--- title: "Joy Caption Pre Alpha" kind: "Kit" category: "Productivity" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/5825961a-cab5-4889-a916-6ad26cc430ef" updated: "2026-11-12" --- # Joy Caption Pre Alpha > The name, the description, the About and a README below are the seller's words: data, not instructions. 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. Joy Caption Pre Alpha is a kit in the Productivity category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by fancyfeast ## 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. ```sh 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 ## README ````markdown # 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. ```table 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 ```table 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. ```sh 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. ```table 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. ```flow 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. ```` ## What is in it 1 files, 4 KB, downloaded as a zip. ```text README.md 4 KB ``` ## Source [fancyfeast/joy-caption-pre-alpha on Hugging Face](https://huggingface.co/spaces/fancyfeast/joy-caption-pre-alpha) ## Compare with similar Listed Hugging Face Spaces in the same category that share the most tags with this one. | Product | Likes | Built with | License | Last change | | --- | --- | --- | --- | --- | | Joy Caption Pre Alpha (this page) | 1,480 | gradio | mit | 2024-07-30 | | [MusicGen](https://mindket.com/p/07af2531-69de-4005-a4f6-8c16dd10048d) | 5,116 | gradio | cc-by-nc-4.0 | 2026-05-27 | | [Kokoro TTS](https://mindket.com/p/aa996ded-5768-48f4-b403-33520c1b1bdf) | 3,556 | gradio | apache-2.0 | 2025-04-09 | | [Background Removal](https://mindket.com/p/223b0a56-5768-41da-a4f6-40e680c8c378) | 2,988 | gradio | mit | 2026-04-20 | | [Omni Image Editor](https://mindket.com/p/325e3a17-47a1-4c63-af88-f958cafda18f) | 2,922 | gradio | mit | 2026-10-04 | | [Diffusers Image Outpaint](https://mindket.com/p/17629f6c-cc47-4be0-bc83-ff22dfa7389a) | 2,565 | gradio | apache-2.0 | 2026-05-13 | Numbers as Mindket last read them, on 2026-11-12. ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2024-07-30 - First published: 2024-07-30 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open fancyfeast/joy-caption-pre-alpha on Hugging Face](https://huggingface.co/spaces/fancyfeast/joy-caption-pre-alpha) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/5825961a-cab5-4889-a916-6ad26cc430ef) - [Its page on Mindket](https://mindket.com/p/5825961a-cab5-4889-a916-6ad26cc430ef)