RRepoGEO

REPOGEO REPORT · LITE

mosaicml/llm-foundry

Default branch main · commit 0cdb2f42 · scanned 6/29/2026, 2:52:06 PM

GitHub: 4,421 stars · 586 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
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 mosaicml/llm-foundry, 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

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

OVERALL DIRECTION
  • mediumtopics#1
    Add more specific topics to improve categorization as an LLM development platform.

    Why:

    CURRENT
    deep-learning, llm, neural-networks, nlp, pytorch
    COPY-PASTE FIX
    deep-learning, llm, neural-networks, nlp, pytorch, llm-training, llm-finetuning, llm-deployment, generative-ai, large-language-models
  • lowreadme#2
    Add a sentence to the README clarifying its relationship to foundational ML libraries.

    Why:

    COPY-PASTE FIX
    While leveraging popular libraries like Hugging Face Transformers and PyTorch, LLM Foundry provides an opinionated, end-to-end workflow specifically optimized for the entire LLM lifecycle.

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 mosaicml/llm-foundry
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
huggingface/transformers
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/transformers · recommended 2×
  2. huggingface/accelerate · recommended 2×
  3. ray-project/ray · recommended 2×
  4. Lightning-AI/pytorch-lightning · recommended 1×
  5. microsoft/DeepSpeed · recommended 1×
  • CATEGORY QUERY
    What are the best tools for efficiently training and finetuning large language models?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. PyTorch Lightning (Lightning-AI/pytorch-lightning)
    3. DeepSpeed (microsoft/DeepSpeed)
    4. Accelerate (huggingface/accelerate)
    5. JAX (google/jax)
    6. Flax (google/flax)
    7. Megatron-LM (NVIDIA/Megatron-LM)

    AI recommended 7 alternatives but never named mosaicml/llm-foundry. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I rapidly experiment with different LLM architectures and deployment strategies?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. Hugging Face Accelerate (huggingface/accelerate)
    3. PyTorch Lightning (Lightning-AI/lightning)
    4. OpenAI API
    5. Azure OpenAI Service
    6. MLflow (mlflow/mlflow)
    7. Ray Train (ray-project/ray)
    8. Ray Serve (ray-project/ray)
    9. Kubernetes (kubernetes/kubernetes)
    10. KServe (kserve/kserve)
    11. Seldon Core (SeldonIO/seldon-core)

    AI recommended 11 alternatives but never named mosaicml/llm-foundry. 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 mosaicml/llm-foundry?
    pass
    AI named mosaicml/llm-foundry explicitly

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

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

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

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mosaicml/llm-foundry — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite