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
| from fastapi import FastAPI | |
| from pydantic import BaseModel | |
| from transformers import GPT2LMHeadModel, GPT2Tokenizer | |
| import uvicorn | |
| class CodeRequest(BaseModel): | |
| prompt: str | |
| app = FastAPI() | |
| model = GPT2LMHeadModel.from_pretrained('./codegen_model') | |
| tokenizer = GPT2Tokenizer.from_pretrained('./codegen_model') | |
| def generate_code(request: CodeRequest): | |
| inputs = tokenizer.encode(request.prompt, return_tensors='pt') | |
| outputs = model.generate(inputs, max_length=150, num_return_sequences=1) | |
| generated_code = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| return {"generated_code": generated_code} | |
| if __name__ == "__main__": | |
| uvicorn.run(app, host="0.0.0.0", port=8000) |