I am in the process of fine-tuning Llama2 using SFT trainer and quantization using Lora.
my dataset is composed of questions structured like:
<s>[INST] <<SYS>> Please select the correct answer from the given multiple Options based on the given Context: <</SYS>> Context: Abrasion is another type of mechanical weathering. With abrasion, one rock bumps against another rock. Gravity causes abrasion as a rock tumbles down a slope. Moving water causes abrasion it moves rocks so that they bump against one another (Figure 9.3). Strong winds cause abrasion by blasting sand against rock surfaces. Question: Gravity causes erosion by all of the following except \ Options:(A) glaciers (B) moving air (C) flowing water (D) mass movement Answer: [/INST] D </s>
And I am currently using DataCollatorForCompletionOnlyLM to compute the loss based on the predicted answers.
Do I provide the context, question and options as an instruction_template?
instruction_template = "</SYS>>\n\n Context:" response_template = "Answer: [/INST]" collator = DataCollatorForCompletionOnlyLM(instruction_template=instruction_template, response_template=response_template, tokenizer=tokenizer, mlm=False)
response_template = "Answer: [/INST]" collator = DataCollatorForCompletionOnlyLM(response_template=response_template, tokenizer=tokenizer, mlm=False)
and do I provide the answers with the val dataset? or else how would evaluate the model and get the accuracy or other evaluation metrics?