RRepoGEO

REPOGEO REPORT · LITE

codefuse-ai/MFTCoder

Default branch main · commit 8ba13f44 · scanned 6/2/2026, 10:47:24 AM

GitHub: 715 stars · 69 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 codefuse-ai/MFTCoder, 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 specific topics for Code LLM frameworks

    Why:

    CURRENT
    customizable, multi-model-support, multi-task-fine-tuning, multi-task-learning, user-friendly
    COPY-PASTE FIX
    customizable, multi-model-support, multi-task-fine-tuning, multi-task-learning, user-friendly, code-llm, llm-finetuning, code-generation-framework, deep-learning-framework
  • mediumabout#2
    Add a homepage URL to the repository's 'About' section

    Why:

    COPY-PASTE FIX
    [Insert URL to the KDD 2024 paper or a dedicated project page for MFTCoder]
  • mediumreadme#3
    Clarify the project's license in the README

    Why:

    COPY-PASTE FIX
    This project is released under [Specify License Name(s) and terms, e.g., "a custom license based on Apache 2.0 and MIT principles"]. Please see the [LICENCE](LICENCE) file for full details.

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 codefuse-ai/MFTCoder
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 2 of 2 queries
COMPETITOR LEADERBOARD
  1. ray-project/ray · recommended 2×
  2. huggingface/peft · recommended 1×
  3. huggingface/transformers · recommended 1×
  4. microsoft/DeepSpeed · recommended 1×
  5. FSDP (PyTorch Distributed) · recommended 1×
  • CATEGORY QUERY
    How to efficiently fine-tune large language models for multiple coding tasks simultaneously?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face PEFT Library (huggingface/peft)
    2. Hugging Face Transformers Library (huggingface/transformers)
    3. DeepSpeed (microsoft/DeepSpeed)
    4. FSDP (PyTorch Distributed)
    5. Ludwig (Predibase) (ludwig-ai/ludwig)
    6. MosaicML Composer (mosaicml/composer)
    7. LLM Foundry (mosaicml/llm-foundry)
    8. Ray Train (ray-project/ray)
    9. Ray Core (ray-project/ray)

    AI recommended 9 alternatives but never named codefuse-ai/MFTCoder. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a framework to boost accuracy and efficiency in multi-task training for code generation models.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. PyTorch Lightning
    3. TensorFlow
    4. OpenNMT-py
    5. Fairseq

    AI recommended 5 alternatives but never named codefuse-ai/MFTCoder. 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 codefuse-ai/MFTCoder?
    pass
    AI named codefuse-ai/MFTCoder explicitly

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

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

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

Embed your GEO score

Drop this badge into the README of codefuse-ai/MFTCoder. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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HTML
<a href="https://repogeo.com/en/r/codefuse-ai/MFTCoder"><img src="https://repogeo.com/badge/codefuse-ai/MFTCoder.svg" alt="RepoGEO" /></a>
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codefuse-ai/MFTCoder — 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