FreedomIntelligence/HuatuoGPT-3-Grader-8B

TEXT GENERATIONPricing:Input $0.468 / Output $1.82Concurrent Unit Cost:1Model Size:8BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Sep 17, 2026Architecture:Transformer0.0K Featherless Exclusive Cold

HuatuoGPT-3-Grader-8B by FreedomIntelligence is an 8 billion parameter medical rubric grader, specifically designed for the OnePO framework. Trained on GPT-4.1-annotated samples, this model evaluates open-ended medical responses against multiple criteria in a single generation. It provides structured feedback as a JSON list of booleans, indicating whether each rubric item is met, making it ideal for automated assessment and reward calculation in medical dialogue systems.

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Overview

FreedomIntelligence's HuatuoGPT-3-Grader-8B is an 8 billion parameter language model specialized in grading open-ended medical responses. It functions as a medical rubric grader within the OnePO framework, providing automated assessment based on predefined criteria.

Key Capabilities

  • Automated Rubric Grading: Evaluates assistant responses against a set of numbered criteria, each with signed points.
  • JSON Output: Returns a simple JSON list of booleans, where true indicates a criterion is met (or a negative behavior is present for negative-point rubrics).
  • GPT-4.1 Annotated Training: The model is trained on samples annotated by GPT-4.1, ensuring high-quality grading logic.
  • OnePO Integration: Designed to provide rewards for the OnePO policy optimization, calculating scores based on matched points.
  • Batch Scoring: Supports efficient batch processing of multiple conversations and rubrics for large-scale evaluation.

Usage and Application

Developers can use a specific grading template to format conversations and rubrics, which the model then processes to generate scores. This model is particularly useful for:

  • Automated Medical Dialogue Evaluation: Assessing the quality and correctness of AI-generated medical advice or responses.
  • Reinforcement Learning from Human Feedback (RLHF): Providing structured rewards for training medical LLMs within frameworks like OnePO.
  • Quality Assurance: Automatically checking if medical AI outputs adhere to specific guidelines or avoid undesirable behaviors.