HYU-NLP-EVAL/qwen3-4b-rar-medicine-onlinerubrics-dense-seed11-step-006

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-006 model is a 4 billion parameter language model, likely based on the Qwen3 architecture, with a context length of 32768 tokens. It is a dense checkpoint from the "RaR-Medicine OnlineRubrics-Every" training run, specifically step 6 of the `phase1-online-rubrics-medicine-full-dense-20260919-seed11` process. This model is intended for research use, particularly in medical applications, and includes BF16 model files for inference.

Loading preview...

HYU-NLP-EVAL/qwen3-4b-rar-medicine-onlinerubrics-dense-seed11-step-006 Overview

This model is a 4 billion parameter language model, likely derived from the Qwen3 architecture, with a substantial context window of 32768 tokens. It represents a specific dense checkpoint, step 6, from the phase1-online-rubrics-medicine-full-dense-20260919-seed11 training run, which is part of the broader "RaR-Medicine OnlineRubrics-Every" project. The model is provided with BF16 files optimized for inference.

Key Capabilities

  • Medical Domain Focus: The model's naming convention ("RaR-Medicine OnlineRubrics") strongly suggests a specialization in medical or healthcare-related natural language processing tasks.
  • Dense Checkpoint: This is a dense checkpoint, indicating a fully trained state from a specific step in the training process.
  • BF16 Inference: Includes BF16 model files, which are suitable for efficient inference on compatible hardware.
  • Research Use: Explicitly designated for research purposes, indicating it may be an experimental or developmental version.

Good for

  • Medical NLP Research: Ideal for researchers exploring language understanding and generation within the medical domain.
  • Evaluating Online Rubrics: Potentially useful for tasks involving the analysis or generation of online rubrics, especially in a medical context.
  • Experimentation with Qwen3-based Models: Provides a specific checkpoint for those interested in the performance and characteristics of Qwen3-based models fine-tuned for specialized tasks.
  • Understanding Training Progress: Useful for analyzing the state of a model at a particular step (step 6) within a long training run.

Finetunes
1 models