#ml

Fine-tune models with Hugging Face. Process: 1. Load pre-trained model and tokenizer. 2. Prepare dataset with train/val split. 3. Define training arguments (epochs, batch size, learning rate). 4. Use Trainer API for training loop. 5. Evaluate with metrics (accuracy, F1). 6. Save model and push to Hub. 7. Inference with pipeline(). 8. PEFT with LoRA for efficiency. Use accelerate for distributed training and implement gradient accumulation.