Tag: catastrophic forgetting

Benchmark Transfer After Fine-Tuning: How LLMs Generalize Across Tasks

by Phillip Ramos

Learn why LLMs lose general skills after fine-tuning and how to fix it. Discover how LoRA and PEFT preserve benchmark transfer, preventing catastrophic forgetting.

Continual Learning in Generative AI: How to Adapt Models Without Catastrophic Forgetting

by Phillip Ramos

Explore how continual learning prevents catastrophic forgetting in generative AI. Learn about experience replay, EWC, and Google's Nested Learning to build adaptive models that retain past knowledge.

Preventing Catastrophic Forgetting During LLM Fine-Tuning: Techniques That Work

by Phillip Ramos

Explore proven techniques to prevent catastrophic forgetting in LLM fine-tuning. We analyze LoRA, EWC, FIP, and hybrid methods to help you preserve model knowledge.

Hyperparameter Selection for Fine-Tuning Large Language Models Without Forgetting

by Phillip Ramos

Learn how to fine-tune large language models without losing their original knowledge. Discover the best hyperparameters, methods like LoRA and FAPM, and real-world trade-offs that keep models accurate and reliable.