This project focuses on efficient machine translation for nine Indic languages using the fine-tuned Gemma2-2B LLM and adapter switching, reducing computational overhead. It also leverages agentic methods and the Groq API for quality assurance and accurate translation analysis between source and target segments.

This repository contains a Jupyter notebook that demonstrates how to build a retrieval-based question-answering system using LangChain and Hugging Face. The notebook guides you through the process of setting up the environment, loading and processing documents, generating embeddings, and querying the system to retrieve relevant info from documents.

Google が開発した言語モデル Gemma 2 と高品質な音声合成エンジン AivisSpeech-Engine を組み合わせた音声つきのLLMチャットアプリ