Multilingual Chatbot Using LLaMA, DeepSeek & RAG

Developed a high-performance, multilingual enterprise chatbot leveraging advanced transformer models such as LLaMA and DeepSeek. The solution integrated LoRA-based fine-tuning to significantly reduce inference latency while maintaining high task completion rates. Retrieval-augmented generation (RAG) techniques were employed to enhance response relevance and context awareness, resulting in a 30% improvement in user satisfaction and an 80% increase in response accuracy. The project delivered a scalable and efficient conversational AI system tailored for enterprise-level applications.

Key Achievements:

Achieved an 80% improvement in response accuracy through dynamic context-aware fine-tuning.

Reduced inference latency by 25% using LoRA, with 98% task completion maintained.

Boosted response relevance by 30% via RAG, enhancing user experience across languages.

Delivered a scalable chatbot solution integrated into an enterprise architecture, optimized for real-time interactions.

Tools & Technologies:

LLaMA, DeepSeek, PyTorch, Hugging Face Transformers, LangChain, RAG, LoRA, Python

Impact:

This project provided a robust and efficient conversational AI solution capable of handling complex enterprise tasks with high accuracy, low latency, and multilingual support—ideal for global businesses seeking intelligent automation.