File size: 2,084 Bytes
60d460f 703c58c 60d460f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 |
from transformers import AutoModelForCausalLM, AutoTokenizer
import time
import datetime
import streamlit as streamlit
question = "Name the planets in the solar system? A: "
question = "Quais são os planetas do sistema solar?"
question = "Qual é o maior planeta do sistema solar?"
before = datetime.datetime.now()
# Use a pipeline as a high-level helper
from transformers import pipeline
messages = [
{"role": "user", "content": question},
]
print('gerando a saida...')
pipe = pipeline("text-generation", model="01-ai/Yi-1.5-34B-Chat")
output = pipe(messages)
st.write(output)
# print('tokenizando...')
# tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)
# print('tokenizado.')
# print('carregando o modelo...')
# # Since transformers 4.35.0, the GPT-Q/AWQ model can be loaded using AutoModelForCausalLM.
# model = AutoModelForCausalLM.from_pretrained(
# model_path,
# device_map="auto",
# torch_dtype='auto'
# ).eval()
# print('modelo carreegado.')
# # Prompt content: "hi"
# messages = [
# {"role": "user", "content": question}
# ]
# print('tokenizando o prompt...')
# input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, return_tensors='pt')
# print('prompt tokenizado.')
# print('gerando a saida...')
# output_ids = model.generate(input_ids, eos_token_id=tokenizer.eos_token_id,
# max_new_tokens=10) #10 # 45
# # max_new_tokens=22)
print('saida gerada.')
# print('Decodificando a saida...')
# response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
# print('saida decodificada.')
# Model response: "Hello! How can I assist you today?"
# print(response)
# question = output['choices'][0]['text'].split('A:')[0]
# answer = output['choices'][0]['text'].split('A:')[1]
# answer = 'A: ' + answer
print('\n\n')
print(question)
print(response)
after = datetime.datetime.now()
current_time = (after - before) # .strftime("%H:%M:%S")
print("\nTime Elapsed: ", current_time)
|