10 articles
Fine-tune Llama, Mistral, or any open-source LLM on your custom dataset in 2026. Step-by-step guide using QLoRA, PEFT, and HuggingFace Transformers. Train on a single GPU for under $10.
Master fine-tuning LLMs for custom tasks using OpenAI API and open-source models.
Master LoRA and QLoRA for efficient fine-tuning of large language models on consumer hardware.
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.