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Gpt2 Large by openai-community

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Model Card on HF 🤗: https://huggingface.co/openai-community/gpt2-large 

Gpt2 Large Benchmarks

MMLU Pro: 1.58
GPQA: 1.23
MUSR: 5.66
BBH: 3.25
IFEval: 20.48 vs 88 (so35)-76.7%
ARC: 25.77 vs 96.7 (so35)-73.4%
HellaSwag: 45.62 vs 95.3 (gpt4)-52.1%
MMLU: 26.07 vs 88.3 (so35)-70.5%
TruthfulQA: 38.72 vs 59 (gpt4)-34.4%
WinoGrande: 55.41 vs 87.5 (gpt4)-36.7%
GSM8K: 0.83 vs 96.4 (so35)-99.1%
MATH Lvl 5: 1.21
LLME Score: 0.17116
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Gpt2 Large (openai-community/gpt2-large)

Gpt2 Large Parameters and Internals

Model Type 
Transformer-based language model
Use Cases 
Areas:
Research, AI development
Applications:
Text generation, Writing assistance, Creative writing, Entertainment
Primary Use Cases:
AI research and understanding generative language models
Limitations:
Certain sensitive and bias-prone applications
Considerations:
Awareness of biases and reliability in factual text generation.
Additional Notes 
The model card highlights that significant caution is to be taken with content bias and ethical deployment considerations.
Supported Languages 
English (high)
Training Details 
Data Sources:
Reddit outbound links
Data Volume:
40GB
Methodology:
Pretrained on a large corpus of English data in a self-supervised manner (causal language modeling - CLM)
Context Length:
1024
Model Architecture:
Transformer architecture
Safety Evaluation 
Methodologies:
Zero-shot and context-aware evaluations
Findings:
Contains biases inherent to training data
Risk Categories:
Bias in generated predictions
Ethical Considerations:
Bias awareness and risk evaluations for sensitive human attributes
Responsible Ai Considerations 
Fairness:
Potential biases in generation across protected classes and groups.
Transparency:
Model's behavior and biases are noted but not fully transparent given training data sources.
Accountability:
OpenAI is accountable for model and its releases.
Mitigation Strategies:
Bias analyses and user awareness recommendations.
Input Output 
Input Format:
PyTorch and TensorFlow pipelines
Accepted Modalities:
text
Output Format:
Text-based generations
Performance Tips:
Potential bias and misuse caution advised.
LLM NameGpt2 Large
Repository 🤗https://huggingface.co/openai-community/gpt2-large 
Model Size811.8m
Required VRAM3.2 GB
Updated2025-02-22
Maintaineropenai-community
Model Typegpt2
Model Files  3.2 GB   3.2 GB
Supported Languagesen
Model ArchitectureGPT2LMHeadModel
Licensemit
Model Max Length1024
Vocabulary Size50257
Activation Functiongelu_new

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Instruction Following and Task Automation  
Factuality and Completeness of Knowledge  
Censorship and Alignment  
Data Analysis and Insight Generation  
Text Generation  
Text Summarization and Feature Extraction  
Code Generation  
Multi-Language Support and Translation  

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Release v20241227
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