RRepoGEO

REPOGEO REPORT · LITE

EleutherAI/gpt-neox

Default branch main · commit ea7aefd8 · scanned 5/24/2026, 7:51:49 AM

GitHub: 7,430 stars · 1,114 forks

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface EleutherAI/gpt-neox, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Strengthen README's opening to explicitly position GPT-NeoX as a leading framework in its category

    Why:

    CURRENT
    This repository records EleutherAI's library for training large-scale language models on GPUs. Our current framework is based on NVIDIA's Megatron Language Model and has been augmented with techniques from DeepSpeed as well as some novel optimizations.
    COPY-PASTE FIX
    GPT-NeoX is EleutherAI's leading open-source framework for training large-scale autoregressive language models on GPUs, directly competing with and building upon techniques from NVIDIA's Megatron Language Model and DeepSpeed. It is specifically designed for distributed training of billion-parameter models, offering novel optimizations and increased usability compared to similar libraries.
  • hightopics#2
    Add more specific topics to clearly categorize the repository

    Why:

    CURRENT
    deepspeed-library, gpt-3, language-model, transformers
    COPY-PASTE FIX
    deepspeed-library, gpt-3, language-model, transformers, distributed-training, model-parallelism, large-language-models, llm-training-framework, gpu-accelerated
  • mediumreadme#3
    Add a dedicated, concise comparison to Megatron-DeepSpeed in the README

    Why:

    COPY-PASTE FIX
    Add a new section or integrate a paragraph into 'Why GPT-NeoX?' that explicitly outlines how GPT-NeoX differs in usability, system support, or novel optimizations from Megatron-DeepSpeed. For example: 'While sharing many features with Megatron-DeepSpeed, GPT-NeoX offers substantially increased usability, broader hardware and system support (including Slurm, MPI, IBM Job Step Manager, AWS, ORNL Summit, LUMI), and novel optimizations for large-scale autoregressive model training.'

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface EleutherAI/gpt-neox
Avg rank
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
DeepSpeed
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. DeepSpeed · recommended 1×
  2. Megatron-LM · recommended 1×
  3. FairScale · recommended 1×
  4. PyTorch FSDP · recommended 1×
  5. Accelerate · recommended 1×
  • CATEGORY QUERY
    What frameworks facilitate training billion-parameter autoregressive language models efficiently on GPUs?
    you: not recommended
    AI recommended (in order):
    1. DeepSpeed
    2. Megatron-LM
    3. FairScale
    4. PyTorch FSDP
    5. Accelerate
    6. JAX/Flax

    AI recommended 6 alternatives but never named EleutherAI/gpt-neox. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a library for distributed training of large transformer models using model parallelism.
    you: not recommended
    AI recommended (in order):
    1. DeepSpeed (microsoft/DeepSpeed)
    2. Megatron-LM (NVIDIA/Megatron-LM)
    3. FairScale (facebookresearch/fairscale)
    4. Colossal-AI (hpcaitech/ColossalAI)
    5. PyTorch FSDP (pytorch/pytorch)

    AI recommended 5 alternatives but never named EleutherAI/gpt-neox. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of EleutherAI/gpt-neox?
    pass
    AI named EleutherAI/gpt-neox explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts EleutherAI/gpt-neox in production, what risks or prerequisites should they evaluate first?
    pass
    AI named EleutherAI/gpt-neox explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo EleutherAI/gpt-neox solve, and who is the primary audience?
    pass
    AI named EleutherAI/gpt-neox explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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  • Brand-free category queries5 vs 2 in Lite
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