12 articles
Fine-tune Llama, Mistral, or any open-source LLM on custom data in 2026 using QLoRA and HuggingFace. Train a 7B model on a single GPU for under $5 with step-by-step code and deployment to HuggingFace Hub.
Master LLM fine-tuning — when to fine-tune vs prompt engineer, OpenAI fine-tuning API, open-source fine-tuning with Hugging Face, data preparation, and evaluation. A practical guide for production AI teams.
Master LoRA and QLoRA for efficient fine-tuning of large language models on consumer GPUs. Covers rank selection, target modules, quantization, and training LLaMA 3 and Mistral with minimal resources.
Master the Hugging Face Transformers library for loading, running, and fine-tuning LLMs — pipelines, tokenizers, generation config, quantization, and integration with the Model Hub. The definitive Python guide.
Master Together AI — the cloud platform for running 200+ open-source LLMs at production scale. This guide covers setup, chat completions, streaming, embeddings, fine-tuning, custom model deployment, and cost optimization for developers and ML engineers.
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.