Fine-Tuning LLMs
Adapt pre-trained language models for your specific tasks and domains. Learn to fine-tune large language models for custom applications.
Level: Intermediate · Category: Tools & Frameworks · Estimated time: 5 hours
Prerequisites
- Transformers & NLP
- PyTorch Mastery
Lessons
- Fine-Tuning Fundamentals — When to fine-tune vs prompt, full vs parameter-efficient.
- Data Preparation for Fine-Tuning — Formatting, instruction tuning, and dataset quality.
- Full Fine-Tuning with Transformers — Training loops, hyperparameters, and GPU memory.
- LoRA & QLoRA — Low-rank adaptation, quantization, and memory efficiency.
- Evaluation & Benchmarking — Perplexity, task metrics, and human evaluation.
- Deployment of Fine-Tuned Models — vLLM, TGI, and inference optimization.
Topics covered
fine-tuning, llm, lora, transformers, huggingface