EleutherAI
How do large language models (LLMs) develop and evolve over the course of training? How do these patterns change as models scale? To answer these questions, we introduce \textit{Pythia}, a suite of 16 LLMs all trained on public data seen in the exact same order and ranging in size from 70M to 12B parameters.
Running this yourself: can likely run on your own machine.
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Quality Score
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Arena ELO
506M
Parameters
2K
Context
This measures the amount of verifiable public evidence we have, not how capable the model is. A missing field means it has not been verified yet, not that its value is zero.
14 of 22 public signals
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201.5K
Downloads
40
Likes
Feb 2023
Released
5/5 signals
0/4 signals
3/5 signals
3/4 signals
3/4 signals
Parameters
410M
Training compute
7.4e20 FLOP
Dataset scale
299.9B units
Base model
Not reported
Source-reported access: Open weights (unrestricted) · Confident confidence
Gaps we are still tracking
This section gives the plain-English basics first: what the model is, how large it is, how much context it can handle, and whether it is still actively supported.
This tells you how you can use the model in practice: whether weights are open, whether an API exists, and what kinds of input or output it supports.
Capabilities