Instructions to use dnnsdunca/Logical_Algorithm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Adapters
How to use dnnsdunca/Logical_Algorithm with Adapters:
from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("undefined") model.load_adapter("dnnsdunca/Logical_Algorithm", set_active=True) - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| datasets: | |
| - codeparrot/codeparrot-clean | |
| language: | |
| - en | |
| metrics: | |
| - code_eval | |
| library_name: adapter-transformers | |
| tags: | |
| - code | |
| # Code Generator with Chat Interface | |
| This project trains a code generator model using Hugging Face's GPT-2 and deploys it with a chat interface using FastAPI and CodeMirror for syntax highlighting. | |
| ## Setup | |
| 1. Install dependencies: | |
| ```bash | |
| pip install -r requirements.txt | |
| ``` | |
| 2. Train the model: | |
| ```bash | |
| python model_training.py | |
| ``` | |
| 3. Run the FastAPI server: | |
| ```bash | |
| uvicorn app:app --reload | |
| ``` | |
| 4. Open `index.html` in your browser to interact with the model. |