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Distilgpt2 HC3 by pszemraj

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Distilgpt2 HC3 is an open-source language model by pszemraj. Features: 88.2m LLM, VRAM: 0.3GB, License: apache-2.0, HF Score: 28.2, LLM Explorer Score: 0.11, Arc: 24.7, HellaSwag: 28, MMLU: 24, TruthfulQA: 42.1, WinoGrande: 50.4.

  Chatgpt Dataset:pszemraj/hc3-textgen-q...   En   Endpoints compatible   Generated from trainer   Gpt2   Hc3   Pytorch   Region:us   Safetensors
Model Card on HF 🤗: https://huggingface.co/pszemraj/distilgpt2-HC3 

Distilgpt2 HC3 Benchmarks

ARC: 24.66 vs 96.7 (so35)-74.5%
HellaSwag: 27.99 vs 95.3 (gpt4)-70.6%
MMLU: 23.95 vs 88.3 (so35)-72.9%
TruthfulQA: 42.1 vs 59 (gpt4)-28.6%
WinoGrande: 50.36 vs 87.5 (gpt4)-42.4%
LLME Score: 0.11389
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Distilgpt2 HC3 (pszemraj/distilgpt2-HC3)
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Distilgpt2 HC3 Parameters and Internals

Model Type 
text-generation
Use Cases 
Limitations:
This model only has 80M parameters and will likely not be factually accurate most of the time.
Additional Notes 
The model is a fine-tuned version of distilgpt2 on the 'chatgpt answers' column of the `Hello-SimpleAI/HC3` dataset. It achieved a loss of 1.9983 and accuracy of 0.5441 on the evaluation set.
Training Details 
Data Sources:
pszemraj/HC3-textgen-qa
LLM NameDistilgpt2 HC3
Repository 🤗https://huggingface.co/pszemraj/distilgpt2-HC3 
Model Size88.2m
Required VRAM0.3 GB
Updated2025-12-29
Maintainerpszemraj
Model Typegpt2
Model Files  0.3 GB   0.3 GB   0.0 GB
Supported Languagesen
Model ArchitectureGPT2LMHeadModel
Licenseapache-2.0
Model Max Length1024
Transformers Version4.27.0.dev0
Tokenizer ClassGPT2Tokenizer
Vocabulary Size50257
Torch Data Typefloat32
Activation Functiongelu_new

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Note: green Score (e.g. "73.2") means that the model is better than pszemraj/distilgpt2-HC3.

Rank the Distilgpt2 HC3 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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