Update README.md

README.md

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 ---
-base_model: unsloth/llama-3.2-1b-instruct-bnb-4bit
+base_model:
+- meta-llama/Llama-3.2-1B-Instruct
 language:
 - en
+- vi
 license: apache-2.0
 tags:
 - text-generation-inference
 - transformers
 - unsloth
 - llama
-- gguf
+- trl
+- Ollama
+- Tool-Calling
+datasets:
+- hiyouga/glaive-function-calling-v2-sharegpt
 ---
+# Function Calling Llama Model Version 2
 
-# Uploaded  model
+## Overview
+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.
 
-- **Developed by:** nguyenthanhthuan
-- **License:** apache-2.0
-- **Finetuned from model :** unsloth/llama-3.2-1b-instruct-bnb-4bit
+## Model Specifications
 
-This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+* **Base Architecture**: meta-llama/Llama-3.2-1B-Instruct
+* **Primary Language**: English (Function/Tool Calling), Vietnamese
+* **Licensing**: Apache 2.0
+* **Primary Developer**: nguyenthanhthuan_banhmi
+* **Key Capabilities**: text-generation-inference, transformers, unsloth, llama, trl, Ollama, Tool-Calling
 
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+## Getting Started
+
+### Prerequisites
+Method 1:
+1. Install [Ollama](https://ollama.com/)
+2. Install required Python packages:
+   ```bash
+   pip install langchain pydantic torch langchain-ollama langchain_core
+   ```
+Method 1:
+1. Click use this model
+2. Click Ollama
+
+### Installation Steps
+
+1. Clone the repository
+2. Navigate to the project directory
+3. Create the model in Ollama:
+   ```bash
+   ollama create <model_name> -f <path_to_modelfile>
+   ```
+
+## Implementation Guide
+
+### Model Initialization
+
+```python
+from langchain_ollama import ChatOllama 
+
+# Initialize model instance
+llm = ChatOllama(model="<model_name>")
+```
+
+### Basic Usage Example
+
+```python
+# Arithmetic computation example
+query = "What is 3 * 12? Also, what is 11 + 49?"
+response = llm.invoke(query)
+
+print(response.content)
+# Output:
+# 1. 3 times 12 is 36.
+# 2. 11 plus 49 is 60.
+```
+
+### Advanced Function Calling (English Recommended)
+
+#### Basic Arithmetic Tools (Different from the first version)
+```python
+from pydantic import BaseModel
+
+
+# Note that the docstrings here are crucial, as they will be passed along
+# to the model along with the class name.
+class add(BaseModel):
+    """Add two integers together."""
+
+    a: int = Field(..., description="First integer")
+    b: int = Field(..., description="Second integer")
+
+
+class multiply(BaseModel):
+    """Multiply two integers together."""
+
+    a: int = Field(..., description="First integer")
+    b: int = Field(..., description="Second integer")
+
+
+tools = [add, multiply]
+llm_with_tools = llm.bind_tools(tools)
+
+# Execute query and parser result (Different from the first version)
+from langchain_core.output_parsers.openai_tools import PydanticToolsParser
+
+query = "What is 3 * 12? Also, what is 11 + 49?"
+chain = llm_with_tools | PydanticToolsParser(tools=tools)
+result = chain.invoke(query)
+print(result)
+
+# Output:
+# [multiply(a=3, b=12), add(a=11, b=49)]
+```
+
+#### Complex Tool Integration (Different from the first version)
+
+```python
+from pydantic import BaseModel, Field
+from typing import List, Optional
+
+class SendEmail(BaseModel):
+    """Send an email to specified recipients."""
+
+    to: List[str] = Field(..., description="List of email recipients")
+    subject: str = Field(..., description="Email subject")
+    body: str = Field(..., description="Email content/body")
+    cc: Optional[List[str]] = Field(None, description="CC recipients")
+    attachments: Optional[List[str]] = Field(None, description="List of attachment file paths")
+
+class WeatherInfo(BaseModel):
+    """Get weather information for a specific location."""
+
+    city: str = Field(..., description="City name")
+    country: Optional[str] = Field(None, description="Country name")
+    units: str = Field("celsius", description="Temperature units (celsius/fahrenheit)")
+
+class SearchWeb(BaseModel):
+    """Search the web for given query."""
+
+    query: str = Field(..., description="Search query")
+    num_results: int = Field(5, description="Number of results to return")
+    language: str = Field("en", description="Search language")
+
+class CreateCalendarEvent(BaseModel):
+    """Create a calendar event."""
+
+    title: str = Field(..., description="Event title")
+    start_time: str = Field(..., description="Event start time (ISO format)")
+    end_time: str = Field(..., description="Event end time (ISO format)")
+    description: Optional[str] = Field(None, description="Event description")
+    attendees: Optional[List[str]] = Field(None, description="List of attendee emails")
+
+class TranslateText(BaseModel):
+    """Translate text between languages."""
+
+    text: str = Field(..., description="Text to translate")
+    source_lang: str = Field(..., description="Source language code (e.g., 'en', 'es')")
+    target_lang: str = Field(..., description="Target language code (e.g., 'fr', 'de')")
+
+class SetReminder(BaseModel):
+    """Set a reminder for a specific time."""
+
+    message: str = Field(..., description="Reminder message")
+    time: str = Field(..., description="Reminder time (ISO format)")
+    priority: str = Field("normal", description="Priority level (low/normal/high)")
+tools = [
+    SendEmail,
+    WeatherInfo,
+    SearchWeb,
+    CreateCalendarEvent,
+    TranslateText,
+    SetReminder
+]
+llm_tools = llm.bind_tools(tools)
+
+# # Execute query and parser result (Different from the first version)
+from langchain_core.output_parsers.openai_tools import PydanticToolsParser
+
+query = "Set a reminder to call John at 3 PM tomorrow. Also, translate 'Hello, how are you?' to Spanish."
+chain = llm_tools | PydanticToolsParser(tools=tools)
+result = chain.invoke(query)
+print(result)
+
+# Output:
+# [SetReminder(message='Set a reminder for a specific time.', time='3 PM tomorrow', priority='normal'),
+# TranslateText(text='Hello, how are you?', source_lang='en', target_lang='es')]
+```
+
+## Core Features
+
+* Arithmetic computation support
+* Advanced function/tool calling capabilities
+* Seamless Langchain integration
+* Full Ollama platform compatibility
+
+## Technical Details
+
+### Dataset Information
+Training utilized the **`hiyouga/glaive-function-calling-v2-sharegpt`** dataset, featuring comprehensive function calling interaction examples.
+
+### Known Limitations
+
+* Basic function/tool calling
+* English language support exclusively
+* Ollama installation dependency
+
+## Important Notes & Considerations
+
+### Potential Limitations and Edge Cases
+
+* **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.
+
+* **Response Format Variations**:
+  - In some cases, the function calling format might deviate from the expected JSON structure
+  - The model may generate additional explanatory text alongside the function call
+  - Multiple function calls in a single query might not always be processed in the expected order
+
+* **Error Handling Considerations**:
+  - Empty or null values might not be handled consistently across different function types
+  - Complex nested objects may sometimes be flattened unexpectedly
+  - Array inputs might occasionally be processed as single values
+
+### Best Practices for Reliability
+
+1. **Input Validation**:
+   - Always validate input parameters before processing
+   - Implement proper error handling for malformed function calls
+   - Consider adding default values for optional parameters
+
+2. **Testing Recommendations**:
+   - Test with various input combinations and edge cases
+   - Implement retry logic for inconsistent responses
+   - Log and monitor function call patterns for debugging
+
+3. **Performance Optimization**:
+   - Keep function descriptions concise and clear
+   - Limit the number of simultaneous function calls
+   - Cache frequently used function results when possible
+
+### Known Issues
+
+* Model may struggle with:
+  - Very long function descriptions
+  - Highly complex nested parameter structures
+  - Ambiguous or overlapping function purposes
+  - Non-English parameter values or descriptions
+
+## Development
+
+### Contributing Guidelines
+We welcome contributions through issues and pull requests for improvements and bug fixes.
+
+### License Information
+Released under Apache 2.0 license. See LICENSE file for complete terms.
+
+## Academic Citation
+
+```bibtex
+@misc{function-calling-llama,
+    author = {nguyenthanhthuan_banhmi},
+    title = {Function Calling Llama Model Version 2} ,
+    year = {2024},
+    publisher = {GitHub},
+    journal = {GitHub repository}
+}
+```