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Model: RekklesAI/Qwen2.5-Coder-32B-Glaive-ToolCall
Source: Original Platform

.gitattributes

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Modelfile

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+# ollama modelfile auto-generated by llamafactory
+
+FROM .
+
+TEMPLATE """{{ if .System }}<|im_start|>system
+{{ .System }}<|im_end|>
+{{ end }}{{ range .Messages }}{{ if eq .Role "user" }}<|im_start|>user
+{{ .Content }}<|im_end|>
+<|im_start|>assistant
+{{ else if eq .Role "assistant" }}{{ .Content }}<|im_end|>
+{{ end }}{{ end }}"""
+
+SYSTEM """You are Qwen, created by Alibaba Cloud. You are a helpful assistant."""
+
+PARAMETER stop "<|im_end|>"
+PARAMETER num_ctx 4096

README.md

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+---
+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.*

added_tokens.json

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config.json

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+    "Qwen2ForCausalLM"
+  ],
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+  "eos_token_id": 151645,
+  "hidden_act": "silu",
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+  "initializer_range": 0.02,
+  "intermediate_size": 27648,
+  "max_position_embeddings": 32768,
+  "max_window_layers": 64,
+  "model_type": "qwen2",
+  "num_attention_heads": 40,
+  "num_hidden_layers": 64,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000.0,
+  "sliding_window": 131072,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.51.3",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 152064
+}

generation_config.json

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+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "4.51.3"
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special_tokens_map.json

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+{
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+}

tokenizer.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9c5ae00e602b8860cbd784ba82a8aa14e8feecec692e7076590d014d7b7fdafa
+size 11421896

tokenizer_config.json

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+}