Gpt2023 by crumb

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  Autotrain compatible   En   Endpoints compatible   Gpt2   Pytorch   Region:us   Safetensors
Model Card on HF ๐Ÿค—: https://huggingface.co/crumb/gpt2023 

Gpt2023 Benchmarks

Gpt2023 (crumb/gpt2023)

Gpt2023 Parameters and Internals

Model Type 
causal-lm
Use Cases 
Areas:
Research
Limitations:
Lack of awareness of some recent events due to finetuning on a limited dataset
Supported Languages 
en (Fluent)
Training Details 
Data Sources:
common crawl sites, ArXiv, GitHub
Data Volume:
2.23 billion tokens
Methodology:
Finetuning on existing GPT-2 model with learning rate adjustments
Context Length:
1024
Training Time:
79.32 hours
Hardware Used:
12GB RTX3060
Model Architecture:
Transformer-based architecture, left-to-right causal language model
Input Output 
Input Format:
Text input, up to 1024 tokens
Accepted Modalities:
text
Output Format:
Text generation
Performance Tips:
Setting a seed can help achieve reproducible results
LLM NameGpt2023
Repository ๐Ÿค—https://huggingface.co/crumb/gpt2023 
Model Size137m
Required VRAM0.3 GB
Updated2025-02-05
Maintainercrumb
Model Typegpt2
Model Files  0.3 GB   0.3 GB
Supported Languagesen
Model ArchitectureGPT2LMHeadModel
Licensemit
Model Max Length1024
Transformers Version4.29.0.dev0
Tokenizer ClassGPT2Tokenizer
Vocabulary Size50257
Torch Data Typebfloat16
Activation Functiongelu_new

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Note: green Score (e.g. "73.2") means that the model is better than crumb/gpt2023.

Rank the Gpt2023 Capabilities

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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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Original data from HuggingFace, OpenCompass and various public git repos.
Release v20241227