target_lang: str = Field(..., description="Target language code (e.g., 'fr', 'de')")
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class SetReminder(BaseModel):
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"""Set a reminder for a specific time."""
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message: str = Field(..., description="Reminder message")
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time: str = Field(..., description="Reminder time (ISO format)")
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priority: str = Field("normal", description="Priority level (low/normal/high)")
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# Combine all tools
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tools = [
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SendEmail,
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WeatherInfo,
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SearchWeb,
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CreateCalendarEvent,
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TranslateText,
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SetReminder
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]
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llm_tools = llm.bind_tools(tools)
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# Example usage
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query = "Set a reminder to call John at 3 PM tomorrow. Also, translate 'Hello, how are you?' to Spanish."
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print(llm_tools.invoke(query).content)
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# Output:
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# {"type":"function","function":{"name":"SetReminder","arguments":{"message":"Call John at 3 PM tomorrow"},"arguments":{"time":"","priority":"normal"}}}
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# {"type":"function","function":{"name":"TranslateText","arguments":{"text":"Hello, how are you?", "source_lang":"en", "target_lang":"es"}}
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```
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## Core Features
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* Arithmetic computation support
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* Advanced function/tool calling capabilities
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* Seamless Langchain integration
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* Full Ollama platform compatibility
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## Technical Details
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### Dataset Information
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Training utilized the **`hiyouga/glaive-function-calling-v2-sharegpt`** dataset, featuring comprehensive function calling interaction examples.
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### Known Limitations
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* Basic function/tool calling
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* English language support exclusively
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* Ollama installation dependency
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## Important Notes & Considerations
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### Potential Limitations and Edge Cases
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* **Function Parameter Sensitivity**: The model may occasionally misinterpret complex parameter combinations, especially when multiple optional parameters are involved. Double-check parameter values in critical applications.
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* **Response Format Variations**:
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- In some cases, the function calling format might deviate from the expected JSON structure
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- The model may generate additional explanatory text alongside the function call
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- Multiple function calls in a single query might not always be processed in the expected order
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* **Error Handling Considerations**:
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- Empty or null values might not be handled consistently across different function types
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- Complex nested objects may sometimes be flattened unexpectedly
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- Array inputs might occasionally be processed as single values
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### Best Practices for Reliability
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1. **Input Validation**:
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- Always validate input parameters before processing
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- Implement proper error handling for malformed function calls
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- Consider adding default values for optional parameters
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2. **Testing Recommendations**:
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- Test with various input combinations and edge cases
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- Implement retry logic for inconsistent responses
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- Log and monitor function call patterns for debugging
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3. **Performance Optimization**:
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- Keep function descriptions concise and clear
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- Limit the number of simultaneous function calls
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- Cache frequently used function results when possible
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### Known Issues
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* Model may struggle with:
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- Very long function descriptions
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- Highly complex nested parameter structures
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- Ambiguous or overlapping function purposes
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- Non-English parameter values or descriptions
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## Development
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### Contributing Guidelines
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We welcome contributions through issues and pull requests for improvements and bug fixes.
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### License Information
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Released under Apache 2.0 license. See LICENSE file for complete terms.
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## Academic Citation
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```bibtex
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@misc{function-calling-llama,
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author = {nguyenthanhthuan_banhmi},
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title = {Function Calling Llama Model Vesion 1},
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year = {2024},
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publisher = {GitHub},
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journal = {GitHub repository}
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}
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```
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