ermiaazarkhalili/Qwen2.5-14B-Instruct_Function_Calling_xLAM
The ermiaazarkhalili/Qwen2.5-14B-Instruct_Function_Calling_xLAM model is a 14.8 billion parameter language model, fine-tuned from Qwen/Qwen2.5-14B-Instruct. It is specifically optimized for function calling tasks, having been trained on the Salesforce/xlam-function-calling-60k dataset using Supervised Fine-Tuning (SFT) with LoRA adapters. This model is designed for applications requiring robust tool use and function invocation capabilities, with a fine-tuning context length of 2,048 tokens.
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Overview
This model, ermiaazarkhalili/Qwen2.5-14B-Instruct_Function_Calling_xLAM, is a 14.8 billion parameter language model derived from the Qwen2.5-14B-Instruct base model. It has been specifically fine-tuned for function calling capabilities using Supervised Fine-Tuning (SFT) with LoRA adapters on the Salesforce/xlam-function-calling-60k dataset.
Key Capabilities
- Function Calling Optimization: Specialized training for understanding and generating function calls.
- Efficient Fine-Tuning: Utilizes LoRA (Low-Rank Adaptation) with 4-bit quantization for efficient training.
- Base Model: Built upon the robust Qwen2.5-14B-Instruct architecture.
- Inference Flexibility: Available in multiple formats, including GGUF quantizations for CPU/mixed CPU/GPU inference.
Good For
- Research: Exploring language model fine-tuning techniques, especially for function calling.
- Prototyping: Developing conversational AI agents that require tool use or function invocation.
- Educational Purposes: Learning about SFT, LoRA, and function-calling model development.
- Personal Projects: Implementing AI assistants with specific action capabilities.