Fine-tuning is the process of adapting a pre-trained model to perform better on specific tasks by training it on domain-specific data. Learn how to fine-tune Jamba models using different approaches including full fine-tuning, LoRA, and QLoRA
Load Model and Tokenizer
Configure LoRA Parameters
Prepare Your Dataset
Configure Training Settings
Initialize and Start Training
messages
column), so SFTTrainer
automatically applies Jamba’s chat template. For more information about supported dataset formats and advanced SFTTrainer features, see the TRL documentation.Initialize Tokenizer and Configure Quantization
Load Model with Quantization
Configure LoRA Parameters
Prepare Your Dataset
Configure Training Settings
Initialize and Start Training