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from transformers import AutoModelForCausalLM, AutoTokenizer, StoppingCriteria, StoppingCriteriaList
import torch
import json
import json
import re
import numpy as np
def create_prompt(text, template, examples):
template = json.dumps(json.loads(template),indent = 4)
prompt = "<|input|>\n### Template:\n"+template+"\n"
if examples[0]:
example1 = json.dumps(json.loads(examples[0]),indent = 4)
prompt+= "### Example:\n"+example1+"\n"
if examples[1]:
example2 = json.dumps(json.loads(examples[1]),indent = 4)
prompt+= "### Example:\n"+example1+"\n"
if examples[2]:
example3 = json.dumps(json.loads(examples[1]),indent = 4)
prompt+= "### Example:\n"+example3+"\n"
prompt += "### Text:\n"+text+'''\n<|output|>'''
return prompt
def generate_answer_short(prompt,model, tokenizer):
model_input = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=3000).to("cuda")
with torch.no_grad():
gen = tokenizer.decode(model.generate(**model_input, max_new_tokens=1500)[0], skip_special_tokens=True)
print(gen.split("<|output|>")[1].split("<|end-output|>")[0])
return gen.split("<|output|>")[1].split("<|end-output|>")[0]
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