Upload Readme

README.md

@@ -1,8 +1,8 @@
 ---
-base_model: unsloth/llama-3.2-1b-instruct
+base_model:
+- meta-llama/Llama-3.2-1B-Instruct
 language:
 - en
-- vi
 license: apache-2.0
 tags:
 - text-generation-inference
@@ -10,17 +10,231 @@ tags:
 - unsloth
 - llama
 - trl
-- ollama
+- Ollama
+- Tool-Calling
 datasets:
-- lilacai/glaive-function-calling-v2-sharegpt
+- hiyouga/glaive-function-calling-v2-sharegpt
 ---
+# Function Calling Llama Model
 
-# 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
+## 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
+
+1. Install [Ollama](https://ollama.com/)
+2. Install required Python packages:
+   ```bash
+   pip install langchain pydantic torch langchain-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
+```python
+from pydantic import BaseModel, Field
+
+class add(BaseModel):
+    """Addition operation for two integers."""
+    a: int = Field(..., description="First integer")
+    b: int = Field(..., description="Second integer")
+
+class multiply(BaseModel):
+    """Multiplication operation for two integers."""
+    a: int = Field(..., description="First integer")
+    b: int = Field(..., description="Second integer")
+
+# Tool registration
+tools = [add, multiply]
+llm_tools = llm.bind_tools(tools)
+
+# Execute query
+response = llm_tools.invoke(query)
+print(response.content)
+# Output:
+# {"type":"function","function":{"name":"multiply","arguments":[{"a":3,"b":12}]}}
+# {"type":"function","function":{"name":"add","arguments":[{"a":11,"b":49}}]}}
+```
+
+#### Complex Tool Integration
+
+```python
+from pydantic import BaseModel, Field
+from typing import List, Optional
+
+class SendEmail(BaseModel):
+    """Email dispatch functionality."""
+    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):
+    """Weather information retrieval."""
+    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):
+    """Web search functionality."""
+    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):
+    """Calendar event creation."""
+    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")
+
+# Tool integration
+tools = [SendEmail, WeatherInfo, SearchWeb, CreateCalendarEvent]
+llm_tools = llm.bind_tools(tools)
+
+# Example usage
+query = "Can you send an email to [email protected] about tomorrow's meeting?"
+print(llm_tools.invoke(query).content)
+# Output:
+#{
+#   "type": "function",
+#   "function": {
+#     "name": "SendEmail",
+#     "arguments": {
+#       "to": ["[email protected]"],
+#       "subject": "",
+#       "body": "",
+#       "cc": "",
+#       "attachments": "",
+#       "body": {
+#         "message": "Hello John, I wanted to let you know that we have a meeting scheduled for tomorrow. Please mark your calendar."
+#       }
+#     }
+#   }
+# }
+
+```
+
+## 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},
+    year = {2024},
+    publisher = {GitHub},
+    journal = {GitHub repository}
+}
+```