Llama 3.2 1B Instruct
by
meta-llama
This model is designed for text generation and supports multiple languages including English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai. It utilizes the transformers library and is built on PyTorch. The model is tagged with keywords such as facebook, meta, pytorch, llama, and llama-3. It is licensed under llama3.2 and includes an extra gated prompt.
Language Model
PYTORCH
Quick Info
Released
9/18/2024Framework
PYTORCHResources
Training Data Analysis
🟡 Average (4.8/10)
Researched training datasets used by Llama 3.2 1B Instruct with quality assessment
Specialized For
general
science
multilingual
reasoning
Training Datasets (4)
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...
c4
🔵 6/10
general
multilingual
Key Strengths
- •Scale and Accessibility: 750GB of publicly available, filtered text
- •Systematic Filtering: Documented heuristics enable reproducibility
- •Language Diversity: Despite English-only, captures diverse writing styles
Considerations
- •English-Only: Limits multilingual applications
- •Filtering Limitations: Offensive content and low-quality text remain despite filtering
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
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