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("<base-model-id>") model.load_adapter("dnnsdunca/Logical_Algorithm", set_active=True) - Notebooks
- Google Colab
- Kaggle
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Download README.md from dnnsdunca/Logical_Algorithm: direct link, hf CLI and curl.
- Browser
- Download file 648 Bytes
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https://huggingface.co/dnnsdunca/Logical_Algorithm/resolve/0c7db5a82c5ddb3fb4e6295ca8dec81b6f71edf0/README.md
- Command line
-
hf download hf://dnnsdunca/Logical_Algorithm@0c7db5a82c5ddb3fb4e6295ca8dec81b6f71edf0/README.md
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curl -L -o README.md https://huggingface.co/dnnsdunca/Logical_Algorithm/resolve/0c7db5a82c5ddb3fb4e6295ca8dec81b6f71edf0/README.md
648 Bytes
| license: mit | |
| 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. |