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Gpt2 by openai-community

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Gpt2 is an open-source language model by openai-community. Features: 137m LLM, VRAM: 0.5GB, License: mit, HF Score: 28.5, LLM Explorer Score: 0.28, Arc: 22, HellaSwag: 31.5, MMLU: 25.8, TruthfulQA: 40.7, WinoGrande: 50.4, GSM8K: 0.7.

  Deploy:azure   Doi:10.57967/hf/0039   En   Endpoints compatible   Exbert   Gpt2   Jax   Onnx   Pytorch   Region:us   Rust   Safetensors   Tf   Tflite
Model Card on HF 🤗: https://huggingface.co/openai-community/gpt2 

Gpt2 Benchmarks

MMLU Pro: 1.84
GPQA: 1.12
MUSR: 13.91
BBH: 2.82
IFEval: 17.8 vs 88 (so35)-79.8%
ARC: 22.01 vs 96.7 (so35)-77.2%
HellaSwag: 31.53 vs 95.3 (gpt4)-66.9%
MMLU: 25.83 vs 88.3 (so35)-70.7%
TruthfulQA: 40.69 vs 59 (gpt4)-31%
WinoGrande: 50.43 vs 87.5 (gpt4)-42.4%
GSM8K: 0.68 vs 96.4 (so35)-99.3%
MATH Lvl 5: 0.53
LLME Score: 0.28183
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Gpt2 (openai-community/gpt2)
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Gpt2 Parameters and Internals

Model Type 
transformers, language model, causal language modeling
Use Cases 
Areas:
research, text generation
Applications:
text generation, language modeling
Primary Use Cases:
generating texts from prompts
Limitations:
Cannot distinguish fact from fiction, Potential bias in outputs
Considerations:
Ensure deployment readiness with an understanding of biases.
Supported Languages 
English (high)
Training Details 
Data Sources:
Reddit outbound links with 3+ karma
Data Volume:
Over 40 GB (WebText dataset)
Methodology:
Self-supervised training with causal language modeling
Context Length:
1024
Hardware Used:
256 TPU v3 cores
Model Architecture:
Transformers architecture with 50,257-token vocabulary
Safety Evaluation 
Ethical Considerations:
Includes biases inherent to training data; caution advised for sensitive use-cases.
Responsible Ai Considerations 
Fairness:
Model reflects biases present in training data; conduct studies on bias in intended use cases.
Transparency:
OpenAI released a model card highlighting limitations and ethical considerations.
Accountability:
Deployers are responsible for usage and bias evaluation.
Mitigation Strategies:
Approach deployment with caution in bias-sensitive applications; consider fine-tuning carefully.
Input Output 
Input Format:
Continuous text sequences
Accepted Modalities:
text
Output Format:
Generated text
LLM NameGpt2
Repository 🤗https://huggingface.co/openai-community/gpt2 
Model Size137m
Required VRAM0.5 GB
Updated2026-03-15
Maintaineropenai-community
Model Typegpt2
Model Files  0.5 GB   0.5 GB
Supported Languagesen
Model ArchitectureGPT2LMHeadModel
Licensemit
Model Max Length1024
Vocabulary Size50257
Activation Functiongelu_new

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Rank the Gpt2 Capabilities

🆘 Have you tried this model? Rate its performance. This feedback would greatly assist ML community in identifying the most suitable model for their needs. Your contribution really does make a difference! 🌟

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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