CorticalStack/neurotic-crown-clown-7b-tak-stack-dpo

neurotic-crown-clown-7b-tak-stack-dpo Overview

CorticalStack/neurotic-crown-clown-7b-tak-stack-dpo is a 7 billion parameter language model that has undergone Direct Preference Optimization (DPO) fine-tuning. This model is built upon the base of CorticalStack/neurotic-crown-clown-7b-ties and leverages the specialized CorticalStack/tak-stack-dpo dataset for its DPO training.

Key Training Details

The DPO fine-tuning process involved specific LoRA (Low-Rank Adaptation) configurations and training arguments:

• LoRA Parameters:
  • r: 32
  • LoRA alpha: 32
  • LoRA dropout: 0.05
• Training Arguments:
  • Batch size: 4
  • Gradient accumulation steps: 4
  • Optimizer: paged_adamw_32bit
  • Max steps: 100
  • Learning rate: 5e-05
  • Learning rate scheduler type: cosine
  • Beta: 0.1
  • Max prompt length: 1024
  • Max length: 1536

This DPO-tuned model is designed to exhibit improved performance aligned with the preferences encoded in the tak-stack-dpo dataset, making it suitable for applications where such alignment is critical.