Nanosaur2-670M
well9472 · Kit
Makes illustrations from text prompts with a small 670M-parameter diffusion model, run through ComfyUI. Built as a low-budget research model for people who wan…
Runs a Gradio app in the browser, backed by model training, evaluation and prediction code, with saved checkpoints and example files. It suits students and developers who want to study or run a finished machine learning project.
FloraFind is a kit in the Developer 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 crashn/FloraFind --repo-type space --local-dir FloraFind cd FloraFind
FloraFind is a Gradio app that runs on Hugging Face Spaces and needs Python 3.13. Its repository holds scripts for data setup, model building, training, evaluation and prediction, plus saved checkpoints, example inputs and a recorded final project presentation. The README gives no description of what the model recognizes, so look at the app and its examples first.
It suits people who want to try a student machine learning project in the browser, or read its code to see how a training pipeline is laid out.
| Industry | Who uses it | How they use it | Example |
|---|---|---|---|
| Education | Students and teachers | Study a finished final project from data setup through to prediction | A learner reads the scripts in order and compares them with the app's behavior. |
| Software | Python developers | Download the repository and run the app on their own computer | A developer installs the packages and starts app.py to test the model locally. |
| Hobby projects | Curious beginners | Open the Space page and try the app with nothing to install | A beginner opens the page in a browser and gives the app one input. |
If you need a production service with support, skip it, because this is a project demo.
| Use case | Who | What they get back |
|---|---|---|
| Try the demo online | Anyone | The app running in the browser with nothing to install |
| Run a local copy | Developers | The same Gradio app on their own computer |
| Read the code | Students | The scripts and folders behind the model, from data setup to prediction |
pip install -U huggingface_hub hf download crashn/FloraFind --repo-type space --local-dir FloraFind cd FloraFind pip install gradio==6.30.0 pip install -r requirements.txt python app.py
Online, open the Space's page and use the app there. On your own computer, run the command below from the FloraFind folder and use the app Gradio starts.
python app.py
| Tool | What it does |
|---|---|
| app.py | The main file, which starts the Gradio app |
| requirements.txt | Lists the Python packages the app needs |
| checkpoints | A folder that, going by its name, holds saved model checkpoints |
| examples | A folder that, going by its name, holds sample inputs to try |
You download a local copy, run python app.py and open the app in your browser. You give it one of the sample inputs from the examples folder. The app answers with the model's output for that input.
If you are new, open the Space's page first and give the app a single input to see what it returns. Once that works, try the local copy.
If you know the field, the script names point to a training pipeline split into data setup, model building, an engine, training, evaluation and prediction. The repository is large at 8336 files, so the download takes longer than for a typical demo.
Runs a Gradio app in the browser, backed by model training, evaluation and prediction code, with saved checkpoints and example files. It suits students and developers who want to study or run a finished machine learning project.
Made by crashn 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: FloraFind is a Gradio app that runs on Hugging Face Spaces and needs Python 3.13. Its repository holds scripts for data setup, model building, training, evaluation and prediction, plus saved checkpoints, example inputs and a recorded final project presentation. The README gives no description of what the model recognizes, so look at the app and its examples first.
Who it suits: It suits people who want to try a student machine learning project in the browser, or read its code to see how a training pipeline is laid out.
Industry | Who uses it | How they use it | Example
Education | Students and teachers | Study a finished final project from data setup through to prediction | A learner reads the scripts in order and compares them with the app's behavior.
Software | Python developers | Download the repository and run the app on their own computer | A developer installs the packages and starts app.py to test the model locally.
Hobby projects | Curious beginners | Open the Space page and try the app with nothing to install | A beginner opens the page in a browser and gives the app one input.
If you need a production service with support, skip it, because this is a project demo.
Where to use it:
Use case | Who | What they get back
Try the demo online | Anyone | The app running in the browser with nothing to install
Run a local copy | Developers | The same Gradio app on their own computer
Read the code | Students | The scripts and folders behind the model, from data setup to prediction
How to set it up:
pip install -U huggingface_hub
hf download crashn/FloraFind --repo-type space --local-dir FloraFind
cd FloraFind
pip install gradio==6.30.0
pip install -r requirements.txt
python app.py
How to use it: Online, open the Space's page and use the app there. On your own computer, run the command below from the FloraFind folder and use the app Gradio starts.
python app.py
Tool | What it does
app.py | The main file, which starts the Gradio app
requirements.txt | Lists the Python packages the app needs
checkpoints | A folder that, going by its name, holds saved model checkpoints
examples | A folder that, going by its name, holds sample inputs to try
Example: You download a local copy, run python app.py and open the app in your browser. You give it one of the sample inputs from the examples folder. The app answers with the model's output for that input.
For newcomers and experts: If you are new, open the Space's page first and give the app a single input to see what it returns. Once that works, try the local copy.
If you know the field, the script names point to a training pipeline split into data setup, model building, an engine, training, evaluation and prediction. The repository is large at 8336 files, so the download takes longer than for a typical demo.
Download the Space
Install gradio and the packages
Run app.py
Open the Gradio app in a browser
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
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