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import gradio as gr | |
from transformers import BloomTokenizerFast, BloomForCausalLM | |
tokenizer = BloomTokenizerFast.from_pretrained("bigscience/bloom-560m") | |
# https://huggingface.co/blog/how-to-generate | |
def generate(text, temp=0.7, logging=True): | |
input_ids = tokenizer.encode(text, return_tensors='pt') | |
output = model.generate( | |
input_ids, | |
do_sample=True, | |
max_length=30, | |
top_p=0.92, | |
top_k=50, | |
temperature=temp, | |
repetition_penalty=1.2, | |
min_length=len(text)+1 | |
) | |
decoded = tokenizer.decode(output[0], skip_special_tokens=True) | |
if logging: | |
print(f"\n\n{'-'*100}\nInput: {text}\nOutput: {decoded}\nTemp: {temp}") | |
return decoded | |
description = "Generate Titles for the Vice Youtube Channel" | |
title = "Vice Headlines" | |
model_name = "marcderbauer/vice-headlines" | |
model = BloomForCausalLM.from_pretrained(model_name) | |
interface = gr.Interface( | |
fn=generate, | |
inputs=['text', gr.Slider(0.01,1, step=0.01, value=0.7, label="Temperature")], | |
outputs='text', | |
examples=[["This Japanese"], ["Why"], ["North Korea"], ["Inside"], ["Spongebob"]], | |
description=description, | |
title=title, | |
) | |
interface.launch() |