RRepoGEO

REPOGEO REPORT · LITE

PKU-Alignment/align-anything

Default branch main · commit 3f9decc2 · scanned 5/27/2026, 6:43:28 PM

GitHub: 4,651 stars · 504 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 PKU-Alignment/align-anything, 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's opening statement for clarity

    Why:

    CURRENT
    Align-Anything aims to align any modality large models (any-to-any models) with human intentions and values.
    COPY-PASTE FIX
    Align-Anything is a unified, modular framework designed to align *any* modality large models (any-to-any models) with human intentions and values, supporting diverse alignment algorithms and multi-modal fine-tuning.
  • mediumhomepage#2
    Add project homepage URL

    Why:

    COPY-PASTE FIX
    https://space.bilibili.com/3493095748405551?spm_id_from=333.337.search-card.all.click
  • lowreadme#3
    Add a "Why Align-Anything?" or comparison section to README

    Why:

    COPY-PASTE FIX
    ## Why Align-Anything?
Align-Anything stands out as a unified, modular framework specifically designed for *all-modality* alignment, integrating a wide variety of state-of-the-art algorithms (e.g., SFT, DPO, PPO) into a single, consistent toolkit. Unlike general-purpose frameworks like Hugging Face Transformers or PyTorch Lightning, or single-modality alignment libraries such as TRL, Align-Anything provides comprehensive support for diverse multi-modal (image/video/audio) models and any-to-any alignment tasks within a single, easily customizable ecosystem.

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 PKU-Alignment/align-anything
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 2 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/transformers · recommended 2×
  2. microsoft/DeepSpeed · recommended 2×
  3. huggingface/peft · recommended 1×
  4. huggingface/trl · recommended 1×
  5. huggingface/alignment-handbook · recommended 1×
  • CATEGORY QUERY
    How to train multimodal large models effectively using human feedback alignment?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. PEFT (huggingface/peft)
    3. TRL (huggingface/trl)
    4. Alignment Handbook (huggingface/alignment-handbook)
    5. DeepSpeed (microsoft/DeepSpeed)
    6. Megatron-LM (NVIDIA/Megatron-LM)
    7. PyTorch Lightning (Lightning-AI/lightning)
    8. JAX (google/jax)
    9. Flax (google/flax)
    10. OpenAI's Triton (openai/triton)
    11. Ray RLlib (ray-project/ray)

    AI recommended 11 alternatives but never named PKU-Alignment/align-anything. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a modular framework to fine-tune diverse large models across multiple modalities.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. PyTorch Lightning (Lightning-AI/pytorch-lightning)
    3. Keras (keras-team/keras)
    4. DeepSpeed (microsoft/DeepSpeed)
    5. OpenMMLab

    AI recommended 5 alternatives but never named PKU-Alignment/align-anything. 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 PKU-Alignment/align-anything?
    pass
    AI named PKU-Alignment/align-anything explicitly

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

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

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

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PKU-Alignment/align-anything — 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