REPOGEO REPORT · LITE

FoundationVision/Liquid

Default branch main · commit b2a7dd53 · scanned 6/12/2026, 8:07:43 PM

GitHub: 643 stars · 35 forks

AI VISIBILITY SCORE

40 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

2 pass · 0 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 FoundationVision/Liquid, 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

Elevate the project's core description immediately after the main title.

Why:

CURRENT

<font size="4">This repo implements Liquid, a scalable and unified autoregressive generation paradigm that seamlessly integrates multimodal comprehension and generation.</font>

COPY-PASTE FIX

Liquid is a scalable and unified autoregressive generation paradigm that seamlessly integrates multimodal comprehension and generation, implemented in this repository.

highreadme#2

Add a concise 'What is Liquid?' section to clearly state its capabilities.

Why:

COPY-PASTE FIX

## What is Liquid?
Liquid is a state-of-the-art multimodal large language model (MLLM) designed for:
- **Unified Multimodal Generation:** Seamlessly integrates both visual understanding and high-quality image generation.
- **Scalable Autoregressive Architecture:** Leverages LLM principles for efficient and powerful text-to-image generation.
- **Comprehensive Capabilities:** Supports diverse tasks from text-to-image synthesis to complex visual comprehension.

mediumreadme#3

Highlight the availability of the demo, model, and evaluation scripts.

Why:

COPY-PASTE FIX

## Get Started with Liquid
- **Live Demo:** Experience Liquid's capabilities directly on our [Hugging Face Space](https://huggingface.co/spaces/Junfeng5/Liquid_demo).
- **Download Model Checkpoints:** Access the Liquid-7B-IT model on [Hugging Face Models](https://huggingface.co/Junfeng5/Liquid_V1_7B).
- **Evaluation Scripts:** Find detailed scripts for text-to-image and visual understanding evaluations in [EVAL.md](evaluation/EVAL.md).

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 FoundationVision/Liquid

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

DALL-E 3

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

DALL-E 3 · recommended 2×
Hugging Face Transformers · recommended 1×
Diffusers · recommended 1×
PEFT · recommended 1×
GPT-4V · recommended 1×

CATEGORY QUERY
How to build a unified multimodal AI model for both image generation and visual understanding?
you: not recommended
AI recommended (in order):
1. Hugging Face Transformers
2. Diffusers
3. PEFT
4. DALL-E 3
5. GPT-4V
6. Gemini
7. Imagen
8. LLaMA
9. LLaVA
10. InstructBLIP
11. Segment Anything Model
12. PyTorch Lightning
13. JAX
14. Flax
15. TensorFlow
16. Keras
AI recommended 16 alternatives but never named FoundationVision/Liquid. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Looking for scalable autoregressive LLMs capable of high-quality text-to-image generation.
you: not recommended
AI recommended (in order):
1. DALL-E 3
2. Midjourney v6
3. Stable Diffusion XL (SDXL)
4. Imagen 2
5. Adobe Firefly
AI recommended 5 alternatives but never named FoundationVision/Liquid. This is the gap to close.
Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

Metadata completeness
pass
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 FoundationVision/Liquid?
pass
AI named FoundationVision/Liquid explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts FoundationVision/Liquid in production, what risks or prerequisites should they evaluate first?
pass
AI named FoundationVision/Liquid 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 FoundationVision/Liquid solve, and who is the primary audience?
pass
AI named FoundationVision/Liquid 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 FoundationVision/Liquid. 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/FoundationVision/Liquid.svg)](https://repogeo.com/en/r/FoundationVision/Liquid)

HTML

<a href="https://repogeo.com/en/r/FoundationVision/Liquid"><img src="https://repogeo.com/badge/FoundationVision/Liquid.svg" alt="RepoGEO" /></a>

Pro

Subscribe to Pro for deep diagnoses

FoundationVision/Liquid — 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