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import os
import requests
import torch
from bs4 import BeautifulSoup
from peft import PeftConfig, PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
generation_config = GenerationConfig(temperature=.8,
top_p=0.75,
top_k=40)
def extract_text(url: str):
print(['extract_text', 'start'])
if url is None or url.strip() == '':
return ''
response = requests.get(url)
soup = BeautifulSoup(response.text, "html.parser")
text = '\n\n'.join(map(lambda p: p.text, soup.find_all('p')))
print(['extract_text', 'end'])
return text
def summarize_text(text: str):
print(['summarize_text', 'start'])
input_text = f'<s>Instruction: Elabora un resume del siguiente texto.\nInput: {text}\nOutput: '
batch = tokenizer(input_text, return_tensors='pt')
print(['summarize_text', 'generating'])
with torch.cuda.amp.autocast():
output_tokens = model.generate(**batch,
max_new_tokens=256,
generation_config=generation_config
)
output = tokenizer.decode(output_tokens[0], skip_special_tokens=True)
print(['summarize_text', 'end'])
return output
def load_model(peft_model_id):
print(['load_model', 'start'])
config = PeftConfig.from_pretrained(peft_model_id)
print(['load_model', 'loading model'])
model = AutoModelForCausalLM.from_pretrained(
config.base_model_name_or_path, return_dict=True, load_in_8bit=True, device_map='auto')
print(['load_model', 'loading tokenizer'])
tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
model = PeftModel.from_pretrained(model, peft_model_id)
model.config.use_cache = True
print(['load_model', 'end'])
return model, tokenizer
model, tokenizer = load_model(
"hackathon-somos-nlp-2023/opt-6.7b-lora-sag-t3000-v300-v2")
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