graliuce/Qwen2.5-3B-Instruct_MedMCQA.20.01_terminate_aug_1.0e-5

TEXT GENERATIONPricing:Input $0.32 / Cached $0.064 / Output $1.6Concurrent Unit Cost:1Model Size:3.1BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Aug 12, 2026Architecture:Transformer Featherless Exclusive Cold

graliuce/Qwen2.5-3B-Instruct_MedMCQA.20.01_terminate_aug_1.0e-5 is a 3.1 billion parameter instruction-tuned causal language model, fine-tuned from Qwen/Qwen2.5-3B-Instruct. This model specializes in medical question answering, having been trained on the graliuce/MedMCQA.20.01_terminate_aug dataset. It is optimized for tasks requiring medical domain knowledge and understanding, leveraging its 32768 token context length.

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Model Overview

This model, graliuce/Qwen2.5-3B-Instruct_MedMCQA.20.01_terminate_aug_1.0e-5, is a specialized version of the Qwen/Qwen2.5-3B-Instruct large language model. With 3.1 billion parameters and a 32768 token context length, it has been specifically fine-tuned for medical question-answering tasks.

Key Capabilities

  • Medical Domain Specialization: Fine-tuned on the graliuce/MedMCQA.20.01_terminate_aug dataset, enhancing its performance in medical contexts.
  • Instruction Following: Inherits instruction-following capabilities from its base Qwen2.5-3B-Instruct model.

Training Details

The model was trained using the TRL library, employing a Supervised Fine-Tuning (SFT) procedure. The training utilized specific versions of frameworks including TRL 0.17.0, Transformers 4.52.3, and Pytorch 2.6.0.

Use Cases

This model is particularly well-suited for applications requiring accurate responses to medical questions, leveraging its specialized training data. Developers can integrate it into systems for medical information retrieval, educational tools, or as a component in clinical decision support systems where medical knowledge is paramount.

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