RRepoGEO

REPOGEO REPORT · LITE

apple/ml-cross-entropy

Default branch main · commit b7a02791 · scanned 6/12/2026, 3:03:28 AM

GitHub: 605 stars · 72 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 apple/ml-cross-entropy, 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
    Clarify the software's purpose immediately after the main title

    Why:

    COPY-PASTE FIX
    This repository provides the Cut Cross-Entropy (CCE) software, an optimized implementation for significantly reducing memory usage during cross-entropy loss computation in large-vocabulary language models. CCE enables efficient training of LLMs by avoiding full logit materialization, making the loss computation memory footprint negligible.
  • mediumabout#2
    Add a concise description to the repository's 'About' section

    Why:

    COPY-PASTE FIX
    Optimized implementation of Cut Cross-Entropy (CCE) for large-vocabulary language models, drastically reducing memory footprint during loss computation by avoiding full logit materialization.

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 apple/ml-cross-entropy
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
torch.cuda.amp
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. torch.cuda.amp · recommended 1×
  2. NVIDIA APEX · recommended 1×
  3. tf.keras.mixed_precision · recommended 1×
  4. torch.utils.checkpoint.checkpoint · recommended 1×
  5. Hugging Face Accelerate · recommended 1×
  • CATEGORY QUERY
    How to reduce memory usage for cross-entropy loss in large language model training?
    you: not recommended
    AI recommended (in order):
    1. torch.cuda.amp
    2. NVIDIA APEX
    3. tf.keras.mixed_precision
    4. torch.utils.checkpoint.checkpoint
    5. Hugging Face Accelerate
    6. Microsoft DeepSpeed
    7. FairScale
    8. torch.nn.functional.cross_entropy
    9. torch.nn.CrossEntropyLoss
    10. tf.keras.losses.SparseCategoricalCrossentropy
    11. tf.keras.losses.CategoricalCrossentropy

    AI recommended 11 alternatives but never named apple/ml-cross-entropy. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking efficient methods for computing cross-entropy loss in large vocabulary models.
    you: not recommended
    AI recommended (in order):
    1. PyTorch
    2. TensorFlow
    3. Faiss

    AI recommended 3 alternatives but never named apple/ml-cross-entropy. 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 apple/ml-cross-entropy?
    pass
    AI named apple/ml-cross-entropy explicitly

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

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

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

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apple/ml-cross-entropy — 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