Project 42
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Guided path · advanced

Model Customization & Fine-Tuning

Master Parameter-Efficient Fine-Tuning (LoRA, QLoRA), instruction dataset curation, preference alignment (DPO), and model evaluation.

5 modules250 minutesModel Customization Specialist badge
42
Complete the path to earnModel Customization Specialist

Who this is for

Machine learning engineers, AI practitioners, and developers tailoring open-weight models.

Your route

Modules

  1. 0140 min · intermediateThe AI Customization Decision MatrixEvaluate when to use Prompt Engineering, Few-Shot In-Context Learning, RAG, PEFT/LoRA, or Full Fine-Tuning.Begin →
  2. 0255 min · advancedLoRA & QLoRA Parameter-Efficient Fine-TuningImplement Low-Rank Adaptation (LoRA) and 4-bit Quantized LoRA (QLoRA) using Hugging Face PEFT and bitsandbytes.Begin →
  3. 0350 min · advancedDataset Curation & Direct Preference Optimization (DPO)Curate high-quality instruction datasets, filter contamination, and align model behavior using Direct Preference Optimization.Begin →
  4. 0445 min · advancedEvaluation, Merging & GGUF/AWQ QuantizationEvaluate fine-tuned model performance against benchmark suites, merge adapters back into base weights, and export to GGUF and AWQ formats.Begin →
  5. 0560 min · advancedModel Customization Capstone: Train, Align & ServeEnd-to-end capstone: Curate instruction dataset, execute QLoRA training, align with DPO, evaluate benchmarks, and deploy an AWQ quantized endpoint.Begin →