Fine-tuning

Compare zero-shot, few-shot, embedding-based, and fine-tuned classification approaches with production trade-offs.

Strategies for updating LLMs with new data including knowledge cutoff solutions, fine-tuning approaches, elastic weight consolidation, experience replay, and RAG alternatives.

Build production DPR systems: train dual encoders, fine-tune on domain data, scale with FAISS, and outperform BM25 on specialized domains.

Practical guide to RLHF and DPO alignment techniques for fine-tuning open-source LLMs with human preference data, reward modeling, and evaluation.

Fine-tune embeddings for specialized domains. Generate training pairs with LLMs, train with sentence-transformers, and deploy custom embedding models in production.

Master LoRA and QLoRA for efficient fine-tuning of open-source models like Llama 2, Mistral, and Phi on limited hardware.

Learn when and how to fine-tune OpenAI models in production, including dataset preparation, cost optimization, and evaluation strategies.