RRepoGEO

REPOGEO REPORT · LITE

sapientinc/HRM-Text

Default branch main · commit da566c99 · scanned 6/5/2026, 2:52:44 AM

GitHub: 1,041 stars · 96 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 sapientinc/HRM-Text, 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
    Clarify "HRM" acronym in the README's opening sentence

    Why:

    CURRENT
    HRM-Text is a 1B text generation model based on the HRM architecture, strengthened by task completion and latent space reasoning.
    COPY-PASTE FIX
    HRM-Text is a 1B text generation model based on the Hierarchical Reasoning Model (HRM) architecture, strengthened by task completion and latent space reasoning.
  • mediumtopics#2
    Expand topics to include efficiency and full HRM name

    Why:

    CURRENT
    hierarchical-reasoning-model, hrm, large-language-models, pretraining
    COPY-PASTE FIX
    hierarchical-reasoning-model, hrm, hrm-architecture, large-language-models, pretraining, efficient-llm-pretraining, low-cost-llm, foundation-model-pretraining
  • lowhomepage#3
    Add Hugging Face model page as the repository homepage

    Why:

    COPY-PASTE FIX
    https://huggingface.co/sapientinc/HRM-Text-1B

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 sapientinc/HRM-Text
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
ray-project/ray
Recommended in 3 of 2 queries
COMPETITOR LEADERBOARD
  1. ray-project/ray · recommended 3×
  2. pytorch/pytorch · recommended 2×
  3. microsoft/DeepSpeed · recommended 1×
  4. NVIDIA/Megatron-LM · recommended 1×
  5. facebookresearch/fairscale · recommended 1×
  • CATEGORY QUERY
    How can I pretrain a large language model efficiently with reduced compute costs?
    you: not recommended
    AI recommended (in order):
    1. DeepSpeed (microsoft/DeepSpeed)
    2. Megatron-LM (NVIDIA/Megatron-LM)
    3. FairScale (facebookresearch/fairscale)
    4. PyTorch FSDP (pytorch/pytorch)
    5. Hugging Face Accelerate (huggingface/accelerate)
    6. FlashAttention (HazyResearch/flash-attention)
    7. Optimum (huggingface/optimum)

    AI recommended 7 alternatives but never named sapientinc/HRM-Text. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools enable building a new text generation foundation model with hierarchical reasoning?
    you: not recommended
    AI recommended (in order):
    1. PyTorch (pytorch/pytorch)
    2. PyTorch Lightning (Lightning-AI/pytorch-lightning)
    3. Hugging Face Transformers (huggingface/transformers)
    4. TensorFlow (tensorflow/tensorflow)
    5. Keras (keras-team/keras)
    6. TensorFlow Extended (TFX) (tensorflow/tfx)
    7. JAX (google/jax)
    8. Flax (google/flax)
    9. Haiku (deepmind/dm-haiku)
    10. Ray (ray-project/ray)
    11. Ray Train (ray-project/ray)
    12. Ray Tune (ray-project/ray)
    13. Weights & Biases (W&B) (wandb/wandb)
    14. MLflow (mlflow/mlflow)
    15. Kubernetes (kubernetes/kubernetes)
    16. Kubeflow (kubeflow/kubeflow)

    AI recommended 16 alternatives but never named sapientinc/HRM-Text. 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 sapientinc/HRM-Text?
    pass
    AI named sapientinc/HRM-Text explicitly

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

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

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

Embed your GEO score

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MARKDOWN (README)
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sapientinc/HRM-Text — 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