REPOGEO REPORT · LITE

2U1/Qwen-VL-Series-Finetune

Default branch master · commit 130ad7cc · scanned 5/9/2026, 4:02:52 PM

GitHub: 1,850 stars · 211 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 2U1/Qwen-VL-Series-Finetune, 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 README opening to emphasize dedicated toolkit

Why:

CURRENT

This repository contains a script for training Qwen2-VL, Qwen2.5-VL , Qwen3-VL and Qwen3.5 with only using HuggingFace and Liger-Kernel.

COPY-PASTE FIX

This repository offers a comprehensive, open-source toolkit for fine-tuning Qwen-VL series models (Qwen2-VL, Qwen2.5-VL, Qwen3-VL, Qwen3.5) using only HuggingFace and Liger-Kernel.

mediumhomepage#2
Add a homepage URL
Why:
COPY-PASTE FIX
```
https://[your-project-homepage-url]
```

lowreadme#3

Move "Other projects" section to a less prominent location

Why:

CURRENT

## Other projects

**[[Phi3-Vision Finetuning]](https://github.com/2U1/Phi3-Vision-Finetune)**<br>
**[[Llama3.2-Vision Finetuning]](https://github.com/2U1/Llama3.2-Vision-Ft)**<br>
**[[Molmo Finetune]](https://github.com/2U1/Molmo-Finetune)**<br>
**[[Pixtral Finetune]](https://github.com/2U1/Pixtral-Finetune)**<br>
**[[SmolVLM Finetune]](https://github.com/2U1/SmolVLM-Finetune)**<br>
**[[Gemma3 Finetune]](https://github.com/2U1/Gemma3-Finetune)**

COPY-PASTE FIX

## Related Projects by 2U1

**[[Phi3-Vision Finetuning]](https://github.com/2U1/Phi3-Vision-Finetune)**<br>
**[[Llama3.2-Vision Finetuning]](https://github.com/2U1/Llama3.2-Vision-Ft)**<br>
**[[Molmo Finetune]](https://github.com/2U1/Molmo-Finetune)**<br>
**[[Pixtral Finetune]](https://github.com/2U1/Pixtral-Finetune)**<br>
**[[SmolVLM Finetune]](https://github.com/2U1/SmolVLM-Finetune)**<br>
**[[Gemma3 Finetune]](https://github.com/2U1/Gemma3-Finetune)**

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 2U1/Qwen-VL-Series-Finetune

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

huggingface/transformers

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

huggingface/transformers · recommended 2×
microsoft/DeepSpeed · recommended 2×
Lightning-AI/lightning · recommended 1×
keras-team/keras · recommended 1×
TensorFlow Hub · recommended 1×

CATEGORY QUERY
How can I fine-tune a vision-language model for custom tasks?
you: not recommended
AI recommended (in order):
1. Hugging Face Transformers (huggingface/transformers)
2. PyTorch Lightning (Lightning-AI/lightning)
3. Keras (keras-team/keras)
4. TensorFlow Hub
5. OpenAI CLIP (openai/CLIP)
6. MMDetection (open-mmlab/mmdetection)
7. MMEngine (open-mmlab/mmengine)
8. Microsoft DeepSpeed (microsoft/DeepSpeed)
9. PyTorch FSDP
AI recommended 9 alternatives but never named 2U1/Qwen-VL-Series-Finetune. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Looking for an open-source solution to fine-tune large vision-language 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. OpenMMLab
5. LoRA
AI recommended 5 alternatives but never named 2U1/Qwen-VL-Series-Finetune. 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 2U1/Qwen-VL-Series-Finetune?
pass
AI named 2U1/Qwen-VL-Series-Finetune explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts 2U1/Qwen-VL-Series-Finetune in production, what risks or prerequisites should they evaluate first?
pass
AI named 2U1/Qwen-VL-Series-Finetune 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 2U1/Qwen-VL-Series-Finetune solve, and who is the primary audience?
pass
AI did not name 2U1/Qwen-VL-Series-Finetune — 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?

Embed your GEO score

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MARKDOWN (README)

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HTML

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2U1/Qwen-VL-Series-Finetune — 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