Model Overview

This model, akshayballal/Qwen2.5-1.5B-Instruct-SFT-MedQA-merged, is an instruction-tuned variant of the Qwen2.5 architecture, developed by akshayballal. It features 1.5 billion parameters and was fine-tuned from unsloth/qwen2.5-1.5b-instruct-unsloth-bnb-4bit.

Key Capabilities

• Instruction Following: The model is specifically designed and fine-tuned for instruction-following tasks, making it suitable for applications requiring precise responses to user prompts.
• Efficient Training: It was trained using Unsloth and Huggingface's TRL library, which enabled a 2x faster training process compared to standard methods.
• Qwen2.5 Architecture: Leverages the Qwen2.5 base architecture, known for its performance in various language understanding and generation tasks.

Good For

• Applications requiring a compact yet capable instruction-tuned model.
• Scenarios where efficient inference from a 1.5B parameter model is beneficial.
• Tasks that can leverage a model fine-tuned with advanced training techniques like Unsloth for improved performance.