RRepoGEO

REPOGEO REPORT · LITE

mst272/LLM-Dojo

Default branch main · commit 1c7068bf · scanned 6/5/2026, 12:33:12 AM

GitHub: 937 stars · 86 forks

AI VISIBILITY SCORE
30 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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 mst272/LLM-Dojo, 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 opening to clarify "framework" identity

    Why:

    CURRENT
    > A lightweight playground for `RLHF` and `SFT` experiments, with support for `RLVR`, `KD`, and `Guide-KD`.
    COPY-PASTE FIX
    > A lightweight framework for `RLHF` and `SFT` experiments, supporting `RLVR`, `KD`, and `Guide-KD` for LLM post-training.
  • hightopics#2
    Add relevant topics for better categorization

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    llm, fine-tuning, rlhf, sft, knowledge-distillation, deepspeed, lora, qlora, reinforcement-learning, machine-learning-framework
  • highlicense#3
    Add a LICENSE file to the repository

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root with a standard open-source license (e.g., MIT, Apache-2.0, or GPL-3.0).

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 mst272/LLM-Dojo
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face Transformers
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Transformers · recommended 2×
  2. PyTorch Lightning · recommended 2×
  3. DeepSpeed · recommended 2×
  4. Accelerate · recommended 1×
  5. PyTorch · recommended 1×
  • CATEGORY QUERY
    What are the best lightweight frameworks for LLM supervised fine-tuning and reinforcement learning experiments?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. Accelerate
    3. PyTorch Lightning
    4. PyTorch
    5. DeepSpeed
    6. FSDP
    7. trl
    8. Ray RLlib
    9. Keras
    10. TensorFlow
    11. TF-Agents
    12. JAX
    13. Flax
    14. Haiku

    AI recommended 14 alternatives but never named mst272/LLM-Dojo. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a framework for efficient large language model fine-tuning with knowledge distillation and mixed training.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. DeepSpeed
    3. PyTorch Lightning
    4. OpenAI Triton

    AI recommended 4 alternatives but never named mst272/LLM-Dojo. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    fail

    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 mst272/LLM-Dojo?
    pass
    AI named mst272/LLM-Dojo explicitly

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

  • If a team adopts mst272/LLM-Dojo in production, what risks or prerequisites should they evaluate first?
    pass
    AI named mst272/LLM-Dojo 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 mst272/LLM-Dojo solve, and who is the primary audience?
    pass
    AI named mst272/LLM-Dojo explicitly

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

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mst272/LLM-Dojo — 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