RRepoGEO

REPOGEO REPORT · LITE

TRI-ML/prismatic-vlms

Default branch main · commit 874c5bbf · scanned 7/1/2026, 12:28:26 PM

GitHub: 1,000 stars · 1,142 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)

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

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 TRI-ML/prismatic-vlms, 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
  • highreadme#1
    Strengthen README opening to emphasize VLM training framework

    Why:

    CURRENT
    A flexible and efficient codebase for training visually-conditioned language-models (VLMs):
    COPY-PASTE FIX
    Prismatic VLMs is a flexible and efficient *framework* for *training and scaling custom visually-conditioned language models (VLMs)*, designed for researchers and developers building state-of-the-art multimodal AI.
  • mediumhomepage#2
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://arxiv.org/abs/2402.07865

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 TRI-ML/prismatic-vlms
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 1 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/transformers · recommended 1×
  2. Lightning-AI/pytorch-lightning · recommended 1×
  3. OpenAI CLIP · recommended 1×
  4. DALL-E 2 · recommended 1×
  5. salesforce/BLIP · recommended 1×
  • CATEGORY QUERY
    What are the best tools for training custom visually-conditioned language models efficiently?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. PyTorch Lightning (Lightning-AI/pytorch-lightning)
    3. OpenAI CLIP
    4. DALL-E 2
    5. BLIP (salesforce/BLIP)
    6. BLIP-2 (salesforce/BLIP-2)
    7. LLaVA (haotian-liu/LLaVA)
    8. TensorFlow (tensorflow/tensorflow)
    9. KerasCV (keras-team/keras-cv)
    10. PyTorch (pytorch/pytorch)
    11. timm (rwightman/pytorch-image-models)

    AI recommended 11 alternatives but never named TRI-ML/prismatic-vlms. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking an efficient framework for scaling large multimodal language models using PyTorch FSDP.
    you: not recommended
    AI recommended (in order):
    1. PyTorch-Lightning
    2. Hugging Face Accelerate
    3. DeepSpeed
    4. Composer
    5. FairScale

    AI recommended 5 alternatives but never named TRI-ML/prismatic-vlms. 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 TRI-ML/prismatic-vlms?
    pass
    AI named TRI-ML/prismatic-vlms explicitly

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

  • If a team adopts TRI-ML/prismatic-vlms in production, what risks or prerequisites should they evaluate first?
    pass
    AI named TRI-ML/prismatic-vlms 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 TRI-ML/prismatic-vlms solve, and who is the primary audience?
    pass
    AI did not name TRI-ML/prismatic-vlms — 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?

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TRI-ML/prismatic-vlms — 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