metadata

library_name: peft
base_model: bigscience/bloom-3b

Low Rank Adapter for Bloom decoder for question answering

Example usage:

import torch
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer
from IPython.display import display, Markdown

peft_model_id = "Jayveersinh-Raj/bloom-que-ans"
config = PeftConfig.from_pretrained(peft_model_id)
model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, return_dict=True, load_in_8bit=False, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)

# Load the Lora model
qa_model = PeftModel.from_pretrained(model, peft_model_id)

def make_inference(context, question):
  batch = tokenizer(f"### CONTEXT\n{context}\n\n### QUESTION\n{question}\n\n### ANSWER\n", return_tensors='pt').to("cuda")

 with torch.cuda.amp.autocast():
   output_tokens = qa_model.generate(**batch, max_new_tokens=200)

 display(Markdown((tokenizer.decode(output_tokens[0], skip_special_tokens=True))))

context = ""
question = "What is the best food?"
make_inference(context, question)