Bonsai-27B-GGUF
lmstudio-community · Kit
A GGUF conversion of the 27B Bonsai model for LM Studio, with a 3.80 GB Q1_0 language file and a 0.93 GB BF16 vision projector. Tested for text, vision and par…
A Gradio demo of the vidore ColPali v1.3 model, shipped with a stored file of page embeddings. For developers and researchers trying out visual document retrieval in the browser on Hugging Face.
Colpali is a kit in the Research category, listed by Mindket. It is free. This is version 1, last updated on . Download it on this page, a zip of its files. Nothing in it runs by itself.
pip install -U huggingface_hub hf download rayyan0/colpali --repo-type space --local-dir colpali cd colpali
This is a Gradio demo built around the vidore colpali-v1.3-hf model. Its repository ships a file of stored embeddings named pages_embeddings.pt, which suggests the app matches a query against pre-computed page data. The README is empty, so the page itself says little more. It runs on zero-a10g hardware at Hugging Face.
Developers and researchers who want to try the ColPali model on documents without setting it up, and anyone who wants a small example of how it is wrapped in an app.
| Industry | Who uses it | How they use it | Example |
|---|---|---|---|
| Research | Retrieval researchers | Try the model on stored page embeddings | Run a few queries and inspect what matches |
| Software | Developers | Read app.py to see how the model and embeddings are loaded | Reuse the layout for their own document search |
| Education | Students | Learn how document retrieval demos are built | Change a query and watch the results change |
Skip it if you need to search your own files, since the page mentions only a bundled embeddings file.
| Use case | Who | What they get back |
|---|---|---|
| Try the model | Researchers | Results from the ColPali model in a web form |
| Study the demo | Developers | A short Gradio app and an embeddings file to read |
pip install -U huggingface_hub hf download rayyan0/colpali --repo-type space --local-dir colpali cd colpali pip install gradio==6.31.0 pip install -r requirements.txt python app.py
The repository has five files, including app.py, requirements.txt and the embeddings file. The page does not say what a local run needs beyond the packages.
Online, open the Space and use the form on its page. 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 |
You open the Space and type a short question into its form. The app runs the model against its stored page data and shows what it finds, which you can read to see how well the match fits your question.
If you are new, open the Space online and try one short question. The README is empty, so treat what you see on the page as the guide.
If you know the field, the stored pages_embeddings.pt file points to retrieval over pre-computed page vectors rather than live indexing. The page gives no corpus, no scoring details and no instructions for adding your own pages.
A Gradio demo of the vidore ColPali v1.3 model, shipped with a stored file of page embeddings. For developers and researchers trying out visual document retrieval in the browser on Hugging Face.
Made by rayyan0 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.
What it is: This is a Gradio demo built around the vidore colpali-v1.3-hf model. Its repository ships a file of stored embeddings named pages_embeddings.pt, which suggests the app matches a query against pre-computed page data. The README is empty, so the page itself says little more. It runs on zero-a10g hardware at Hugging Face.
Who it suits: Developers and researchers who want to try the ColPali model on documents without setting it up, and anyone who wants a small example of how it is wrapped in an app.
Industry | Who uses it | How they use it | Example
Research | Retrieval researchers | Try the model on stored page embeddings | Run a few queries and inspect what matches
Software | Developers | Read app.py to see how the model and embeddings are loaded | Reuse the layout for their own document search
Education | Students | Learn how document retrieval demos are built | Change a query and watch the results change
Skip it if you need to search your own files, since the page mentions only a bundled embeddings file.
Where to use it:
Use case | Who | What they get back
Try the model | Researchers | Results from the ColPali model in a web form
Study the demo | Developers | A short Gradio app and an embeddings file to read
How to set it up:
pip install -U huggingface_hub
hf download rayyan0/colpali --repo-type space --local-dir colpali
cd colpali
pip install gradio==6.31.0
pip install -r requirements.txt
python app.py
The repository has five files, including app.py, requirements.txt and the embeddings file. 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. 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 open the Space and type a short question into its form. The app runs the model against its stored page data and shows what it finds, which you can read to see how well the match fits your question.
For newcomers and experts: If you are new, open the Space online and try one short question. The README is empty, so treat what you see on the page as the guide.
If you know the field, the stored pages_embeddings.pt file points to retrieval over pre-computed page vectors rather than live indexing. The page gives no corpus, no scoring details and no instructions for adding your own pages.
Open the Space in a browser
Type a query
The ColPali model compares it with stored pages
The matches are shown
Good to know:
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.
README.md 4 KB
Listed Hugging Face Spaces in the same category that share the most tags with this one.
| Product | Likes | Built with | License | Last change |
|---|---|---|---|---|
| Colpali This page | 1 | gradio | mit | |
| Open VLM LeaderboardShares gradio | 1,034 | gradio | apache-2.0 | |
| Face SwapShares gradio | 808 | gradio | cc-by-nc-sa-4.0 | |
| TTS Spaces ArenaShares gradio | 493 | gradio | zlib | |
| Depth Anything 3Shares gradio | 490 | gradio | cc-by-nc-4.0 | |
| BLIPShares gradio | 431 | gradio | bsd-3-clause |
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