Fine-Tuning Techniques for LLMs 🛠️
Explore various techniques and methods to fine-tune Large Language Models for specialized tasks and domains.
Task-Specific Fine-Tuning
Adapt LLMs for specific tasks like text classification or summarization.
Transfer Learning
Leverage pre-trained models and transfer them to new domains or tasks.
Domain-Specific Fine-Tuning
Fine-tune LLMs on specialized datasets for fields like law or medicine.
Multitask Fine-Tuning
Train models on multiple tasks simultaneously to improve performance.
Parameter Efficient Fine-Tuning
Explore methods like LoRA, Adapters, and prefix tuning to save computation.
Continual Fine-Tuning
Update models continuously as new data becomes available.
Dataset Selection & Preparation
Prepare and curate datasets for effective fine-tuning of models.