xLAM-7b-fc-r
| Property | Value |
|---|---|
| Parameter Count | 6.91B |
| Context Length | 4k tokens |
| License | CC-BY-NC-4.0 |
| Research Paper | APIGen: Automated Pipeline for Generating Verifiable and Diverse Function-Calling Datasets |
What is xLAM-7b-fc-r?
xLAM-7b-fc-r is a specialized Large Action Model (LAM) developed by Salesforce, specifically optimized for function-calling and tool use capabilities. Built on the foundation of deepseek-coder, this model represents a significant advancement in AI's ability to translate natural language instructions into executable API calls and actions.
Implementation Details
The model is implemented using PyTorch and supports BF16 precision, making it efficient for deployment on personal devices. It features a 4k token context window and leverages the Transformers architecture with specific optimizations for function-calling tasks.
- Built on deepseek-coder architecture with specialized function-calling capabilities
- Supports deployment via Hugging Face Transformers and vLLM
- Includes comprehensive JSON output formatting for API interactions
Core Capabilities
- Achieves 88.24% accuracy on the Berkeley Function-Calling Leaderboard
- Specialized in translating natural language to structured API calls
- Supports multiple programming languages including Java, JavaScript, and Python
- Capable of autonomous planning and execution of complex tasks
Frequently Asked Questions
Q: What makes this model unique?
xLAM-7b-fc-r stands out for its exceptional function-calling capabilities while maintaining a relatively small parameter count, making it suitable for deployment on personal devices. It achieves competitive performance against much larger models, securing 3rd place on the BFCL benchmark.
Q: What are the recommended use cases?
The model is ideal for applications requiring API integration, tool use automation, and structured function calling. Common use cases include weather information retrieval, social media management, and financial service interactions, particularly in scenarios where precise API interaction is crucial.
