RRepoGEO

REPOGEO REPORT · LITE

beyondguo/LLM-Tuning

Default branch master · commit 73e6bd55 · scanned 6/21/2026, 11:47:56 PM

GitHub: 1,014 stars · 97 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
23 /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
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 beyondguo/LLM-Tuning, 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 the README H1 to emphasize Sample Design Engineering (SDE)

    Why:

    CURRENT
    # LLM-Tuning
    COPY-PASTE FIX
    # LLM-Tuning: Sample Design Engineering (SDE) for LLM Downstream Fine-Tuning
  • hightopics#2
    Add specific topics to improve categorization

    Why:

    COPY-PASTE FIX
    llm-fine-tuning, sample-design-engineering, sde, large-language-models, data-centric-ai, prompt-engineering, machine-learning-research
  • highlicense#3
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the root directory, choosing an appropriate open-source license (e.g., MIT, Apache-2.0, or GPL-3.0) and adding its full text.

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 beyondguo/LLM-Tuning
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LoRA
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. LoRA · recommended 1×
  2. QLoRA · recommended 1×
  3. PEFT · recommended 1×
  4. DeepSpeed · recommended 1×
  5. FlashAttention · recommended 1×
  • CATEGORY QUERY
    How to efficiently fine-tune large language models for better downstream task performance?
    you: not recommended
    AI recommended (in order):
    1. LoRA
    2. QLoRA
    3. PEFT
    4. DeepSpeed
    5. FlashAttention
    6. Unsloth
    7. Axolotl

    AI recommended 7 alternatives but never named beyondguo/LLM-Tuning. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are effective strategies for designing training samples to optimize LLM downstream tuning?
    you: not recommended
    AI recommended (in order):
    1. Argilla (argilla-io/argilla)
    2. Snorkel (snorkel-team/snorkel)
    3. Lightly (lightly-ai/lightly)
    4. Hugging Face `transformers` library (huggingface/transformers)
    5. `datasets` (huggingface/datasets)
    6. NLPAug (makcedward/nlpaug)
    7. TextAttack (TextAttack/TextAttack)
    8. OpenAI API
    9. Anthropic API
    10. PyTorch Lightning (Lightning-AI/lightning)
    11. TensorFlow (tensorflow/tensorflow)
    12. Keras (keras-team/keras)
    13. `numpy` (numpy/numpy)
    14. Faiss (facebookresearch/faiss)
    15. Elasticsearch (elastic/elasticsearch)
    16. OpenSearch (opensearch-project/OpenSearch)
    17. Scikit-learn (scikit-learn/scikit-learn)
    18. DeepSpeed (microsoft/DeepSpeed)
    19. Weights & Biases (W&B) (wandb/wandb)

    AI recommended 19 alternatives but never named beyondguo/LLM-Tuning. 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 beyondguo/LLM-Tuning?
    pass
    AI named beyondguo/LLM-Tuning explicitly

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

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