RRepoGEO

REPOGEO REPORT · LITE

lich99/ChatGLM-finetune-LoRA

Default branch main · commit 5b0dec68 · scanned 6/9/2026, 10:02:45 AM

GitHub: 716 stars · 63 forks

AI VISIBILITY SCORE
22 /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
1 / 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 lich99/ChatGLM-finetune-LoRA, 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

2 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 paragraph to highlight unique value

    Why:

    CURRENT
    This repository contains code for finetuning ChatGLM-6b using low-rank adaptation (LoRA).
    COPY-PASTE FIX
    This repository provides a complete solution for efficiently finetuning the ChatGLM-6b large language model using the low-rank adaptation (LoRA) method, including finetuned weights and optimized training code for consumer GPUs.
  • mediumhomepage#2
    Add a project homepage URL

    Why:

    COPY-PASTE FIX
    Add a project homepage URL to the repository's 'About' section (e.g., a GitHub Pages site, documentation, or a related project page).

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 lich99/ChatGLM-finetune-LoRA
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
huggingface/transformers
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/transformers · recommended 1×
  2. huggingface/peft · recommended 1×
  3. microsoft/DeepSpeed · recommended 1×
  4. TimDettmers/bitsandbytes · recommended 1×
  5. OpenAI API · recommended 1×
  • CATEGORY QUERY
    How can I efficiently fine-tune large language models for domain-specific applications?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. PEFT Library (huggingface/peft)
    3. DeepSpeed (microsoft/DeepSpeed)
    4. bitsandbytes (TimDettmers/bitsandbytes)
    5. OpenAI API
    6. Google Cloud Vertex AI
    7. AWS SageMaker
    8. MosaicML

    AI recommended 8 alternatives but never named lich99/ChatGLM-finetune-LoRA. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What frameworks enable low-resource adaptation of large generative models on consumer GPUs?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face PEFT
    2. QLoRA
    3. bitsandbytes
    4. Axolotl
    5. Lit-GPT
    6. DeepSpeed

    AI recommended 6 alternatives but never named lich99/ChatGLM-finetune-LoRA. 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 lich99/ChatGLM-finetune-LoRA?
    pass
    AI did not name lich99/ChatGLM-finetune-LoRA — 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 lich99/ChatGLM-finetune-LoRA in production, what risks or prerequisites should they evaluate first?
    pass
    AI named lich99/ChatGLM-finetune-LoRA 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 lich99/ChatGLM-finetune-LoRA solve, and who is the primary audience?
    pass
    AI did not name lich99/ChatGLM-finetune-LoRA — 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?

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lich99/ChatGLM-finetune-LoRA — 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