Hugging Face Transformers

Learn how LLMs like GPT, Claude, and Llama process and generate human language at scale.

Discover how AI converts text, images, and data into numerical vectors that capture meaning.

Learn how to adapt a pre-trained model to your specific domain or task with custom training data.

Understand the transformer architecture that powers every modern LLM - self-attention, encoder-decoder stacks, and why it replaced RNNs.

Learn how attention lets models focus on relevant parts of input - the core mechanism behind GPT, Claude, and every modern LLM.

Low-Rank Adaptation - the most popular parameter-efficient fine-tuning method for adapting LLMs without full retraining.

Quantized LoRA - fine-tune large models on consumer GPUs by combining 4-bit quantization with low-rank adapters.

Parameter-Efficient Fine-Tuning - techniques to adapt large models by training only a small subset of parameters.

Reinforcement Learning from Human Feedback - how ChatGPT and Claude learn to be helpful, harmless, and honest.

Direct Preference Optimization - a simpler alternative to RLHF that aligns models using preference pairs without a separate reward model.

Meta Llama open-weight models - self-hosting, fine-tuning, quantization, and building on open foundations.

Mistral AI models - efficient architectures, Mixtral MoE, and strong open-weight alternatives.

DeepSeek models - reasoning-focused architectures, open weights, and cost-efficient inference.

Choosing and evaluating embedding models - OpenAI, Cohere, BGE, E5, and open-source alternatives for production RAG.

Cross-encoder rerankers that re-score retrieved documents for precision - the highest-ROI improvement in most RAG pipelines.