Collaborative
AI Research

Learn to build and train large language models from the ground up, covering tokenization, architecture design, and training strategies.

• Implement transformer architecture from scratch
• Design tokenization strategies
• Train models on distributed systems
• Optimize inference performance

Master parameter-efficient fine-tuning methods including LoRA, QLoRA, and prefix tuning for adapting LLMs to specific tasks.

• Implement LoRA and QLoRA techniques
• Fine-tune models with limited compute
• Evaluate fine-tuned model performance
• Deploy fine-tuned models in production

Build production-ready Retrieval Augmented Generation systems with advanced chunking, embedding strategies, and reranking.

• Design optimal chunking strategies
• Implement vector database solutions
• Build RAG pipelines with LangChain
• Optimize retrieval accuracy

Explore cutting-edge multimodal models combining vision, language, and audio for next-generation AI applications.

• Train vision-language models
• Implement multimodal embeddings
• Build cross-modal retrieval systems
• Create generative multimodal applications

Design and implement autonomous AI agents with reasoning, planning, and tool-use capabilities for complex workflows.

• Implement ReAct and ReWOO patterns
• Build multi-step reasoning agents
• Design tool-calling interfaces
• Create agentic workflow systems

Master techniques for compressing and optimizing large models for edge deployment and inference efficiency.

• Implement quantization techniques
• Apply model pruning strategies
• Perform knowledge distillation
• Optimize for edge devices