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README.md

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+# Qwen2.5-Coder-32B-Glaive-ToolCall
+
+## 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_toolcall_100k` dataset 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_toolcall_100k
+- **Language Support**: Multilingual with strong English and Chinese capabilities
+
+## 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 = "path/to/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:
+
+![Training Loss](training_loss.png)
+
+*Training loss curve demonstrating stable convergence over 3 epochs with the glaive_toolcall_100k dataset.*
+
+## Limitations
+
+- The model's tool calling capabilities are primarily trained on the patterns present in the glaive_toolcall_100k 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_toolcall_100k` dataset, which contains:
+- 100,000 high-quality examples of tool calling scenarios
+- Diverse function schemas and use cases
+- Multi-turn conversations involving tool usage
+- Error handling and edge case examples
+
+## 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={[Your Name/Organization]},
+  year={2025},
+  note={Fine-tuned version of Qwen2.5-Coder-32B-Instruct with enhanced tool calling capabilities using Glaive dataset}
+}
+```
+
+## License
+
+This model inherits the license from the base Qwen2.5-Coder-32B-Instruct model. Please refer to the original model's license for usage terms and conditions.
+
+## 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
+
+## Contact
+
+For questions, issues, or collaboration opportunities, please [contact information].
+
+---
+
+*This model card follows the guidelines for responsible AI model documentation and transparency.*