RRepoGEO

REPOGEO REPORT · LITE

stanford-crfm/mistral

Default branch main · commit d1fb88e2 · scanned 6/7/2026, 11:41:44 PM

GitHub: 580 stars · 51 forks

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 stanford-crfm/mistral, 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
  • hightopics#1
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    large-language-models, llm-training, distributed-training, huggingface-transformers, deep-learning, machine-learning, ai-research, model-evaluation, gcp
  • highreadme#2
    Clarify the README's opening to emphasize research and evaluation context

    Why:

    CURRENT
    A framework for transparent and accessible large-scale language model training, built with Hugging Face 🤗 . Includes tools and helpful scripts for incorporating new pre-training datasets, various schemes for single node and distributed training - including on cloud providers like GCP, and importantly, scripts for evaluation.
    COPY-PASTE FIX
    Mistral is a research framework from Stanford CRFM for transparent and accessible large-scale language model training, built with Hugging Face 🤗 Transformers. It provides tools and scripts for incorporating new pre-training datasets, various schemes for single-node and distributed training (including on cloud providers like GCP), and importantly, robust scripts for evaluation and analysis of LLMs like Mistral 7B.
  • mediumhomepage#3
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    Add the URL for the full documentation (e.g., the 'Read the Docs' link mentioned in the README) as the repository homepage.

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 stanford-crfm/mistral
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
tensorflow/tensorflow
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. tensorflow/tensorflow · recommended 2×
  2. ray-project/ray · recommended 2×
  3. Lightning-AI/pytorch-lightning · recommended 1×
  4. microsoft/DeepSpeed · recommended 1×
  5. pytorch/pytorch · recommended 1×
  • CATEGORY QUERY
    How can I efficiently train large language models using distributed computing on cloud platforms?
    you: not recommended
    AI recommended (in order):
    1. PyTorch Lightning (Lightning-AI/pytorch-lightning)
    2. DeepSpeed (microsoft/DeepSpeed)
    3. Fully Sharded Data Parallel (FSDP) (pytorch/pytorch)
    4. Hugging Face Accelerate (huggingface/accelerate)
    5. Hugging Face Transformers (huggingface/transformers)
    6. Hugging Face Datasets (huggingface/datasets)
    7. TensorFlow (tensorflow/tensorflow)
    8. Horovod (horovod/horovod)
    9. tf.distribute.Strategy (tensorflow/tensorflow)
    10. Ray Train (ray-project/ray)
    11. Ray (ray-project/ray)
    12. Megatron-LM (NVIDIA/Megatron-LM)

    AI recommended 12 alternatives but never named stanford-crfm/mistral. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What frameworks simplify building and evaluating custom large language models with Hugging Face Transformers?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Accelerate
    2. Hugging Face Optimum
    3. PyTorch Lightning
    4. DeepSpeed
    5. Weights & Biases (W&B)
    6. MLflow

    AI recommended 6 alternatives but never named stanford-crfm/mistral. 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 stanford-crfm/mistral?
    pass
    AI named stanford-crfm/mistral explicitly

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

  • If a team adopts stanford-crfm/mistral in production, what risks or prerequisites should they evaluate first?
    pass
    AI named stanford-crfm/mistral 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 stanford-crfm/mistral solve, and who is the primary audience?
    pass
    AI named stanford-crfm/mistral 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
  • Prioritized action items8 vs 3 in Lite