RRepoGEO

REPOGEO REPORT · LITE

shibing624/MedicalGPT

Default branch main · commit 2f1008cf · scanned 5/17/2026, 9:51:44 PM

GitHub: 5,405 stars · 748 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
28 /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
2 / 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 shibing624/MedicalGPT, 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 H1 and opening sentence to emphasize "medical LLM pipeline"

    Why:

    CURRENT
    # MedicalGPT: Training Medical GPT Model
**MedicalGPT** training medical GPT model with ChatGPT training pipeline, implemantation of Pretraining, Supervised Finetuning, RLHF(Reward Modeling and Reinforcement Learning), DPO(Direct Preference Optimization) and standalone OPD(On-Policy Distillation).
    COPY-PASTE FIX
    # MedicalGPT: A Comprehensive Pipeline for Training Medical Large Language Models
**MedicalGPT** provides an end-to-end pipeline for developing specialized medical GPT models, implementing advanced techniques like Pretraining, Supervised Finetuning, RLHF, DPO, ORPO, GRPO, and OPD.
  • mediumhomepage#2
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://github.com/shibing624/MedicalGPT
  • mediumtopics#3
    Add "finetuning" and "pipeline" to repository topics

    Why:

    CURRENT
    chatgpt, dpo, gpt, llama, llm, medical, medicalgpt
    COPY-PASTE FIX
    chatgpt, dpo, finetuning, gpt, llama, llm, medical, medicalgpt, pipeline

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 shibing624/MedicalGPT
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
huggingface/peft
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/peft · recommended 2×
  2. mistralai/mistral-src · recommended 2×
  3. huggingface/transformers · recommended 1×
  4. facebookresearch/llama · recommended 1×
  5. google/gemma · recommended 1×
  • CATEGORY QUERY
    How can I fine-tune a large language model specifically for medical applications?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. PEFT (huggingface/peft)
    3. LoRA (huggingface/peft)
    4. Llama 2 (facebookresearch/llama)
    5. Mistral 7B (mistralai/mistral-src)
    6. Mixtral 8x7B (mistralai/mistral-src)
    7. Gemma (google/gemma)
    8. Med-PaLM 2
    9. OpenAI API
    10. GPT-3.5 Turbo
    11. GPT-4
    12. EleutherAI's GPT-J (EleutherAI/gpt-j-6B)
    13. GPT-NeoX (EleutherAI/gpt-neox)
    14. NVIDIA NeMo (NVIDIA/NeMo)
    15. Microsoft Azure OpenAI Service

    AI recommended 15 alternatives but never named shibing624/MedicalGPT. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools are available for DPO or RLHF based preference optimization of LLMs?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face TRL
    2. DeepSpeed-Chat
    3. trlX
    4. OpenAI's Alignment Research (openai/baselines)
    5. PyTorch-RLHF

    AI recommended 5 alternatives but never named shibing624/MedicalGPT. 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 shibing624/MedicalGPT?
    pass
    AI did not name shibing624/MedicalGPT — likely talking about a different project

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

  • If a team adopts shibing624/MedicalGPT in production, what risks or prerequisites should they evaluate first?
    pass
    AI named shibing624/MedicalGPT 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 shibing624/MedicalGPT solve, and who is the primary audience?
    pass
    AI named shibing624/MedicalGPT explicitly

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

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MARKDOWN (README)
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shibing624/MedicalGPT — 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