text: Title: Comparison of Different Fine-Tuning Techniques for Conversational AI
  Body: ### Feature request
  
  It would be incredibly helpful to have a clear comparison or support for various fine-tuning techniques specifically for conversational AI. This feature could include insights into their strengths, limitations, and ideal use cases, helping practitioners choose the right approach for their needs.
  
  Here’s a list of techniques to consider:
  
  LoRa
  AdaLoRa
  BONE
  VeRa
  XLora
  LN Tuning
  VbLora
  HRA (Hyperparameter Regularization Adapter)
  IA3 (Input-Aware Adapter)
  Llama Adapter
  CPT (Conditional Prompt Tuning)etc
  
  ### Motivation
  
  With the growing number of fine-tuning techniques for conversational AI, it can be challenging to identify the most suitable approach for specific use cases. A comprehensive comparison of these techniques—highlighting their strengths, limitations, and ideal scenarios—would save time, reduce trial-and-error, and empower users to make informed decisions. This feature would bridge the gap between research and practical application, enabling more effective model customization and deployment.
  
  ### Your contribution
  
  I’d be happy to collaborate on this! While I might not have a complete solution right now, I’m willing to contribute by gathering resources, reviewing papers, or helping organize comparisons. If others are interested in teaming up, we could work together on a PR to make this feature happen. Let’s connect and brainstorm how we can tackle this effectively! 