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Math Gpt2 by Sharathhebbar24

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Math Gpt2 is an open-source language model by Sharathhebbar24. Features: LLM, VRAM: 0.5GB, License: apache-2.0, Instruction-Based, LLM Explorer Score: 0.17, Arc: 24.2, HellaSwag: 30.9, MMLU: 25.4, GSM8K: 0.2.

  Arxiv-math Dataset:artifactai/arxiv-math-...   En   Endpoints compatible   Gpt2   Instruct   Mathgpt2   Maths   Pytorch   Region:us
Model Card on HF 🤗: https://huggingface.co/Sharathhebbar24/math_gpt2 

Math Gpt2 Benchmarks

ARC: 24.23 vs 96.7 (so35)-74.9%
HellaSwag: 30.88 vs 95.3 (gpt4)-67.6%
MMLU: 25.38 vs 88.3 (so35)-71.3%
GSM8K: 0.23 vs 96.4 (so35)-99.8%
LLME Score: 0.16899
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Math Gpt2 (Sharathhebbar24/math_gpt2)
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Math Gpt2 Parameters and Internals

Model Type 
text-generation
Use Cases 
Areas:
maths, arxiv-math
Primary Use Cases:
Text generation based on mathematical prompts.
Additional Notes 
The model is fine-tuned specifically for math-related instructions using the arxiv-math dataset.
Supported Languages 
en (fluent)
Training Details 
Data Sources:
ArtifactAI/arxiv-math-instruct-50k
Methodology:
Finetuned version of GPT-2 with math instructions.
Model Architecture:
Transformers
LLM NameMath Gpt2
Repository 🤗https://huggingface.co/Sharathhebbar24/math_gpt2 
Required VRAM0.5 GB
Updated2026-05-22
MaintainerSharathhebbar24
Model Typegpt2
Instruction-BasedYes
Model Files  0.5 GB
Supported Languagesen
Model ArchitectureGPT2LMHeadModel
Licenseapache-2.0
Model Max Length1024
Transformers Version4.30.0.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 Sharathhebbar24/math_gpt2.

Rank the Math Gpt2 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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