README: LLM (Large Language Model) - French and English

Overview

This project is designed to support the use of a Large Language Model (LLM) for generating and processing content in both French and English. The LLM can assist with a variety of tasks, such as translation, text summarization, question answering, and more.

Features

Bilingual Support: Seamlessly handles French and English inputs and outputs.

Translation: Converts text between French and English with high accuracy.

Content Generation: Creates natural-sounding text in both languages.

Summarization: Generates concise summaries of longer texts.

Customization: Allows fine-tuning for domain-specific applications.

Prerequisites

Python 3.7 or later

Required libraries (install via pip install -r requirements.txt):

transformers

torch

langdetect

sentencepiece

Installation

Clone the repository:

git clone https://github.com/your-repo/llm-french-english.git cd llm-french-english

Install the dependencies:

pip install -r requirements.txt

(Optional) Set up a virtual environment:

python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate

Usage

Basic Example

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

Load the model and tokenizer

model_name = "your-huggingface-model-name" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

Translate text

text = "Bonjour, comment allez-vous?" inputs = tokenizer(text, return_tensors="pt") outputs = model.generate(**inputs) translation = tokenizer.decode(outputs[0], skip_special_tokens=True) print("Translation:", translation)

Generate text

prompt = "Write a story about a hero in French." inputs = tokenizer(prompt, return_tensors="pt") outputs = model.generate(**inputs, max_length=100) generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True) print("Generated Text:", generated_text)

CLI Usage

Run the following command to use the model from the command line:

python cli.py --task translate --input "Hello, how are you?" --target_language fr

Configuration

Model Name: Update the model_name parameter in config.py to specify a different pretrained Hugging Face model.

Language Detection: The model can automatically detect the input language if auto_detect is enabled.

Testing

Run the included unit tests to verify functionality:

pytest tests/

Model Files

best_model.keras: A trained Keras model for additional fine-tuning.

final_model.keras: The finalized Keras model ready for deployment. For compatibility, these can be converted to a Hugging Face format if needed.

Contributing

Fork the repository.

Create a feature branch (git checkout -b feature/YourFeature).

Commit your changes (git commit -m 'Add YourFeature').

Push to the branch (git push origin feature/YourFeature).

Open a Pull Request.

License

This project is licensed under the MIT License. See the LICENSE file for details.