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Model: RekklesAI/Qwen2.5-Coder-32B-Glaive-ToolCall Source: Original Platform
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README.md
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---
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license: apache-2.0
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datasets:
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- glaiveai/glaive-function-calling-v2
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language:
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- en
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base_model:
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- Qwen/Qwen2.5-Coder-32B-Instruct
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- tools
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- functions
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---
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# Qwen2.5-Coder-32B-Glaive-ToolCall
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## Model Description
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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.
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## Model Details
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- **Base Model**: Qwen/Qwen2.5-Coder-32B-Instruct
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- **Model Type**: Large Language Model (LLM) with enhanced tool calling capabilities
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- **Architecture**: Transformer-based decoder model
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- **Parameters**: 32 billion parameters
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- **Fine-tuning Method**: LoRA (Low-Rank Adaptation)
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- **Training Dataset**: glaive-function-calling-v2
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- **Language Support**: Multilingual
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## Training Configuration
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- **Fine-tuning Type**: LoRA with rank 8, alpha 16
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- **Training Epochs**: 3.0
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- **Learning Rate**: 5e-5 with cosine scheduler
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- **Batch Size**: 2 per device with 8 gradient accumulation steps
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- **Context Length**: 2048 tokens
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- **Optimizer**: AdamW
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- **Precision**: BF16
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- **Max Samples**: 100,000
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## Enhanced Capabilities
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### Tool Calling Improvements
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This model demonstrates significant improvements in:
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1. **Function Schema Understanding**: Enhanced ability to parse and understand complex function signatures and parameter requirements
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2. **Context-Aware Tool Selection**: Improved decision-making for selecting appropriate tools based on user queries
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3. **Parameter Extraction**: Better extraction and formatting of function parameters from natural language inputs
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4. **Multi-step Tool Orchestration**: Enhanced capability to chain multiple tool calls for complex tasks
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5. **Error Handling**: Improved error detection and recovery in tool calling scenarios
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### Key Features
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- **Robust JSON Generation**: Produces well-formatted JSON for function calls with proper schema adherence
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- **Natural Language Integration**: Seamlessly integrates tool calls within conversational responses
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- **Code Generation with Tools**: Enhanced ability to generate code that incorporates external tool usage
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- **API Integration**: Improved understanding of REST APIs, GraphQL, and other web service interfaces
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## Use Cases
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This model is particularly well-suited for:
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- **AI Assistants**: Building conversational AI that can interact with external systems
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- **Automation Workflows**: Creating intelligent automation scripts with dynamic tool usage
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- **Code Generation**: Generating code that integrates with APIs and external services
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- **Data Processing**: Automating data analysis and processing tasks with appropriate tools
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- **System Integration**: Building bridges between different software systems and services
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## Usage Example
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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# Load the model and tokenizer
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model_name = "RekklesAI/Qwen2.5-Coder-32B-Glaive-ToolCall"
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tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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trust_remote_code=True
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)
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# Example prompt for tool calling
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prompt = """You have access to a weather API. Help me get the current weather for New York City.
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Available tools:
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- get_weather(location: str, units: str = "metric") -> dict
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User: What's the weather like in New York City?"""
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# Generate response
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inputs = tokenizer(prompt, return_tensors="pt")
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with torch.no_grad():
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outputs = model.generate(
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inputs.input_ids,
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max_new_tokens=512,
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temperature=0.7,
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do_sample=True,
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pad_token_id=tokenizer.eos_token_id
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)
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response = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
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print(response)
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```
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## Performance Metrics
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The model shows significant improvements in tool calling benchmarks:
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- **Function Call Accuracy**: Enhanced precision in generating syntactically correct function calls
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- **Parameter Extraction**: Improved accuracy in extracting relevant parameters from user queries
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- **Tool Selection**: Better performance in selecting appropriate tools for given tasks
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- **JSON Formatting**: Reduced errors in JSON structure and formatting
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### Training Loss
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The following chart shows the training loss progression during the fine-tuning process:
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*Training loss curve demonstrating stable convergence over 3 epochs with the Glaive Function Calling v2 dataset.*
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## Limitations
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- The model's tool calling capabilities are primarily trained on the patterns present in the Glaive Function Calling v2 dataset
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- Performance may vary for highly specialized or domain-specific tools not represented in the training data
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- Like all LLMs, the model may occasionally generate plausible-sounding but incorrect tool calls
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- The model requires careful prompt engineering for optimal tool calling performance
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## Ethical Considerations
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- **Tool Safety**: Users should implement proper validation and sandboxing when allowing the model to execute actual tool calls
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- **Access Control**: Implement appropriate access controls and permissions for tools accessible to the model
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- **Data Privacy**: Be mindful of sensitive data that might be passed through tool calls
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- **Monitoring**: Implement logging and monitoring for tool usage in production environments
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## Training Data
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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.
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### Dataset Overview
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- **Dataset Size**: 113,000 training examples
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- **Format**: JSON with structured conversations
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- **Language**: English
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- **License**: Apache 2.0
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- **Source**: [Glaive AI](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2)
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### Dataset Characteristics
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The Glaive Function Calling v2 dataset is meticulously curated to provide diverse and realistic function calling scenarios:
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#### **Conversation Structure**
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- **System Messages**: Define the assistant's role and available functions with detailed schemas
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- **Multi-turn Dialogues**: Natural conversations between users and AI assistants
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- **Function Calls**: Properly formatted JSON function invocations
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- **Function Responses**: Realistic API responses and result handling
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- **Error Scenarios**: Examples of graceful error handling and capability limitations
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#### **Function Diversity**
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The dataset covers a wide range of function types and use cases:
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- **Utility Functions**: Email sending, calendar management, password generation
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- **Data Retrieval**: News headlines, stock prices, weather information
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- **Computational Tasks**: Mathematical calculations, unit conversions, data analysis
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- **Search Operations**: Movie searches, book lookups, general information retrieval
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- **Communication Tools**: Contact management, messaging systems
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- **Financial Services**: Exchange rates, loan calculations, investment data
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- **Content Creation**: Text generation, formatting, summarization
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#### **Quality Features**
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1. **Realistic Scenarios**: Conversations mirror real-world user interactions with AI assistants
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2. **Proper Error Handling**: Examples of polite refusals when functions are unavailable
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3. **Parameter Validation**: Correct handling of required and optional function parameters
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4. **Context Awareness**: Functions are called appropriately based on conversation context
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5. **Natural Language Integration**: Seamless integration of function results into conversational responses
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#### **Training Examples Include**:
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- **Single Function Calls**: Simple, direct function invocations
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- **Multi-step Workflows**: Complex scenarios requiring multiple function calls
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- **Parameter Extraction**: Converting natural language requests into structured function parameters
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- **Response Formatting**: Presenting function results in user-friendly formats
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- **Capability Boundaries**: Clear communication of system limitations
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### Dataset Impact on Model Performance
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This carefully curated dataset enables the model to:
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- **Understand Function Schemas**: Parse and comprehend complex function definitions
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- **Extract Parameters**: Accurately identify and format required function arguments from user queries
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- **Generate Valid JSON**: Produce syntactically correct function calls
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- **Handle Edge Cases**: Manage scenarios where requested functions are unavailable
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- **Maintain Conversational Flow**: Integrate function calling seamlessly into natural dialogue
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- **Provide Helpful Responses**: Transform function results into meaningful user communications
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### Technical Implementation
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The dataset follows industry-standard formats for function calling:
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- OpenAI-compatible function schemas
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- Structured JSON for function definitions and calls
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- Clear separation between system instructions, user queries, and function responses
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- Consistent formatting across all examples
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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.
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## Technical Specifications
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- **Framework**: Built using LLaMA-Factory
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- **Hardware Requirements**: Recommended 80GB+ VRAM for inference
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- **Quantization**: Compatible with various quantization methods (GPTQ, AWQ, etc.)
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- **Deployment**: Suitable for both cloud and on-premise deployment
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## Citation
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If you use this model in your research or applications, please cite:
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```bibtex
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@misc{qwen25-coder-glaive-toolcall,
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title={Qwen2.5-Coder-32B-Glaive-ToolCall},
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author={[RekklesAI]},
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year={2025},
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note={Fine-tuned version of Qwen2.5-Coder-32B-Instruct with enhanced tool calling capabilities using Glaive dataset}
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}
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```
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## License
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apache-2.0
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## Acknowledgments
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- **Qwen Team**: For the excellent base model Qwen2.5-Coder-32B-Instruct
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- **Glaive**: For providing the high-quality tool calling dataset
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- **LLaMA-Factory**: For the efficient fine-tuning framework
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---
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*This model card follows the guidelines for responsible AI model documentation and transparency.*
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