8 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 HuggingFace Transformers in 2026: sentiment analysis, NER, summarization, embeddings, image classification, and Whisper speech recognition. Production deployment with FastAPI included.
A comprehensive guide to how Large Language Models work — from transformer architecture to training and inference. Essential knowledge for every AI developer building with GPT-4, Claude, or open-source models.
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.
Complete guide to setting up and running Meta's LLaMA 3 models locally and in the cloud. Covers Hugging Face access, Ollama, quantization, chat formatting, fine-tuning with LoRA, and benchmarking for developers and ML engineers.
Master Mistral AI models: setup, usage, quantization, API integration, and fine-tuning. This guide covers every variant from Mistral 7B to Mistral Large, with practical Python code for developers and ML engineers building production LLM applications.
Deep dive into Mixtral 8x7B — the Mixture of Experts model that delivers 70B-class quality at 7B inference cost. This guide covers the MoE architecture, local setup, quantization, chat formatting, multi-GPU inference, and production deployment for ML engineers.
Complete guide to Google's Gemma and Gemma 2 open-source models: setup, chat formatting, quantization, fine-tuning, and production deployment. Covers Gemma 2B through 27B with Python examples for developers and ML engineers.