REPOGEO REPORT · LITE

DLYuanGod/TinyGPT-V

Default branch main · commit 836d3844 · scanned 6/18/2026, 11:16:42 PM

GitHub: 1,314 stars · 79 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 DLYuanGod/TinyGPT-V, 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 relevant topics to improve categorization and recall

Why:

CURRENT

(none)

COPY-PASTE FIX

multimodal-llm, vision-language-model, efficient-llm, small-llm, resource-constrained-ai, quantization, llm-inference, computer-vision, nlp

mediumreadme#2

Add a concise category statement to the README's opening

Why:

CURRENT

# TinyGPT-V

<font size='5'>**TinyGPT-V: Efficient Multimodal Large Language Model via Small Backbones**</font>

Zhengqing Yuan✟, Zhaoxu Li❁, Weiran Huang❋, Yanfang Ye✟, Lichao Sun❁

COPY-PASTE FIX

# TinyGPT-V: An Efficient Multimodal Large Language Model for Resource-Constrained Devices

<font size='5'>**TinyGPT-V: Efficient Multimodal Large Language Model via Small Backbones**</font>

TinyGPT-V is a state-of-the-art multimodal large language model (MLLM) designed for high performance on devices with limited computational resources. It achieves strong vision-language capabilities by integrating small backbones and advanced quantization techniques.

Zhengqing Yuan✟, Zhaoxu Li❁, Weiran Huang❋, Yanfang Ye✟, Lichao Sun❁

mediumhomepage#3
Add a homepage URL to the repository metadata
Why:
CURRENT
```
(none)
```
COPY-PASTE FIX
```
https://huggingface.co/spaces/llizhx/TinyGPT-V
```

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 DLYuanGod/TinyGPT-V

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

ggerganov/llama.cpp

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

ggerganov/llama.cpp · recommended 2×
AWQ · recommended 1×
GPTQ · recommended 1×
AutoGPTQ · recommended 1×
huggingface/transformers · recommended 1×

CATEGORY QUERY
How to run large language models on devices with limited computational resources?
you: not recommended
AI recommended (in order):
1. GGML/GGUF (ggerganov/llama.cpp)
2. llama.cpp (ggerganov/llama.cpp)
3. AWQ
4. GPTQ
5. AutoGPTQ
6. Hugging Face Transformers (huggingface/transformers)
7. DistilBERT
8. DistilRoBERTa
9. ONNX Runtime (microsoft/onnxruntime)
10. TensorFlow Lite (tensorflow/tensorflow)
11. OpenVINO (openvinotoolkit/openvino)
12. TinyLlama
13. Phi-2
14. Mistral 7B
15. NVIDIA Jetson Series
16. TensorRT (NVIDIA/TensorRT)
17. Google Coral Edge TPU
AI recommended 17 alternatives but never named DLYuanGod/TinyGPT-V. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Seeking an accurate multimodal AI model that performs well without requiring massive compute.
you: not recommended
AI recommended (in order):
1. OpenCLIP
2. BLIP
3. MiniGPT-4
4. LLaVA
5. OWL-ViT
AI recommended 5 alternatives but never named DLYuanGod/TinyGPT-V. 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 DLYuanGod/TinyGPT-V?
pass
AI named DLYuanGod/TinyGPT-V explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts DLYuanGod/TinyGPT-V in production, what risks or prerequisites should they evaluate first?
pass
AI named DLYuanGod/TinyGPT-V 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 DLYuanGod/TinyGPT-V solve, and who is the primary audience?
pass
AI named DLYuanGod/TinyGPT-V explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

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MARKDOWN (README)

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DLYuanGod/TinyGPT-V — 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