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.

Engineer LLM fine-tuning with clear prompting and RAG boundaries, governed datasets, LoRA and QLoRA, evaluation gates, and production operations.

Learn why LLMs use tokens, how BPE and SentencePiece work, and how token budgets govern context, latency, and cost.

Transformer architecture that powers modern LLMs — self-attention, encoder/decoder stacks, KV cache implications, and why transformers replaced RNNs.

Understand scaled dot-product attention, Q/K/V, multi-head attention, KV caches, and the production trade-offs behind long-context LLMs.

Low-Rank Adaptation — train small adapters on frozen LLM weights for efficient domain specialization and multi-adapter serving.

Quantized LoRA — fine-tune large models on limited GPUs by combining 4-bit (NF4) quantization with low-rank adapters.

Parameter-Efficient Fine-Tuning hub — LoRA, QLoRA, adapters, and when PEFT beats full fine-tuning, RAG, or prompting alone.

Reinforcement Learning from Human Feedback - the three-stage pipeline (SFT, reward model, PPO) that aligns LLM behavior to human preferences, plus when to use DPO instead.

Direct Preference Optimization - a simpler, more stable alternative to RLHF that aligns LLM behavior on preference pairs without a separate reward model or PPO loop.

Meta Llama open-weight models — Llama 4 Scout/Maverick for new deployments, Llama 3.x baselines, plus Muse Spark 1.3 (closed API / Muse Code) and Muse Glimmer (on-device open 30B).

Mistral AI models — commercial Large/Codestral tiers (Large 4 in public preview), efficient open weights, European residency options, Firefox Smart Window distribution, and workload routing vs frontier closed APIs.

DeepSeek models — V4.1-Flash (deepseek-flash) as the live multimodal Flash SKU, V4-Pro-0813 still on deepseek-v4-pro after 2026-09-14, V3/R1 open checkpoints, and peak/off-peak pricing from 2026-09-10.

Sarvam AI models — Sarvam-M (24B), Sarvam 30B (2.4B active), and Sarvam 105B (10.3B active), Apache-2.0 weights trained for Indian languages.

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.