REPOGEO REPORT · LITE

xverse-ai/XVERSE-13B

Default branch main · commit b0f30a0e · scanned 6/3/2026, 2:33:23 AM

GitHub: 642 stars · 57 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 xverse-ai/XVERSE-13B, 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 descriptive topics to the repository

Why:

COPY-PASTE FIX

large-language-model, llm, multilingual, chinese-llm, 13b-model, long-context, tool-calling, generative-ai, ai-model, deep-learning, apache-2.0-license

highhomepage#2

Add an official project homepage URL

Why:

COPY-PASTE FIX

[Insert official project or company AI homepage URL here, e.g., https://xverse.cn/en/ai]

mediumreadme#3

Add a concise English summary to the top of the README

Why:

CURRENT

<h4 align="left">
    <p>
        <b>中文</b> |
        <a href="README_EN.md">English</a> |
        <a href="README_JA.md">日本語</a>
    <p>
</h4>

## 更新信息

COPY-PASTE FIX

<h4 align="left">
    <p>
        <b>中文</b> |
        <a href="README_EN.md">English</a> |
        <a href="README_JA.md">日本語</a>
    <p>
</h4>

XVERSE-13B is a powerful, multilingual large language model developed by XVERSE Technology Inc. This 13B parameter model features an industry-leading 8K context length (with a 256K long-context variant), supports tool calling, and is optimized for both Chinese and English, making it suitable for deployment on consumer GPUs for a wide range of generative AI applications.

## 更新信息

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 xverse-ai/XVERSE-13B

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Mistral 7B Instruct v0.2 / v0.1

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

Mistral 7B Instruct v0.2 / v0.1 · recommended 1×
Llama 2 7B Chat / 13B Chat · recommended 1×
Qwen 1.5 7B Chat / 14B Chat · recommended 1×
OpenHermes 2.5 Mistral 7B · recommended 1×
Zephyr 7B Beta · recommended 1×

CATEGORY QUERY
What are some open-source multilingual large language models deployable on consumer GPUs?
you: not recommended
AI recommended (in order):
1. Mistral 7B Instruct v0.2 / v0.1
2. Llama 2 7B Chat / 13B Chat
3. Qwen 1.5 7B Chat / 14B Chat
4. OpenHermes 2.5 Mistral 7B
5. Zephyr 7B Beta
6. Gemma 2B Instruct / 7B Instruct
7. Phi-2
AI recommended 7 alternatives but never named xverse-ai/XVERSE-13B. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Seeking a commercially usable large language model with long context and tool calling ability.
you: not recommended
AI recommended (in order):
1. GPT-4o
2. Claude 3 Opus
3. GPT-4 Turbo
4. Google Gemini 1.5 Pro
5. Llama 3
6. Mistral Large
AI recommended 6 alternatives but never named xverse-ai/XVERSE-13B. 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 xverse-ai/XVERSE-13B?
pass
AI named xverse-ai/XVERSE-13B explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts xverse-ai/XVERSE-13B in production, what risks or prerequisites should they evaluate first?
pass
AI named xverse-ai/XVERSE-13B 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 xverse-ai/XVERSE-13B solve, and who is the primary audience?
pass
AI named xverse-ai/XVERSE-13B 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 xverse-ai/XVERSE-13B. 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)

[![RepoGEO](https://repogeo.com/badge/xverse-ai/XVERSE-13B.svg)](https://repogeo.com/en/r/xverse-ai/XVERSE-13B)

HTML

<a href="https://repogeo.com/en/r/xverse-ai/XVERSE-13B"><img src="https://repogeo.com/badge/xverse-ai/XVERSE-13B.svg" alt="RepoGEO" /></a>

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xverse-ai/XVERSE-13B — 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