Tutorials

Hands-On Guides.

Step-by-step technical tutorials with code examples, from neural network fundamentals to production-grade LLM fine-tuning.

Intermediate1.5 to 2 hours

Preference Optimization After DPO: IPO, KTO, and SimPO Compared

Runnable comparison of preference optimization after DPO: IPO, KTO, and SimPO. What each objective changes, when reference-free wins, and how to switch in TRL.

Prerequisites

Comfort with the Hugging Face stack (transformerstrlpeftdatasets)one 24GB+ GPUand prior exposure to supervised fine-tuning. Understanding of DPO at a conceptual level helps but is not required.

Intermediate45 min

Constrained Decoding: How to Get Guaranteed JSON from an LLM (and the Reasoning Tax)

How constrained decoding guarantees valid JSON from an LLM: runnable vLLM and structured-output examples, the latency cost, and the reasoning tax that JSON-mode hides.

Prerequisites

Python 3.10+vLLM 0.19+ and a GPU that can serve the model (or a smaller Qwen3.6 dense variant on a 24GB card)the datasets and pydantic libraries

Intermediate2-3 hours

Fine-Tuning Transformer Models with Low-Rank Adaptation (LoRA)

Learn LoRA fine-tuning step by step: the math behind low-rank adaptation, QLoRA quantization, Unsloth training, hyperparameter selection, and practical code for consumer GPUs.

Prerequisites

Python proficiencyPyTorch basicsUnderstanding of transformer architecture