RRepoGEO

REPOGEO REPORT · LITE

yule-BUAA/MergeLM

Default branch main · commit 6d49ad96 · scanned 6/12/2026, 2:47:57 PM

GitHub: 868 stars · 52 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 yule-BUAA/MergeLM, 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
  • hightopics#1
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    llm-merging, language-models, model-fusion, icml-2024, peft, huggingface
  • highreadme#2
    Reposition the README's H1 and opening sentence to clearly state the repo's purpose

    Why:

    CURRENT
    # Language Models are Super Mario: Absorbing Abilities from Homologous Models as a Free Lunch

<div  align="center">  
 
</div>

This repository is built for the paper Language Models are Super Mario: Absorbing Abilities from Homologous Models as a Free Lunch.
    COPY-PASTE FIX
    # MergeLM: Codebase for Merging Language Models (ICML 2024)

This repository provides the official codebase and implementation for MergeLM, a novel method for merging language models to absorb abilities from homologous models as a free lunch.
  • highlicense#3
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the repository root with a standard open-source license (e.g., MIT, Apache-2.0) that clarifies the terms of use for the codebase.

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 yule-BUAA/MergeLM
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
pytorch/pytorch
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. pytorch/pytorch · recommended 2×
  2. tensorflow/tensorflow · recommended 2×
  3. huggingface/transformers · recommended 2×
  4. scikit-learn/scikit-learn · recommended 1×
  5. Google's Switch Transformer · recommended 1×
  • CATEGORY QUERY
    How can I merge multiple language models to enhance their overall capabilities?
    you: not recommended
    AI recommended (in order):
    1. Scikit-learn (scikit-learn/scikit-learn)
    2. PyTorch (pytorch/pytorch)
    3. TensorFlow (tensorflow/tensorflow)
    4. Google's Switch Transformer
    5. Google's GLaM
    6. Mistral AI's Mixtral 8x7B
    7. DeepMind's Gopher
    8. Hugging Face Transformers (huggingface/transformers)
    9. DistilBERT
    10. TinyBERT
    11. Hugging Face PEFT (huggingface/peft)
    12. Google's T5
    13. AdapterHub (Adapter-Hub/AdapterHub)

    AI recommended 13 alternatives but never named yule-BUAA/MergeLM. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for methods to combine pre-trained LLMs to improve specific task performance.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. OpenAI API
    3. Anthropic API
    4. Google Gemini API
    5. Mistral 8x7B (Mixtral)
    6. LangChain (langchain-ai/langchain)
    7. LlamaIndex (run-llama/llama_index)
    8. Haystack (deepset-ai/haystack)
    9. PyTorch (pytorch/pytorch)
    10. TensorFlow (tensorflow/tensorflow)
    11. JAX (google/jax)

    AI recommended 11 alternatives but never named yule-BUAA/MergeLM. 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 yule-BUAA/MergeLM?
    pass
    AI named yule-BUAA/MergeLM explicitly

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

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

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

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yule-BUAA/MergeLM — 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