RRepoGEO

REPOGEO REPORT · LITE

bigscience-workshop/bigscience

Default branch master · commit 9e64edec · scanned 5/21/2026, 5:13:43 AM

GitHub: 1,015 stars · 101 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
35 /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
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 bigscience-workshop/bigscience, 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 README to clarify its role as a workshop resource hub

    Why:

    CURRENT
    Research workshop on large language models - The Summer of Language Models 21
At the moment we have 2 code repos: ... 2. bigscience (this repo) for everything else - docs, experiments, etc.
    COPY-PASTE FIX
    This repository serves as the central documentation and resource hub for the BigScience research workshop on large language models (The Summer of Language Models 21). It contains essential information, SLURM scripts, experiment logs, and details about the compute environment and data used during the workshop, complementing our main code repository at bigscience-workshop/Megatron-DeepSpeed.
  • hightopics#2
    Add specific topics to accurately reflect the repo's content type

    Why:

    CURRENT
    machine-learning, models, nlp, training
    COPY-PASTE FIX
    llm-research-workshop, llm-documentation, slurm-scripts, experiment-logs, compute-infrastructure, large-language-models, nlp-training
  • mediumhomepage#3
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://bigscience.huggingface.co/

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/bigscience
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Google Cloud Vertex AI
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Google Cloud Vertex AI · recommended 2×
  2. Azure Machine Learning · recommended 2×
  3. mlflow/mlflow · recommended 1×
  4. wandb/wandb · recommended 1×
  5. comet-ml/comet-python-sdk · recommended 1×
  • CATEGORY QUERY
    What are effective strategies for managing large-scale language model training experiments and data?
    you: not recommended
    AI recommended (in order):
    1. MLflow (mlflow/mlflow)
    2. Weights & Biases (W&B) (wandb/wandb)
    3. Comet ML (comet-ml/comet-python-sdk)
    4. DVC (Data Version Control) (iterative/dvc)
    5. LakeFS (treeverse/lakeFS)
    6. Pachyderm (pachyderm/pachyderm)
    7. Kubeflow Pipelines (kubeflow/pipelines)
    8. Apache Airflow (apache/airflow)
    9. Metaflow (Netflix/metaflow)
    10. Google Cloud Vertex AI
    11. Amazon SageMaker
    12. Azure Machine Learning

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

    Show full AI answer
  • CATEGORY QUERY
    How to set up compute environments and infrastructure for training massive NLP models efficiently?
    you: not recommended
    AI recommended (in order):
    1. AWS SageMaker
    2. DeepSpeed (microsoft/DeepSpeed)
    3. Megatron-LM (NVIDIA/Megatron-LM)
    4. Google Cloud Vertex AI
    5. JAX/Flax
    6. PyTorch FSDP
    7. Azure Machine Learning
    8. FairScale (facebookresearch/fairscale)
    9. Slurm (SchedMD/slurm)
    10. Kubernetes (kubernetes/kubernetes)
    11. RunPod.io
    12. Vast.ai
    13. Lambda Labs Cloud

    AI recommended 13 alternatives but never named bigscience-workshop/bigscience. 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/bigscience?
    pass
    AI named bigscience-workshop/bigscience explicitly

    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/bigscience in production, what risks or prerequisites should they evaluate first?
    pass
    AI named bigscience-workshop/bigscience 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/bigscience solve, and who is the primary audience?
    pass
    AI named bigscience-workshop/bigscience explicitly

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

Embed your GEO score

Drop this badge into the README of bigscience-workshop/bigscience. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/bigscience-workshop/bigscience.svg)](https://repogeo.com/en/r/bigscience-workshop/bigscience)
HTML
<a href="https://repogeo.com/en/r/bigscience-workshop/bigscience"><img src="https://repogeo.com/badge/bigscience-workshop/bigscience.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

bigscience-workshop/bigscience — 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