RRepoGEO

REPOGEO REPORT · LITE

ssbuild/chatglm_finetuning

Default branch glm4.0 · commit f3086a4f · scanned 5/29/2026, 9:43:22 PM

GitHub: 1,532 stars · 173 forks

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 ssbuild/chatglm_finetuning, 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 clearly state its purpose

    Why:

    COPY-PASTE FIX
    This repository provides a consolidated, ready-to-use framework for efficiently fine-tuning a specific collection of popular Chinese large language models, including the ChatGLM series, using various common methods like LoRA, QLoRA, P-tuning, and full fine-tuning. It aims to simplify the adaptation of these models for specific tasks and datasets.
  • mediumhomepage#2
    Add a homepage URL to the repository metadata

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    https://github.com/ssbuild/chatglm_finetuning
  • lowabout#3
    Enhance the repository description for clarity

    Why:

    CURRENT
    chatglm 6b finetuning and alpaca finetuning
    COPY-PASTE FIX
    A consolidated framework for fine-tuning popular Chinese large language models (e.g., ChatGLM, Baichuan2, Qwen) using methods like LoRA, QLoRA, P-tuning, and full fine-tuning.

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 ssbuild/chatglm_finetuning
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. PEFT · recommended 1×
  3. Axolotl · recommended 1×
  4. Lit-GPT · recommended 1×
  5. OpenAssistant/oasst-sft-training · recommended 1×
  • CATEGORY QUERY
    How to efficiently fine-tune large language models using LoRA or QLoRA techniques?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. PEFT
    3. Axolotl
    4. Lit-GPT
    5. OpenAssistant/oasst-sft-training (OpenAssistant/oasst-sft-training)
    6. bitsandbytes

    AI recommended 6 alternatives but never named ssbuild/chatglm_finetuning. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are robust PyTorch libraries for supervised fine-tuning of generative AI models?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. PyTorch Lightning (Lightning-AI/pytorch-lightning)
    3. DeepSpeed (microsoft/DeepSpeed)
    4. Accelerate (huggingface/accelerate)
    5. bitsandbytes (TimDettmers/bitsandbytes)

    AI recommended 5 alternatives but never named ssbuild/chatglm_finetuning. 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 ssbuild/chatglm_finetuning?
    pass
    AI did not name ssbuild/chatglm_finetuning — 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 ssbuild/chatglm_finetuning in production, what risks or prerequisites should they evaluate first?
    pass
    AI named ssbuild/chatglm_finetuning 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 ssbuild/chatglm_finetuning solve, and who is the primary audience?
    pass
    AI named ssbuild/chatglm_finetuning 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 ssbuild/chatglm_finetuning. 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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ssbuild/chatglm_finetuning — 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