Phi-3-vision-128k-instruct

17.2K
969
1 language
license:mit
by
microsoft
Language Model
OTHER
Fair
17K downloads
Community-tested
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Mobile
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Quick Summary

🎉 Phi-3.5: [[mini-instruct]](https://huggingface.co/microsoft/Phi-3.5-mini-instruct); [[MoE-instruct]](https://huggingface.co/microsoft/Phi-3.5-MoE-instruct) ;...

Training Data Analysis

🟡 Average (5.2/10)

Researched training datasets used by Phi-3-vision-128k-instruct with quality assessment

Specialized For

code
general
science
multilingual

Training Datasets (3)

the pile
🟢 8/10
code
general
science
multilingual
Key Strengths
  • •Deliberate Diversity: Explicitly curated to include diverse content types (academia, code, Q&A, book...
  • •Documented Quality: Each component dataset is thoroughly documented with rationale for inclusion, en...
  • •Epoch Weighting: Component datasets receive different training epochs based on perceived quality, al...
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 ...

Explore our comprehensive training dataset analysis

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Code Examples

text
flash_attn==2.5.8
numpy==1.24.4
Pillow==10.3.0
Requests==2.31.0
torch==2.3.0
torchvision==0.18.0
transformers==4.40.2

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