How to use PEFT approach to do Prompt Tuning on DollyV2 model
I am using this link to study about Prompt Tuning.Parameter-Efficient Fine-Tuning using 🤗 PEFTIt has 4 options.LoRA:LORA: LOW-RANK ADAPTATION OF LARGE LANGUAGE MODELSPrefix Tuning:P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and TasksPrompt Tuning:The Power of Scale for Parameter-Efficient Prompt TuningP-Tuning:GPT Understands, TooI am more interested inOption 3. Prompt Tuning: [The Power of Scale for Parameter-Efficient Prompt Tuning]Can I usePrompt Tuningto tune DollyV2 [databricks/dolly-v2-12b · Hugging Face] model.The use-case is:I haveContextwhich has lot of paragraphs and thenQuestion, the model has toanswertheQuestionbased onContextin a professional manner. Also can it classify theQuestionasrelevantif answer is present inContextandirrelevantifansweris not inContextLike this:1 data sample:{Context:How to Link Credit Card to ICICI Bank Account Step 1: Login to ICICIBank.com using your existing internet banking credentials. Step 2: Go to the 'Service Request' section. Step 3: Visit the 'Customer Service' option. Step 4: Select the Link Accounts/ Policy option to link your credit card to the existing user ID.Question: How to add card?Prompt: Answer the question as truthfully as possible using and only using the provided context and if the answer is not contained within the context/text, say IrrelevantAnswer:Relevant.  To add your card you can follow these steps:Step 1: Login to ICICIBank.com using your existing internet banking credentials.
Step 2: Go to the 'Service Request' section.
Step 3: Visit the 'Customer Service' option.
Step 4: Select the Link Accounts/ Policy option to link your credit card to the existing user ID.```
}

The idea is since **dollyv2** is huge model, so want to explore `prompt tuning`.