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("fill-in-model-name") 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 655 Bytes
-
https://huggingface.co/dnnsdunca/Logical_Algorithm/resolve/main/README.md
- Command line
-
hf download hf://dnnsdunca/Logical_Algorithm/README.md
-
curl -L -o README.md https://huggingface.co/dnnsdunca/Logical_Algorithm/resolve/main/README.md
655 Bytes
metadata
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
Install dependencies:
pip install -r requirements.txtTrain the model:
python model_training.pyRun the FastAPI server:
uvicorn app:app --reloadOpen
index.htmlin your browser to interact with the model.