Joy Caption Pre Alpha

Joy Caption Pre Alpha

An early demo that pairs a SigLIP vision model with Llama 3.1 8B, apparently to write captions for images. A Gradio app on Hugging Face, marked pre-alpha by its name.

Made by fancyfeast on Hugging Face. These notes are Mindket's, written from its Space page and README; the app's own files and license are on Hugging Face.

About

What it is: This is an early Gradio demo, marked pre-alpha in its name. It names two models, a SigLIP vision model and Llama 3.1 8B, and the name Joy Caption suggests it writes text captions for images. The README is a leftover chatbot template, so the page itself says little about the app. It runs on zero-a10g hardware at Hugging Face.

Who it suits: People who want to try an image captioning demo, and developers who want to see how a vision model and a language model are joined in a Gradio app.

Industry | Who uses it | How they use it | Example
Content | Creators | Try a caption for a picture they own | Add an image and read the text that comes back
Software | Developers | Read app.py to see how two models are combined | Reuse the layout in a captioning demo
Research | Vision-language researchers | Try an early captioning setup | Compare its output on several pictures

Skip it if you need a stable, documented product, since the page is a pre-alpha demo with a template README.

Where to use it:

• A web browser, on the Space's page at Hugging Face
• Windows, macOS or Linux with Python, as a local copy

Use case | Who | What they get back
Caption a picture | Creators | A short text for the image
Study the setup | Developers | A running copy of the demo app

How to set it up:

• Nothing at all if you only use the online Space
• Python and pip, for a local copy
• The Python packages listed in requirements.txt

1. Open the Space's page at Hugging Face to use it online.
2. For a local copy, install the Hugging Face hub package and download the Space.
3. Move into the new folder and install Gradio at the pinned version.
4. Install the packages from requirements.txt.
5. Start the app with Python.

pip install -U huggingface_hub
hf download fancyfeast/joy-caption-pre-alpha --repo-type space --local-dir joy-caption-pre-alpha
cd joy-caption-pre-alpha
pip install gradio==4.36.1
pip install -r requirements.txt
python app.py

The repository holds a README, app.py, requirements.txt and a folder named wpkklhc6, which the page does not explain. The page does not say what a local run needs beyond the packages.

How to use it: Online, open the Space and use the form on its page to give it an image. On your own machine, run the app file and open the page Gradio serves in your browser. The page lists no other commands.

Tool | What it does
python app.py | Starts the Gradio app on your computer

Example: You add a photo of a dog on a beach through the form. The app is expected to answer with a written caption describing the picture, and you read it to see how well it matches what you see.

For newcomers and experts: If you are new, open the Space online and try one clear photo. Since it is pre-alpha, expect rough edges and compare the caption with the picture yourself.

If you know the field, a SigLIP vision encoder beside a Llama 3.1 8B language model is a common captioning design. The page does not say how the two are connected or trained.

Open the Space in a browser
Add an image
The SigLIP vision model reads it
Llama 3.1 8B writes the caption

Good to know:

• License: mit
• Cost to run: none named, though the Space runs on zero-a10g hardware at Hugging Face
• Data it reaches: the images you add, and the model files it pulls from Hugging Face
• Not for: anything that needs documented behavior, since the page is a template README

On Hugging Face, 13 November 2026

• Likes: 1,480
• Built with: gradio
• License: mit

Links

• Space: https://huggingface.co/spaces/fancyfeast/joy-caption-pre-alpha
• Files: https://huggingface.co/spaces/fancyfeast/joy-caption-pre-alpha/tree/main

Try it

Open the Space in your browser, or copy it to your own Hugging Face account from its page. Nothing in this kit runs by itself.