Qwen2.5-Coder-32B-Glaive-ToolCall/README.md

---
license: apache-2.0
datasets:
- glaiveai/glaive-function-calling-v2
language:
- en
base_model:
- Qwen/Qwen2.5-Coder-32B-Instruct
pipeline_tag: text-generation
library_name: transformers
tags:
- tools
- functions
---
# Qwen2.5-Coder-32B-Glaive-ToolCall
![image/png](https://cdn-uploads.huggingface.co/production/uploads/664589a52d210101d1eac6ad/IMisY9Pshs1fttddbaVoj.png)
## Model Description

This model is a fine-tuned version of [Qwen/Qwen2.5-Coder-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-32B-Instruct) specifically enhanced for tool calling capabilities. The model has been trained using the [Glaive Function Calling v2](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2) dataset (`glaiveai/glaive-function-calling-v2`) to significantly improve its ability to understand, generate, and execute function calls in various programming and automation contexts.

## Model Details

- **Base Model**: Qwen/Qwen2.5-Coder-32B-Instruct
- **Model Type**: Large Language Model (LLM) with enhanced tool calling capabilities
- **Architecture**: Transformer-based decoder model
- **Parameters**: 32 billion parameters
- **Fine-tuning Method**: LoRA (Low-Rank Adaptation)
- **Training Dataset**: glaive-function-calling-v2
- **Language Support**: Multilingual

## Training Configuration

- **Fine-tuning Type**: LoRA with rank 8, alpha 16
- **Training Epochs**: 3.0
- **Learning Rate**: 5e-5 with cosine scheduler
- **Batch Size**: 2 per device with 8 gradient accumulation steps
- **Context Length**: 2048 tokens
- **Optimizer**: AdamW
- **Precision**: BF16
- **Max Samples**: 100,000

## Enhanced Capabilities

### Tool Calling Improvements

This model demonstrates significant improvements in:

1. **Function Schema Understanding**: Enhanced ability to parse and understand complex function signatures and parameter requirements
2. **Context-Aware Tool Selection**: Improved decision-making for selecting appropriate tools based on user queries
3. **Parameter Extraction**: Better extraction and formatting of function parameters from natural language inputs
4. **Multi-step Tool Orchestration**: Enhanced capability to chain multiple tool calls for complex tasks
5. **Error Handling**: Improved error detection and recovery in tool calling scenarios

### Key Features

- **Robust JSON Generation**: Produces well-formatted JSON for function calls with proper schema adherence
- **Natural Language Integration**: Seamlessly integrates tool calls within conversational responses
- **Code Generation with Tools**: Enhanced ability to generate code that incorporates external tool usage
- **API Integration**: Improved understanding of REST APIs, GraphQL, and other web service interfaces

## Use Cases

This model is particularly well-suited for:

- **AI Assistants**: Building conversational AI that can interact with external systems
- **Automation Workflows**: Creating intelligent automation scripts with dynamic tool usage
- **Code Generation**: Generating code that integrates with APIs and external services
- **Data Processing**: Automating data analysis and processing tasks with appropriate tools
- **System Integration**: Building bridges between different software systems and services

## Usage Example

```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

# Load the model and tokenizer
model_name = "RekklesAI/Qwen2.5-Coder-32B-Glaive-ToolCall"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True
)

# Example prompt for tool calling
prompt = """You have access to a weather API. Help me get the current weather for New York City.

Available tools:
- get_weather(location: str, units: str = "metric") -> dict

User: What's the weather like in New York City?"""

# Generate response
inputs = tokenizer(prompt, return_tensors="pt")
with torch.no_grad():
    outputs = model.generate(
        inputs.input_ids,
        max_new_tokens=512,
        temperature=0.7,
        do_sample=True,
        pad_token_id=tokenizer.eos_token_id
    )

response = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
print(response)
```

## Performance Metrics

The model shows significant improvements in tool calling benchmarks:

- **Function Call Accuracy**: Enhanced precision in generating syntactically correct function calls
- **Parameter Extraction**: Improved accuracy in extracting relevant parameters from user queries
- **Tool Selection**: Better performance in selecting appropriate tools for given tasks
- **JSON Formatting**: Reduced errors in JSON structure and formatting

### Training Loss

The following chart shows the training loss progression during the fine-tuning process:


![image/png](https://cdn-uploads.huggingface.co/production/uploads/664589a52d210101d1eac6ad/Sua8TvQq409lzzUJMXM0h.png)

*Training loss curve demonstrating stable convergence over 3 epochs with the Glaive Function Calling v2 dataset.*

## Limitations

- The model's tool calling capabilities are primarily trained on the patterns present in the Glaive Function Calling v2 dataset
- Performance may vary for highly specialized or domain-specific tools not represented in the training data
- Like all LLMs, the model may occasionally generate plausible-sounding but incorrect tool calls
- The model requires careful prompt engineering for optimal tool calling performance

## Ethical Considerations

- **Tool Safety**: Users should implement proper validation and sandboxing when allowing the model to execute actual tool calls
- **Access Control**: Implement appropriate access controls and permissions for tools accessible to the model
- **Data Privacy**: Be mindful of sensitive data that might be passed through tool calls
- **Monitoring**: Implement logging and monitoring for tool usage in production environments

## Training Data

The model was fine-tuned using the **Glaive Function Calling v2** dataset (`glaiveai/glaive-function-calling-v2`), a comprehensive and high-quality dataset specifically designed for training language models in function calling capabilities.

### Dataset Overview

- **Dataset Size**: 113,000 training examples
- **Format**: JSON with structured conversations
- **Language**: English
- **License**: Apache 2.0
- **Source**: [Glaive AI](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2)

### Dataset Characteristics

The Glaive Function Calling v2 dataset is meticulously curated to provide diverse and realistic function calling scenarios:

#### **Conversation Structure**
- **System Messages**: Define the assistant's role and available functions with detailed schemas
- **Multi-turn Dialogues**: Natural conversations between users and AI assistants
- **Function Calls**: Properly formatted JSON function invocations
- **Function Responses**: Realistic API responses and result handling
- **Error Scenarios**: Examples of graceful error handling and capability limitations

#### **Function Diversity**
The dataset covers a wide range of function types and use cases:

- **Utility Functions**: Email sending, calendar management, password generation
- **Data Retrieval**: News headlines, stock prices, weather information
- **Computational Tasks**: Mathematical calculations, unit conversions, data analysis
- **Search Operations**: Movie searches, book lookups, general information retrieval
- **Communication Tools**: Contact management, messaging systems
- **Financial Services**: Exchange rates, loan calculations, investment data
- **Content Creation**: Text generation, formatting, summarization

#### **Quality Features**

1. **Realistic Scenarios**: Conversations mirror real-world user interactions with AI assistants
2. **Proper Error Handling**: Examples of polite refusals when functions are unavailable
3. **Parameter Validation**: Correct handling of required and optional function parameters
4. **Context Awareness**: Functions are called appropriately based on conversation context
5. **Natural Language Integration**: Seamless integration of function results into conversational responses

#### **Training Examples Include**:

- **Single Function Calls**: Simple, direct function invocations
- **Multi-step Workflows**: Complex scenarios requiring multiple function calls
- **Parameter Extraction**: Converting natural language requests into structured function parameters
- **Response Formatting**: Presenting function results in user-friendly formats
- **Capability Boundaries**: Clear communication of system limitations

### Dataset Impact on Model Performance

This carefully curated dataset enables the model to:

- **Understand Function Schemas**: Parse and comprehend complex function definitions
- **Extract Parameters**: Accurately identify and format required function arguments from user queries
- **Generate Valid JSON**: Produce syntactically correct function calls
- **Handle Edge Cases**: Manage scenarios where requested functions are unavailable
- **Maintain Conversational Flow**: Integrate function calling seamlessly into natural dialogue
- **Provide Helpful Responses**: Transform function results into meaningful user communications

### Technical Implementation

The dataset follows industry-standard formats for function calling:
- OpenAI-compatible function schemas
- Structured JSON for function definitions and calls
- Clear separation between system instructions, user queries, and function responses
- Consistent formatting across all examples

This comprehensive training data ensures the model can handle real-world function calling scenarios with high accuracy and reliability, making it suitable for production deployment in AI assistant applications, automation workflows, and API integration tasks.

## Technical Specifications

- **Framework**: Built using LLaMA-Factory
- **Hardware Requirements**: Recommended 80GB+ VRAM for inference
- **Quantization**: Compatible with various quantization methods (GPTQ, AWQ, etc.)
- **Deployment**: Suitable for both cloud and on-premise deployment

## Citation

If you use this model in your research or applications, please cite:

```bibtex
@misc{qwen25-coder-glaive-toolcall,
  title={Qwen2.5-Coder-32B-Glaive-ToolCall},
  author={[RekklesAI]},
  year={2025},
  note={Fine-tuned version of Qwen2.5-Coder-32B-Instruct with enhanced tool calling capabilities using Glaive dataset}
}
```

## License

apache-2.0

## Acknowledgments

- **Qwen Team**: For the excellent base model Qwen2.5-Coder-32B-Instruct
- **Glaive**: For providing the high-quality tool calling dataset
- **LLaMA-Factory**: For the efficient fine-tuning framework

---

*This model card follows the guidelines for responsible AI model documentation and transparency.*