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Pythia 70M by EleutherAI

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  Arxiv:2101.00027   Arxiv:2201.07311   Arxiv:2304.01373   Dataset:eleutherai/pile   En   Gpt-neox   Gpt neox   Pythia   Pytorch   Region:us   Safetensors
Model Card on HF 🤗: https://huggingface.co/EleutherAI/pythia-70m 

Pythia 70M Benchmarks

ARC: 21.59 vs 96.7 (so35)-77.7%
HellaSwag: 27.29 vs 95.3 (gpt4)-71.4%
MMLU: 25.9 vs 88.3 (so35)-70.7%
TruthfulQA: 47.06 vs 59 (gpt4)-20.2%
WinoGrande: 51.46 vs 87.5 (gpt4)-41.2%
GSM8K: 0.3 vs 96.4 (so35)-99.7%
LLME Score: 0.20232
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
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Pythia 70M Parameters and Internals

Model Type 
Transformer-based Language Model, Causal Language Model
Use Cases 
Areas:
Research
Primary Use Cases:
Research on large language model behavior and interpretability
Limitations:
Not suitable for deployment or human-facing interactions, Limited to English language only
Additional Notes 
Suit designed to promote large language model research.
Supported Languages 
English (Fluent)
Training Details 
Data Sources:
The Pile
Data Volume:
299,892,736,000 tokens
Methodology:
Uniform batch size, Flash Attention, checkpoints at steps {0,1,2,4...143000}.
Model Architecture:
Transformer
Input Output 
Input Format:
Text input
Accepted Modalities:
text
Output Format:
Text output
Release Notes 
Version:
Current
Notes:
Included checkpoints, uniform batch size, Flash Attention.
LLM NamePythia 70M
Repository 🤗https://huggingface.co/EleutherAI/pythia-70m 
Model Size70m
Required VRAM0.2 GB
Updated2025-06-09
MaintainerEleutherAI
Model Typegpt_neox
Model Files  0.2 GB   0.2 GB
Supported Languagesen
Model ArchitectureGPTNeoXForCausalLM
Licenseapache-2.0
Context Length2048
Model Max Length2048
Transformers Version4.24.0
Tokenizer ClassGPTNeoXTokenizer
Vocabulary Size50304
Torch Data Typefloat16
Pythia 70M (EleutherAI/pythia-70m)

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Note: green Score (e.g. "73.2") means that the model is better than EleutherAI/pythia-70m.

Rank the Pythia 70M 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 v20241124
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