ted

app.py

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+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)