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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM | |
from transformers import pipeline | |
import torch | |
import gradio as gr | |
# chatgpt-gpt4-prompts-bart-large-cnn-samsum | |
tokenizer = AutoTokenizer.from_pretrained( | |
"Kaludi/chatgpt-gpt4-prompts-bart-large-cnn-samsum") | |
model = AutoModelForSeq2SeqLM.from_pretrained( | |
"Kaludi/chatgpt-gpt4-prompts-bart-large-cnn-samsum", from_tf=True) | |
# zephyr | |
pipe = pipeline("text-generation", model="HuggingFaceH4/zephyr-7b-alpha",torch_dtype=torch.bfloat16, device_map="auto") | |
def generate(inputuno, inputdos, max_new_tokens=3556, top_p=0.95, repetition_penalty=1.0): | |
top_p = float(top_p) | |
prompt = inputuno | |
promptdos = inputdos | |
generate_kwargs = dict( | |
temperature=fixed_temperature, | |
max_new_tokens=max_new_tokens, | |
top_p=top_p, | |
repetition_penalty=repetition_penalty, | |
do_sample=True, | |
seed=42, | |
) | |
messages = [ | |
{ | |
"role": "system", "content": str(new_prompt) | |
}, | |
{ | |
"role": "user", "content": str(promptdos) | |
}, | |
] | |
stream = pipe.tokenizer.apply_chat_template(messages, **generate_kwargs, stream=True, details=True, return_full_text=False) | |
output = "" | |
for response in stream: | |
output += response.token.text | |
yield output | |
return output | |
def generatePrompt(inputuno, inputdos): | |
prompt = inputuno | |
promptdos = inputdos | |
batch = tokenizer(prompt, return_tensors="pt") | |
generated_ids = model.generate(batch["input_ids"]) | |
output = tokenizer.batch_decode(generated_ids, skip_special_tokens=True) | |
new_prompt = output[0] | |
messages = [ | |
{ | |
"role": "system", "content": str(new_prompt) | |
}, | |
{ | |
"role": "user", "content": str(promptdos) | |
}, | |
] | |
# https://huggingface.co./docs/transformers/main/en/chat_templating | |
final_prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) | |
outputs = pipe(final_prompt, do_sample=True,) | |
return outputs[0]["generated_text"] | |
# | |
# Interface | |
input_prompt = gr.Textbox(label="Actua como: ", value="Chef") | |
input_promptdos = gr.Textbox(label="Prompt: ", value="Recipe for ham croquettes") | |
output_component = gr.Textbox(label="Output: ") | |
examples = [["photographer"], ["developer"], ["teacher"], [ | |
"human resources staff"], ["recipe for ham croquettes"]] | |
description = "" | |
PerfectGPT = gr.Interface(generate, inputs=[input_prompt, input_promptdos], outputs=output_component, examples=examples, title="๐ฟ PerfectGPT v1 ๐ฟ", description=description) | |
PerfectGPT.launch() | |