Tag: LoRA
Debiasing Through Fine-Tuning: Approaches for Safer Large Language Models
Explore how fine-tuning reduces bias in LLMs while balancing safety risks. Learn about LoRA, regularized methods, and real-world implementation strategies for safer AI.
Preventing Catastrophic Forgetting During LLM Fine-Tuning: Techniques That Work
Learn how to stop LLMs from forgetting what they learned during fine-tuning. Explore proven techniques like FIP, EWC, LoRA, and new 2025 methods that actually work-no fluff, just what helps in real applications.
Optimizing Attention Patterns for Domain-Specific Large Language Models
Optimizing attention patterns in domain-specific LLMs improves accuracy by teaching models where to focus within data. LoRA and PEFT methods cut costs and boost performance in healthcare, legal, and finance without full retraining.
