Choose the exact Llama 4 checkpoint you want to fine-tune
Verify the model license and usage terms
Define the task, target behavior, and evaluation criteria
Collect and clean a high-quality training dataset
Format the data into instruction, chat, or completion pairs
Split data into train, validation, and test sets
Set up the training environment with compatible GPUs and software
Install PyTorch, Transformers, Accelerate, PEFT, and related libraries
Load the base model in the correct precision
Choose a fine-tuning method such as full fine-tuning, LoRA, or QLoRA
Configure tokenizer, padding, and special tokens
Set training hyperparameters such as batch size, learning rate, and epochs
Enable gradient accumulation if GPU memory is limited
Use mixed precision to reduce memory usage
Apply checkpointing to save training progress
Train on the prepared dataset
Monitor loss, validation metrics, and overfitting
Evaluate the fine-tuned model on held-out examples
Run safety and quality checks on generated outputs
Merge LoRA adapters if needed
Save and export the final model and tokenizer
Deploy the model in your inference stack
Iterate with more data and retraining if performance is insufficient