PopiT-Gemma-3-12B-Medical-SFT-1213

37
—
by
Hoshino-Yumetsuki
Other
OTHER
12B params
New
37 downloads
Early-stage
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Edge AI:
Mobile
Laptop
Server
27GB+ RAM
Mobile
Laptop
Server
Quick Summary

AI model with specialized capabilities.

Device Compatibility

Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
12GB+ RAM

Training Data Analysis

🟡 Average (4.3/10)

Researched training datasets used by PopiT-Gemma-3-12B-Medical-SFT-1213 with quality assessment

Specialized For

general
science
multilingual
reasoning

Training Datasets (3)

common crawl
🔴 2.5/10
general
science
Key Strengths
  • •Scale and Accessibility: At 9.5+ petabytes, Common Crawl provides unprecedented scale for training d...
  • •Diversity: The dataset captures billions of web pages across multiple domains and content types, ena...
  • •Comprehensive Coverage: Despite limitations, Common Crawl attempts to represent the broader web acro...
Considerations
  • •Biased Coverage: The crawling process prioritizes frequently linked domains, making content from dig...
  • •Large-Scale Problematic Content: Contains significant amounts of hate speech, pornography, violent c...
wikipedia
🟡 5/10
science
multilingual
Key Strengths
  • •High-Quality Content: Wikipedia articles are subject to community review, fact-checking, and citatio...
  • •Multilingual Coverage: Available in 300+ languages, enabling training of models that understand and ...
  • •Structured Knowledge: Articles follow consistent formatting with clear sections, allowing models to ...
Considerations
  • •Language Inequality: Low-resource language editions have significantly lower quality, fewer articles...
  • •Biased Coverage: Reflects biases in contributor demographics; topics related to Western culture and ...
arxiv
🟡 5.5/10
science
reasoning
Key Strengths
  • •Scientific Authority: Peer-reviewed content from established repository
  • •Domain-Specific: Specialized vocabulary and concepts
  • •Mathematical Content: Includes complex equations and notation
Considerations
  • •Specialized: Primarily technical and mathematical content
  • •English-Heavy: Predominantly English-language papers

Explore our comprehensive training dataset analysis

View All Datasets

Code Examples

text
@misc{wang2023cmb,
      title={CMB: A Comprehensive Medical Benchmark in Chinese}, 
      author={Xidong Wang and Guiming Hardy Chen and Dingjie Song and Zhiyi Zhang and Zhihong Chen and Qingying Xiao and Feng Jiang and Jianquan Li and Xiang Wan and Benyou Wang and Haizhou Li},
      year={2023},
      eprint={2308.08833},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
text
@misc{cmedbenchmark,
  title={CMB: Chinese Medical Benchmark},
  author={Xidong Wang*, Guiming Hardy Chen*, Dingjie Song*, Zhiyi Zhang*, Qingying Xiao*, Xiangbo Wu, Feng Jiang, Jianquan Li, Benyou Wang},
  note={Authors Xidong Wang, Guiming Hardy Chen, Dingjie Song, Zhiyi Zhang and Qingying Xiao contributed equally to this work.},
  year = {2023},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/FreedomIntelligence/CMB}},
}

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