RRepoGEO

REPOGEO REPORT · LITE

bigscience-workshop/Megatron-DeepSpeed

Default branch main · commit 8387ae17 · scanned 5/9/2026, 7:12:48 PM

GitHub: 1,440 stars · 226 forks

AI VISIBILITY SCORE
28 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
2 / 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 bigscience-workshop/Megatron-DeepSpeed, 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
    Reposition the README H1 to state the repo's purpose for BigScience

    Why:

    CURRENT
    # What is this fork of Megatron-LM and Megatron-DeepSpeed
    COPY-PASTE FIX
    # bigscience-workshop/Megatron-DeepSpeed: The BigScience Project's Framework for Large-Scale Transformer Training
  • hightopics#2
    Add relevant topics to improve categorization

    Why:

    COPY-PASTE FIX
    large-language-models, llm-training, distributed-training, deepspeed, megatron-lm, transformer-models, bigscience, pytorch
  • mediumreadme#3
    Clarify the repository's license in the README

    Why:

    COPY-PASTE FIX
    Add a new section to the README: `## License This repository is a fork used for the BigScience project and incorporates code from NVIDIA/Megatron-LM and microsoft/Megatron-DeepSpeed. Please refer to the LICENSE file for specific licensing details, as it may include components under various licenses.`

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 bigscience-workshop/Megatron-DeepSpeed
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
PyTorch
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. PyTorch · recommended 1×
  2. PyTorch Lightning · recommended 1×
  3. DeepSpeed · recommended 1×
  4. Hugging Face Transformers · recommended 1×
  5. Hugging Face Accelerate · recommended 1×
  • CATEGORY QUERY
    What are the best frameworks for training massive transformer models across multiple GPUs?
    you: not recommended
    AI recommended (in order):
    1. PyTorch
    2. PyTorch Lightning
    3. DeepSpeed
    4. Hugging Face Transformers
    5. Hugging Face Accelerate
    6. TensorFlow
    7. Keras
    8. Horovod
    9. JAX
    10. Flax
    11. Haiku
    12. Megatron-LM

    AI recommended 12 alternatives but never named bigscience-workshop/Megatron-DeepSpeed. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a robust library for distributed deep learning to scale large language model development.
    you: not recommended
    AI recommended (in order):
    1. DeepSpeed (microsoft/DeepSpeed)
    2. PyTorch FSDP (pytorch/pytorch)
    3. Megatron-LM (NVIDIA/Megatron-LM)
    4. Hugging Face Accelerate (huggingface/accelerate)
    5. Ray Train (ray-project/ray)

    AI recommended 5 alternatives but never named bigscience-workshop/Megatron-DeepSpeed. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    warn

    Suggestion:

  • 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 bigscience-workshop/Megatron-DeepSpeed?
    pass
    AI did not name bigscience-workshop/Megatron-DeepSpeed — likely talking about a different project

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

  • If a team adopts bigscience-workshop/Megatron-DeepSpeed in production, what risks or prerequisites should they evaluate first?
    pass
    AI named bigscience-workshop/Megatron-DeepSpeed 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 bigscience-workshop/Megatron-DeepSpeed solve, and who is the primary audience?
    pass
    AI named bigscience-workshop/Megatron-DeepSpeed explicitly

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

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bigscience-workshop/Megatron-DeepSpeed — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite