gemma-4-E2B-it-mxfp4-mlx

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1
license:apache-2.0
by
nightmedia
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OTHER
2B params
New
2K downloads
Early-stage
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Edge AI:
Mobile
Laptop
Server
5GB+ RAM
Mobile
Laptop
Server
Quick Summary

AI model with specialized capabilities.

Device Compatibility

Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
2GB+ RAM

Training Data Analysis

🟡 Average (4.3/10)

Researched training datasets used by gemma-4-E2B-it-mxfp4-mlx 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

gemma-4-E2B-it-mxfp4-mlxbrainwaves
arc   arc/e boolq hswag obkqa piqa  wino
bf16     0.389,0.465,0.762,0.486,0.372,0.707,0.641
mxfp8    0.376,0.464,0.743,0.490,0.378,0.709,0.622
q8-hi    0.392,0.462,0.762,0.487,0.376,0.706,0.636
qx86-hi  0.387,0.461,0.766,0.483,0.392,0.699,0.623
mxfp4    0.380,0.451,0.762,0.494,0.374,0.699,0.594

Perplexity               Peak Memory   Tokens/sec
mxfp8    170.519 ± 3.170  11.78 GB      2174
q8-hi    133.388 ± 2.383  11.21 GB      1889
qx86-hi  125.278 ± 2.215  11.87 GB      1856
mxfp4    140.693 ± 2.546   9.48 GB      2352

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