Methods › Natural Language Processing › Language Models › Pythia

Pythia

60 papers tagged archive 2025-07-28

Introduced by Stella Biderman et al. in Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

Pythia is a suite of decoder-only autoregressive language models all trained on public data seen in the exact same order and ranging in size from 70M to 12B parameters. The model architecture and hyperparameters largely follow GPT-3, with a few notable deviations based on recent advances in best practices for large scale language modeling.

PaperSource

Papers archive 2025-07-28

30 shown of 60, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

20 shown of 58 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Language Modelling23
Language Modeling15
Memorization10
Large Language Model5
Question Answering4
Text Generation3
Articles2
Common Sense Reasoning2
Computational Efficiency2
Hallucination2
In-Context Learning2
Knowledge Distillation2
LAMBADA2
Machine Translation2
Math2
Sentence Completion2
World Knowledge2
counterfactual2
model2
Adversarial Attack1

Usage over time archive 2025-07-28

Papers per year tagged with Pythia: 2023 to 2025, peak 31 31 0 2023: 17 papers 2023 2024: 31 papers 2024 2025: 12 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (60 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Language Models

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