Spaces:
Sleeping
Sleeping
title: SmolLM2 Text Generator | |
emoji: 🦀 | |
colorFrom: blue | |
colorTo: gray | |
sdk: gradio | |
sdk_version: 5.12.0 | |
app_file: app.py | |
pinned: false | |
# SmolLM2 Text Generator | |
This is a Gradio application for generating text using the trained SmolLM2 model. The app allows users to input a text prompt and generate multiple sequences of text based on that prompt. The number of sequences and the length of the generated text can be adjusted using sliders. | |
## Features | |
- **Text Generation**: Generate text based on a user-provided prompt using the SmolLM2 model. | |
- **Adjustable Length**: Control the length of the generated text. | |
- **Multiple Sequences**: Generate multiple sequences of text in one go. | |
## Requirements | |
To run this application, you need the following Python packages: | |
- `torch` | |
- `transformers` | |
- `gradio` | |
You can install the required packages using pip: | |
```bash | |
pip install -r requirements.txt | |
``` | |
## Usage | |
1. **Run the App**: Launch the Gradio app by running the following command in your terminal: | |
```bash | |
python app.py | |
``` | |
2. **Input Prompt**: Enter your desired text prompt in the provided textbox. | |
3. **Adjust Sliders**: | |
- Use the "Predict Additional Text of Length" slider to set the desired length of the generated text. | |
- Use the "Number of Sequences to Generate" slider to specify how many sequences you want to generate. | |
4. **Generate Text**: Click the "Generate Text" button to produce the text sequences. | |
5. **View Output**: The generated sequences will be displayed in the output textbox, each prefixed with "Sequence X:" for clarity. | |
## Example | |
- **Prompt**: "Once upon a time" | |
- **Number of Sequences**: 2 | |
**Output**: | |
``` | |
Sequence 1: | |
Once upon a time, there is a cat .... | |
Sequence 2: | |
Once upon a time in a small village .... | |
``` | |
## License | |
This project is licensed under the MIT License. See the LICENSE file for more details. | |
## Acknowledgments | |
- Hugging Face for the Transformers library and model support. | |
- Gradio for providing an easy-to-use interface for machine learning applications. | |
- The SmolLM2 model for enabling advanced text generation capabilities. | |