LLM Fine-tuning with RLHF (Pre-trained Model)
I applied RLHF-style fine-tuning to a pre-trained transformer model, focusing on aligning outputs with human preferences. The process involved the training of a reward model using PyTorch and evaluating alignment improvements via response ranking and prompt-based testing. This work resulted in a 30% reduction in low-quality model responses, supporting improved AI alignment for deployment. • Conducted iterative RLHF fine-tuning cycles on LLM outputs • Trained reward models using human preference data • Evaluated model performance via ranking and prompt-response analysis • Used distributed clusters for scalable experimentation