RRepoGEO

REPOGEO REPORT · LITE

hkust-nlp/deita

Default branch main · commit b279f2c3 · scanned 6/6/2026, 1:13:11 PM

GitHub: 596 stars · 35 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 hkust-nlp/deita, 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's opening sentence to specify Deita's core function

    Why:

    CURRENT
    Welcome to Deita (**D**ataE**fficient **I**nstruction **T**uning for **A**lignment) Project!
    COPY-PASTE FIX
    Deita is an open-source toolkit for **automatic data selection and generation** for instruction tuning in Large Language Models (LLMs), enabling data-efficient alignment.
  • mediumtopics#2
    Add more specific topics for automatic data selection and generation

    Why:

    CURRENT
    alignment, data-centric, instruction-tuning, large-language-models
    COPY-PASTE FIX
    alignment, data-centric, instruction-tuning, large-language-models, automatic-data-selection, synthetic-data, data-generation
  • mediumhomepage#3
    Add the paper link as the repository's homepage

    Why:

    COPY-PASTE FIX
    https://arxiv.org/abs/2312.15685

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 hkust-nlp/deita
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. huggingface/peft · recommended 1×
  3. QLoRA · recommended 1×
  4. microsoft/DeepSpeed · recommended 1×
  5. OpenAccess-AI-Collective/axolotl · recommended 1×
  • CATEGORY QUERY
    How to efficiently fine-tune large language models for better alignment with less data?
    you: not recommended
    AI recommended (in order):
    1. LoRA
    2. Hugging Face PEFT (huggingface/peft)
    3. QLoRA
    4. DeepSpeed (microsoft/DeepSpeed)
    5. Axolotl (OpenAccess-AI-Collective/axolotl)
    6. RLHF
    7. PPO
    8. Hugging Face TRL (huggingface/trl)
    9. DPO

    AI recommended 9 alternatives but never named hkust-nlp/deita. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help select high-quality instruction tuning data for large language models?
    you: not recommended
    AI recommended (in order):
    1. Argilla (argilla-io/argilla)
    2. Snorkel Flow
    3. Cleanlab (cleanlab/cleanlab)
    4. Galileo
    5. Label Studio (heartexlabs/label-studio)
    6. OpenAI Evals (openai/evals)
    7. Humanloop

    AI recommended 7 alternatives but never named hkust-nlp/deita. 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 hkust-nlp/deita?
    pass
    AI named hkust-nlp/deita explicitly

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

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

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

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hkust-nlp/deita — 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