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Pythia 160M by EleutherAI

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Pythia 160M is an open-source language model by EleutherAI. Features: 160m LLM, VRAM: 0.4GB, Context: 2K, License: apache-2.0, HF Score: 29, LLM Explorer Score: 0.23, Arc: 22.8, HellaSwag: 30.3, MMLU: 25, TruthfulQA: 44.3, WinoGrande: 51.5, GSM8K: 0.2.

  Arxiv:2101.00027   Arxiv:2201.07311   Arxiv:2304.01373   Dataset:eleutherai/pile   Deploy:azure   En   Endpoints compatible   Gpt neox   Pythia   Pytorch   Region:us   Safetensors
Model Card on HF 🤗: https://huggingface.co/EleutherAI/pythia-160m 

Pythia 160M Benchmarks

MMLU Pro: 1.33
GPQA: 1.12
MUSR: 10.68
BBH: 2.2
IFEval: 18.16 vs 88 (so35)-79.4%
ARC: 22.78 vs 96.7 (so35)-76.4%
HellaSwag: 30.34 vs 95.3 (gpt4)-68.2%
MMLU: 24.95 vs 88.3 (so35)-71.7%
TruthfulQA: 44.26 vs 59 (gpt4)-25%
WinoGrande: 51.54 vs 87.5 (gpt4)-41.1%
GSM8K: 0.23 vs 96.4 (so35)-99.8%
MATH Lvl 5: 0.91
LLME Score: 0.23327
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Pythia 160M (EleutherAI/pythia-160m)
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Pythia 160M Parameters and Internals

Model Type 
Transformer-based Language Model
Use Cases 
Areas:
Scientific Research, Interpretability Research
Applications:
Research on behavior, functionality, limitations of large language models
Primary Use Cases:
Controlled scientific experiments
Limitations:
Not suitable for human-facing interactions, English language-only models, unsuitable for generating text in other languages, Not fine-tuned for genre prose or commercial chatbots
Considerations:
Conduct risk and bias assessment if fine-tuning; evaluate risks before deployment
Additional Notes 
Pythia model suite renamed in January 2023 for clarity
Supported Languages 
English (Native)
Training Details 
Data Sources:
The Pile, 22 diverse sources including arXiv, CommonCrawl, Project Gutenberg, YouTube subtitles, GitHub
Data Volume:
299,892,736,000 tokens
Model Architecture:
GPT-NeoX
Responsible Ai Considerations 
Fairness:
Documented biases with regards to gender, religion, and race (as per Pile paper).
Input Output 
Input Format:
String of text for next token prediction.
Accepted Modalities:
text
Output Format:
String (one token at a time)
Release Notes 
Version:
Current Release
Date:
January 2023
Notes:
Pyhtia-160M retrained to address hyperparameter discrepancies
LLM NamePythia 160M
Repository 🤗https://huggingface.co/EleutherAI/pythia-160m 
Model Size160m
Required VRAM0.4 GB
Updated2026-02-28
MaintainerEleutherAI
Model Typegpt_neox
Model Files  0.4 GB   0.4 GB
Supported Languagesen
Model ArchitectureGPTNeoXForCausalLM
Licenseapache-2.0
Context Length2048
Model Max Length2048
Transformers Version4.24.0
Tokenizer ClassGPTNeoXTokenizer
Vocabulary Size50304
Torch Data Typefloat16

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Rank the Pythia 160M 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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