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Model: RekklesAI/Qwen2.5-Coder-32B-Glaive-ToolCall Source: Original Platform
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# ollama modelfile auto-generated by llamafactory
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FROM .
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TEMPLATE """{{ if .System }}<|im_start|>system
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{{ .System }}<|im_end|>
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{{ end }}{{ range .Messages }}{{ if eq .Role "user" }}<|im_start|>user
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{{ .Content }}<|im_end|>
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<|im_start|>assistant
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{{ else if eq .Role "assistant" }}{{ .Content }}<|im_end|>
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{{ end }}{{ end }}"""
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SYSTEM """You are Qwen, created by Alibaba Cloud. You are a helpful assistant."""
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PARAMETER stop "<|im_end|>"
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PARAMETER num_ctx 4096
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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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||||
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||||
### Dataset Impact on Model Performance
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||||
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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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||||
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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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||||
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||||
apache-2.0
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||||
## Acknowledgments
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||||
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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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||||
---
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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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"Qwen2ForCausalLM"
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|
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|
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|
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|
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|
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|
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|
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}
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|
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"top_p": 0.8,
|
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"transformers_version": "4.51.3"
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}
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"model.layers.8.input_layernorm.weight": "model-00003-of-00014.safetensors",
|
||||
"model.layers.8.mlp.down_proj.weight": "model-00003-of-00014.safetensors",
|
||||
"model.layers.8.mlp.gate_proj.weight": "model-00002-of-00014.safetensors",
|
||||
"model.layers.8.mlp.up_proj.weight": "model-00003-of-00014.safetensors",
|
||||
"model.layers.8.post_attention_layernorm.weight": "model-00003-of-00014.safetensors",
|
||||
"model.layers.8.self_attn.k_proj.bias": "model-00002-of-00014.safetensors",
|
||||
"model.layers.8.self_attn.k_proj.weight": "model-00002-of-00014.safetensors",
|
||||
"model.layers.8.self_attn.o_proj.weight": "model-00002-of-00014.safetensors",
|
||||
"model.layers.8.self_attn.q_proj.bias": "model-00002-of-00014.safetensors",
|
||||
"model.layers.8.self_attn.q_proj.weight": "model-00002-of-00014.safetensors",
|
||||
"model.layers.8.self_attn.v_proj.bias": "model-00002-of-00014.safetensors",
|
||||
"model.layers.8.self_attn.v_proj.weight": "model-00002-of-00014.safetensors",
|
||||
"model.layers.9.input_layernorm.weight": "model-00003-of-00014.safetensors",
|
||||
"model.layers.9.mlp.down_proj.weight": "model-00003-of-00014.safetensors",
|
||||
"model.layers.9.mlp.gate_proj.weight": "model-00003-of-00014.safetensors",
|
||||
"model.layers.9.mlp.up_proj.weight": "model-00003-of-00014.safetensors",
|
||||
"model.layers.9.post_attention_layernorm.weight": "model-00003-of-00014.safetensors",
|
||||
"model.layers.9.self_attn.k_proj.bias": "model-00003-of-00014.safetensors",
|
||||
"model.layers.9.self_attn.k_proj.weight": "model-00003-of-00014.safetensors",
|
||||
"model.layers.9.self_attn.o_proj.weight": "model-00003-of-00014.safetensors",
|
||||
"model.layers.9.self_attn.q_proj.bias": "model-00003-of-00014.safetensors",
|
||||
"model.layers.9.self_attn.q_proj.weight": "model-00003-of-00014.safetensors",
|
||||
"model.layers.9.self_attn.v_proj.bias": "model-00003-of-00014.safetensors",
|
||||
"model.layers.9.self_attn.v_proj.weight": "model-00003-of-00014.safetensors",
|
||||
"model.norm.weight": "model-00014-of-00014.safetensors"
|
||||
}
|
||||
}
|
||||
31
special_tokens_map.json
Normal file
31
special_tokens_map.json
Normal file
@@ -0,0 +1,31 @@
|
||||
{
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"eos_token": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:9c5ae00e602b8860cbd784ba82a8aa14e8feecec692e7076590d014d7b7fdafa
|
||||
size 11421896
|
||||
209
tokenizer_config.json
Normal file
209
tokenizer_config.json
Normal file
@@ -0,0 +1,209 @@
|
||||
{
|
||||
"add_bos_token": false,
|
||||
"add_prefix_space": false,
|
||||
"added_tokens_decoder": {
|
||||
"151643": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151644": {
|
||||
"content": "<|im_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151645": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151646": {
|
||||
"content": "<|object_ref_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151647": {
|
||||
"content": "<|object_ref_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151648": {
|
||||
"content": "<|box_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151649": {
|
||||
"content": "<|box_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151650": {
|
||||
"content": "<|quad_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151651": {
|
||||
"content": "<|quad_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151652": {
|
||||
"content": "<|vision_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151653": {
|
||||
"content": "<|vision_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151654": {
|
||||
"content": "<|vision_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151655": {
|
||||
"content": "<|image_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151656": {
|
||||
"content": "<|video_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151657": {
|
||||
"content": "<tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151658": {
|
||||
"content": "</tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151659": {
|
||||
"content": "<|fim_prefix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151660": {
|
||||
"content": "<|fim_middle|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151661": {
|
||||
"content": "<|fim_suffix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151662": {
|
||||
"content": "<|fim_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151663": {
|
||||
"content": "<|repo_name|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151664": {
|
||||
"content": "<|file_sep|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"bos_token": null,
|
||||
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n {%- endif %}\n {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0]['role'] == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n {%- else %}\n {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role }}\n {%- if message.content %}\n {{- '\\n' + message.content }}\n {%- endif %}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"extra_special_tokens": {},
|
||||
"model_max_length": 32768,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"padding_side": "left",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null
|
||||
}
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user