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52cb1af bf44e50 0ce09fc bf44e50 52cb1af 7a5a1bc bf44e50 dbd9832 0ce09fc bf44e50 114444e dbd9832 114444e dbd9832 07f635e dbd9832 0ce09fc d288670 dbd9832 0ce09fc bf44e50 769e23e bf44e50 52cb1af 114444e bf44e50 52cb1af | 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 | from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from transformers import pipeline
import torch
import gradio as gr
from huggingface_hub import InferenceClient
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
# 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 generateZep(inputuno, inputdos):
prompt = inputuno
promptdos = inputdos
generate_kwargs = dict(
temperature=0.9,
max_new_tokens=3556,
top_p=float(0.95),
repetition_penalty=1.0,
do_sample=True,
seed=42,
)
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)
},
]
stream = client.text_generation(messages, **generate_kwargs, stream=True, details=True, return_full_text=False)
output = ""
for response in stream:
output += response.token.text
yield output
return output
#
# 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(fn=generateZep, inputs=[input_prompt, input_promptdos], outputs=output_component, examples=examples, title="🗿 PerfectGPT v1 🗿", description=description)
PerfectGPT.launch()
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