RRepoGEO

REPOGEO REPORT · LITE

mlfoundations/wise-ft

Default branch master · commit 58b7a4b3 · scanned 6/12/2026, 4:37:55 PM

GitHub: 764 stars · 75 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 mlfoundations/wise-ft, 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
    Refine README H1 to emphasize the WiSE-FT method and its unique benefit

    Why:

    CURRENT
    # Robust fine-tuning of zero-shot models
    COPY-PASTE FIX
    # WiSE-FT: Robust Fine-Tuning of Zero-Shot Models via Weight Interpolation
  • mediumlicense#2
    Clarify the repository's license in the README

    Why:

    COPY-PASTE FIX
    Add a section to the README, e.g., "## License", stating clearly which license(s) apply to the code and data in this repository, referencing the existing LICENSE file.

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 mlfoundations/wise-ft
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
PyTorch
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. PyTorch · recommended 2×
  2. TensorFlow · recommended 2×
  3. Hugging Face Transformers · recommended 2×
  4. Hugging Face PEFT library · recommended 1×
  5. LoRA · recommended 1×
  • CATEGORY QUERY
    How to fine-tune large pre-trained models without sacrificing out-of-distribution generalization?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face PEFT library
    2. LoRA
    3. QLoRA
    4. AdaLoRA
    5. Avalanche
    6. PackNet
    7. PyTorch
    8. TensorFlow
    9. Albumentations
    10. Augly
    11. Hugging Face Transformers
    12. DANN
    13. MMAction2
    14. MMDetection
    15. MMSegmentation
    16. OpenMMLab
    17. Pytorch-DANN
    18. lightly
    19. Scikit-learn

    AI recommended 19 alternatives but never named mlfoundations/wise-ft. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are effective strategies for robustly adapting zero-shot vision models to new datasets?
    you: not recommended
    AI recommended (in order):
    1. CLIP
    2. ALIGN
    3. OpenCLIP
    4. CoOp
    5. CoCoOp
    6. MaPLe
    7. Hugging Face Transformers
    8. PyTorch Lightning
    9. PyTorch
    10. TensorFlow
    11. scikit-learn
    12. Hugging Face Accelerate

    AI recommended 12 alternatives but never named mlfoundations/wise-ft. 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 mlfoundations/wise-ft?
    pass
    AI named mlfoundations/wise-ft explicitly

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

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

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

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mlfoundations/wise-ft — 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