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from transformers import AutoModelForCausalLM, AutoTokenizer, AutoConfig, BitsAndBytesConfig, GenerationConfig
import gradio as gr
import torch
title = "????AI ChatBot bajo GPU"
description = "A State-of-the-Art Large-scale Pretrained Response generation model (DialoGPT)"
examples = [["How are you?"]]
model_id="clibrain/Llama-2-13b-ft-instruct-es-gptq-4bit"
config = AutoConfig.from_pretrained(model_id)
#config.quantization_config["use_exllama"] = True
config.quantization_config["disable_exllama"] = True
config.quantization_config["exllama_config"] = {"version":2}
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print("********************")
print(device)
print("********************")
model = AutoModelForCausalLM.from_pretrained(model_id, config=config, torch_dtype=torch.float32) #float 32 es necesario para CPU
#model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto")
model = model.to(device)
tokenizer = AutoTokenizer.from_pretrained(model_id)
def predict(input, history=[]):
# tokenize the new input sentence
new_user_input_ids = tokenizer.encode(
input + tokenizer.eos_token, return_tensors="pt"
).to(device)
# append the new user input tokens to the chat history
historygpu=torch.LongTensor(history).to(device)
bot_input_ids = torch.cat([historygpu, new_user_input_ids], dim=-1)
# generate a response
history = model.generate(
bot_input_ids, max_length=4000, pad_token_id=tokenizer.eos_token_id
)
# convert the tokens to text, and then split the responses into lines
response = tokenizer.decode(history[0]).split("<|endoftext|>")
# print('decoded_response-->>'+str(response))
print(response)
response = [
(response[i], response[i + 1]) for i in range(0, len(response) - 1, 2)
] # convert to tuples of list
# print('response-->>'+str(response))
return response, history
gr.Interface(
fn=predict,
title=title,
description=description,
examples=examples,
inputs=["text", "state"],
outputs=["chatbot", "state"],
theme="finlaymacklon/boxy_violet",
).launch() |