Methods › Natural Language Processing › Language Models › GPT-NeoX

GPT-NeoX

11 papers tagged archive 2025-07-28

Introduced by Sid Black et al. in GPT-NeoX-20B: An Open-Source Autoregressive Language Model

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

GPT-NeoX is an autoregressive transformer decoder model whose architecture largely follows that of GPT-3, with a few notable deviations. The model has 20 billion parameters with 44 layers, a hidden dimension size of 6144, and 64 heads. The main difference with GPT-3 is the change in tokenizer, the addition of Rotary Positional Embeddings, the parallel computation of attention and feed-forward layers, and a different initialization scheme and hyperparameters.

PaperSource

Papers archive 2025-07-28

11 shown of 11, 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

14 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 Modelling3
GPU2
Language Modeling2
Position2
4k1
Articles1
Attribute1
Dataset Generation1
Hallucination1
Linguistic Acceptability1
Multi-task Language Understanding1
Quantization1
Text Detection1
Text Generation1

Usage over time archive 2025-07-28

Papers per year tagged with GPT-NeoX: 2022 to 2024, peak 8 8 0 2022: 2 papers 2022 2023: 8 papers 2023 2024: 1 paper 2024
Papers per year the archive tags with this method, by the paper's archive date (11 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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