REPOGEO REPORT · LITE

CVI-SZU/Linly

Default branch main · commit ad223a75 · scanned 6/20/2026, 11:21:51 AM

GitHub: 3,050 stars · 225 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

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 CVI-SZU/Linly, 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

highlicense#1
Add a LICENSE file to the repository
Why:
COPY-PASTE FIX
```
Create a `LICENSE` file in the repository root with the Apache 2.0 license text.
```

highreadme#2

Add a concise, explicit positioning statement to the README's opening

Why:

CURRENT

## 中文 LLaMA1-2 & Linly-OpenLLaMA & Falcon 大模型

<p align="center">
    <br>
    
    <br>
</p>

<p align="center">
    
    
    
    
    
</p>
<br/>

本项目向社区提供**中文对话模型 Linly-ChatFlow 、中文基础模型 Chinese-LLaMA (1-2)、Chinese-Falcon 及其训练数据**。

COPY-PASTE FIX

## 中文 LLaMA1-2 & Linly-OpenLLaMA & Falcon 大模型

Linly 项目致力于提供全面的**中文基础大模型**和**中文对话模型**，以及高质量的**预训练与指令微调数据集**，是开发中文AI应用的理想开源资源。

本项目向社区提供**中文对话模型 Linly-ChatFlow 、中文基础模型 Chinese-LLaMA (1-2)、Chinese-Falcon 及其训练数据**。

mediumtopics#3

Expand GitHub topics to include 'foundation-model', 'pretraining', 'finetuning'

Why:

CURRENT

bert, chatbot, chatgpt, chinese, chinese-nlp, gpt-3, language-model, llama, nlp, zero-shot-learning

COPY-PASTE FIX

bert, chatbot, chatgpt, chinese, chinese-nlp, gpt-3, language-model, llama, nlp, zero-shot-learning, foundation-model, pretraining, finetuning

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 CVI-SZU/Linly

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Qwen

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

Qwen · recommended 1×
Baichuan2 · recommended 1×
ChatGLM3-6B · recommended 1×
Yi · recommended 1×
Llama 2 · recommended 1×

CATEGORY QUERY
Looking for open-source foundation models to build a Chinese chatbot assistant.
you: not recommended
AI recommended (in order):
1. Qwen
2. Baichuan2
3. ChatGLM3-6B
4. Yi
5. Llama 2
6. Bloom
AI recommended 6 alternatives but never named CVI-SZU/Linly. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What are good resources for pre-training or fine-tuning large language models for Chinese?
you: not recommended
AI recommended (in order):
1. Hugging Face Transformers Library
2. PaddlePaddle
3. MindSpore
4. OpenBMB
5. TencentPretrain
6. FudanNLP
AI recommended 6 alternatives but never named CVI-SZU/Linly. 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 CVI-SZU/Linly?
pass
AI named CVI-SZU/Linly explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts CVI-SZU/Linly in production, what risks or prerequisites should they evaluate first?
pass
AI named CVI-SZU/Linly 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 CVI-SZU/Linly solve, and who is the primary audience?
pass
AI named CVI-SZU/Linly 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 CVI-SZU/Linly. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

Live preview

MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/CVI-SZU/Linly.svg)](https://repogeo.com/en/r/CVI-SZU/Linly)

HTML

<a href="https://repogeo.com/en/r/CVI-SZU/Linly"><img src="https://repogeo.com/badge/CVI-SZU/Linly.svg" alt="RepoGEO" /></a>

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CVI-SZU/Linly — 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