HYU-NLP-EVAL/qwen3-4b-rar-medicine-onlinerubrics-dense-seed11-step-040

TEXT GENERATIONPricing:Input $0.4 / Cached $0.08 / Output $0.8Concurrent Unit Cost:1Model Size:4BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Oct 1, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold

The HYU-NLP-EVAL/qwen3-4b-rar-medicine-onlinerubrics-dense-seed11-step-040 is a 4 billion parameter Qwen3-based language model, specifically a BF16 model for inference, developed by HYU-NLP-EVAL. This model is a checkpoint from the 'phase1-online-rubrics-medicine-full-dense-20260919-seed11' run, optimized for research use in the medical domain. It is designed for tasks related to online rubrics in medicine, leveraging its dense architecture to process and understand medical text.

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HYU-NLP-EVAL/qwen3-4b-rar-medicine-onlinerubrics-dense-seed11-step-040 Overview

This model is a 4 billion parameter variant of the Qwen3 architecture, developed by HYU-NLP-EVAL. It represents a specific checkpoint (step 40) from the phase1-online-rubrics-medicine-full-dense-20260919-seed11 training run. The model is provided in BF16 format, suitable for efficient inference.

Key Capabilities

  • Medical Domain Specialization: Fine-tuned for applications within the medical field, particularly concerning online rubrics.
  • Dense Architecture: Utilizes a dense model structure, indicating a focus on comprehensive feature learning.
  • Inference-Optimized: Provided as a BF16 model, suggesting readiness for deployment in inference scenarios.

Good For

  • Medical Research: Primarily intended for research purposes, especially in natural language processing within medicine.
  • Online Rubrics Analysis: Suitable for tasks involving the understanding, generation, or evaluation of online rubrics in a medical context.
  • Exploration of Checkpoints: Useful for researchers studying the evolution and performance of models at specific training steps.

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