Code Generation with Large Language Models
This project is a Flask web application that utilizes the simba LLM autoregressive language model to generate code solutions for coding problems provided by users. It leverages the torch and transformers libraries for interacting with the simba LLM model. The application receives a coding problem through a web form, formats and encodes the input, passes it to the simba LLM model for prediction, and displays the generated code solution as the output.
Natural Language Processing
The provided code demonstrates a Flask web application that utilizes simba LLM, an autoregressive language model, to generate code based on user input. The application takes a coding problem as input and generates a corresponding code solution using the simba LLM model.
The code leverages the torch and transformers libraries to interact with the simba LLM model. It uses the Flask framework to create a web application for user interaction. The application's functionality includes formatting the input problem, encoding the text using the tokenizer, generating predictions using the simba LLM model, and displaying the generated code as the output.
Upon receiving a coding problem through a web form, the input is formatted, encoded, and passed to the simba LLM model for prediction. The generated code solution is then extracted and displayed to the user. The application is designed to handle POST requests containing the coding problem and respond with the generated code as the output.
Flask, Transformers, Pytorch