RRepoGEO

REPOGEO REPORT · LITE

dhcode-cpp/X-R1

Default branch main · commit 03d16fdd · scanned 6/17/2026, 1:03:28 AM

GitHub: 815 stars · 102 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 dhcode-cpp/X-R1, 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 specify category and language

    Why:

    CURRENT
    # X-R1
    COPY-PASTE FIX
    # X-R1: Minimal-Cost Reinforcement Learning for LLM Training (Python)
  • hightopics#2
    Add relevant topics to improve categorization

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    llm, reinforcement-learning, rlhf, fine-tuning, lora, deep-learning, machine-learning, python, gpu-training, cost-effective
  • mediumabout#3
    Update the repository description to explicitly mention 'Python framework' and 'LLMs'

    Why:

    CURRENT
    minimal-cost for training 0.5B R1-Zero
    COPY-PASTE FIX
    Python framework for minimal-cost reinforcement learning (RL) training of 0.5B+ R1-Zero LLMs.

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 dhcode-cpp/X-R1
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. Hugging Face PEFT · recommended 1×
  3. bitsandbytes · recommended 1×
  4. QLoRA · recommended 1×
  5. IA3 · recommended 1×
  • CATEGORY QUERY
    What are cost-effective methods for training large language models using reinforcement learning?
    you: not recommended
    AI recommended (in order):
    1. LoRA
    2. Hugging Face PEFT
    3. bitsandbytes
    4. QLoRA
    5. IA3
    6. Hugging Face TRL
    7. DeepSpeed-Chat
    8. PyTorch's Automatic Mixed Precision (AMP)
    9. DeepSpeed
    10. PyTorch FSDP
    11. DistilBERT
    12. RoBERTa
    13. Llama 2
    14. Mistral 7B
    15. Gemma

    AI recommended 15 alternatives but never named dhcode-cpp/X-R1. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to efficiently fine-tune large language models with LoRA on consumer GPUs?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face PEFT (huggingface/peft)
    2. bitsandbytes (TimDettmers/bitsandbytes)
    3. PyTorch FSDP (pytorch/pytorch)
    4. unsloth (unslothai/unsloth)
    5. accelerate (huggingface/accelerate)
    6. DeepSpeed (microsoft/DeepSpeed)
    7. xformers (facebookresearch/xformers)

    AI recommended 7 alternatives but never named dhcode-cpp/X-R1. 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 dhcode-cpp/X-R1?
    pass
    AI named dhcode-cpp/X-R1 explicitly

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

  • If a team adopts dhcode-cpp/X-R1 in production, what risks or prerequisites should they evaluate first?
    pass
    AI named dhcode-cpp/X-R1 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 dhcode-cpp/X-R1 solve, and who is the primary audience?
    pass
    AI named dhcode-cpp/X-R1 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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dhcode-cpp/X-R1 — 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