Gemma-3-R1984-27B / README.md
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A newer version of the Gradio SDK is available: 5.33.1

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metadata
title: Gemma-3-R1984-27B ChatBot
emoji: πŸ”₯
colorFrom: blue
colorTo: yellow
sdk: gradio
sdk_version: 5.33.0
app_file: app.py
pinned: false
short_description: Reasoning + Deep Research + API(NVIDIA H100 GPU)
models:
  - VIDraft/Gemma-3-R1984-27B
  - VIDraft/Gemma-3-R1984-12B
  - VIDraft/Gemma-3-R1984-4B

FACTS Grounding Leaderboard - Medical AI Evaluation

πŸ₯ Overview FACTS Grounding is an AI reliability evaluation system developed by Google DeepMind that verifies whether AI responses are grounded solely in provided documents. This evaluation is particularly crucial in healthcare, where inaccurate information can be life-threatening. 🎯 Key Features Evaluation Methodology

Long Medical Document Input (~32,000 tokens β‰ˆ 40 A4 pages) AI Response Generation Based on Documents Dual-Criteria Assessment

βœ… Quality Check: Does the AI accurately understand the question? βœ… Grounding Check: Are all responses based on the provided documents?

Medical-Focused Version

236 medical cases selected from 860 total problems Strict evaluation criteria reflecting healthcare field requirements

πŸ† Current Leaderboard Rankings (June 5, 2025) Overall Score TOP 5(https://huggingface.co/spaces/MaziyarPanahi/FACTS-Leaderboard)

  1. deepseek-ai/DeepSeek-R1-Distill-Qwen-14B
  2. VIDraft/Gemma-3-R1984-27B
  3. meta-llama/Llama-3.3-70B-Instruct
  4. Qwen/Qwen3-30B-A3B
  5. Qwen/Qwen3-4B

πŸ’‘ Why FACTS Matters for Medical AI

  1. Patient Safety Assurance

General AI Error: "The capital of France is London" β†’ Simple mistake Medical AI Error: "This medication is safe for pregnant women" β†’ Life-threatening!

  1. Building Healthcare Provider Trust

Ensures all responses are grounded in medical literature Transparent scoring enables AI system reliability verification

  1. Regulatory Compliance & Standardization

Emerging as a global medical AI standard Potential reference for FDA, CE, and other regulatory approvals

🌍 Advancing Global Medical AI Core Values of FACTS

Accuracy: Provides only evidence-based medical responses Transparency: All evaluation processes and data are public Accessibility: Global participation in evaluation Practicality: Reflects real-world healthcare scenarios

Medical-Specific Features

Understanding complex medical terminology and drug interactions Recognition of diverse symptom descriptions Verification of clinical guideline and protocol adherence Consideration of medical ethics and patient privacy

πŸš€ The Future of Medical AI FACTS Grounding is establishing itself as the 'quality certification system' for medical AI. Expected Impact

Global Standardization: Unified criteria for worldwide medical AI evaluation Quality Improvement: Continuous model enhancement through benchmarking Accelerated Clinical Adoption: Rapid deployment of validated AI systems Enhanced Patient Care: More accurate and safer healthcare services

🀝 How to Participate The leaderboard operates with complete transparency, allowing anyone to submit and evaluate their models.

Download the FACTS Grounding dataset Train and optimize your model Submit evaluation results Check rankings on the leaderboard

πŸ“Š Utilizing Evaluation Results Healthcare Institutions

Objective performance metrics for AI adoption decisions Selection criteria among multiple AI systems

AI Developers

Model performance benchmarking and improvement direction Marketing and reliability verification materials

Regulatory Bodies

Reference material for AI medical device approval processes Supplementary indicator for safety assessments

πŸ“Œ Key Takeaways

FACTS Grounding = Global standard for verifying AI medical accuracy Medical-Specific Evaluation = Testing based on 236 real healthcare scenarios Transparent Operation = Open system accessible to all participants Real-World Impact = Promoting safer and more reliable medical AI development

"What cannot be measured cannot be improved" - FACTS Grounding objectively measures medical AI reliability, contributing to global healthcare AI advancement.

We look forward to more research teams and developers joining this challenge to create better AI for human health! 🌍