| 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] | |