akshayballal/Qwen2.5-1.5B-Instruct-SFT-MedQA-merged
TEXT GENERATIONConcurrency Cost:1Model Size:1.5BQuant:BF16Ctx Length:32kPublished:Feb 8, 2026License:apache-2.0Architecture:Transformer Open Weights Warm
The akshayballal/Qwen2.5-1.5B-Instruct-SFT-MedQA-merged model is a 1.5 billion parameter instruction-tuned Qwen2.5 variant, developed by akshayballal. It was fine-tuned using Unsloth and Huggingface's TRL library, resulting in faster training. This model is specifically optimized for instruction-following tasks, leveraging its Qwen2.5 architecture for efficient performance.
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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.