RRepoGEO

REPOGEO REPORT · LITE

young-geng/EasyLM

Default branch main · commit fe5b2c35 · scanned 5/24/2026, 2:23:17 PM

GitHub: 2,517 stars · 259 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 young-geng/EasyLM, 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's opening to emphasize 'comprehensive framework'

    Why:

    CURRENT
    Large language models (LLMs) made easy, EasyLM is a one stop solution for pre-training, finetuning, evaluating and serving LLMs in JAX/Flax.
    COPY-PASTE FIX
    EasyLM is a comprehensive, high-level framework for pre-training, finetuning, evaluating, and serving large language models (LLMs) in JAX/Flax, abstracting away the complexities of distributed training across hundreds of TPU/GPU accelerators.
  • mediumhomepage#2
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://github.com/young-geng/EasyLM
  • lowtopics#3
    Add more specific topics to clarify LLM framework functionality

    Why:

    CURRENT
    chatbot, deep-learning, flax, jax, language-model, large-language-models, llama, natural-language-processing, transformer
    COPY-PASTE FIX
    chatbot, deep-learning, flax, jax, language-model, large-language-models, llama, natural-language-processing, transformer, llm-framework, llm-training, llm-finetuning, llm-serving

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 young-geng/EasyLM
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face Transformers
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Transformers · recommended 1×
  2. JAX/Flax examples · recommended 1×
  3. Trax · recommended 1×
  4. DeepMind's Haiku · recommended 1×
  5. EleutherAI's GPT-J/GPT-NeoX · recommended 1×
  • CATEGORY QUERY
    What's a comprehensive JAX/Flax framework for pre-training and deploying large language models?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. JAX/Flax examples
    3. Trax
    4. DeepMind's Haiku
    5. EleutherAI's GPT-J/GPT-NeoX

    AI recommended 5 alternatives but never named young-geng/EasyLM. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to easily scale LLM training across multiple GPUs/TPUs using JAX?
    you: not recommended
    AI recommended (in order):
    1. pmap
    2. pjit
    3. flax.linen
    4. optax
    5. Hugging Face transformers
    6. Pathways
    7. Orbax
    8. Mesh-TF

    AI recommended 8 alternatives but never named young-geng/EasyLM. 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 young-geng/EasyLM?
    pass
    AI named young-geng/EasyLM explicitly

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

  • If a team adopts young-geng/EasyLM in production, what risks or prerequisites should they evaluate first?
    pass
    AI named young-geng/EasyLM 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 young-geng/EasyLM solve, and who is the primary audience?
    pass
    AI named young-geng/EasyLM 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 young-geng/EasyLM. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/young-geng/EasyLM.svg)](https://repogeo.com/en/r/young-geng/EasyLM)
HTML
<a href="https://repogeo.com/en/r/young-geng/EasyLM"><img src="https://repogeo.com/badge/young-geng/EasyLM.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

young-geng/EasyLM — 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