HYU-NLP-EVAL/qwen3-4b-rar-medicine-onlinerubrics-dense-seed11-step-045
The HYU-NLP-EVAL/qwen3-4b-rar-medicine-onlinerubrics-dense-seed11-step-045 is a 4 billion parameter Qwen3-based model, fine-tuned for medical applications using online rubrics. This model is a dense checkpoint from the `phase1-online-rubrics-medicine-full-dense-20260919-seed11` run, designed for research use in medical natural language processing. It features a 32768-token context length and is optimized for tasks requiring nuanced understanding within the medical domain.
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Overview
This model, HYU-NLP-EVAL/qwen3-4b-rar-medicine-onlinerubrics-dense-seed11-step-045, is a 4 billion parameter variant of the Qwen3 architecture. It has been specifically fine-tuned for applications within the medical domain, leveraging an online rubrics methodology. This particular version represents a dense checkpoint from the phase1-online-rubrics-medicine-full-dense-20260919-seed11 training run.
Key Characteristics
- Architecture: Based on the Qwen3 model family.
- Parameter Count: 4 billion parameters, offering a balance between performance and computational efficiency.
- Context Length: Supports a substantial context window of 32768 tokens, beneficial for processing longer medical texts.
- Domain Specificity: Fine-tuned with online rubrics for medical applications, indicating specialized knowledge in this field.
- Checkpoint Type: A dense checkpoint, with the root containing a BF16 model suitable for inference.
Primary Use Case
This model is intended for research use only within medical natural language processing (NLP). Its specialized training makes it suitable for tasks requiring a deep understanding of medical terminology, concepts, and contexts, potentially including medical text analysis, information extraction, or question answering within the healthcare domain. The original_checkpoint/ directory contains the original veRL checkpoint files, comprising only the model parameters.