|
from huggingface_hub import InferenceClient |
|
import gradio as gr |
|
from transformers import AutoModelForCausalLM, AutoTokenizer |
|
import torch |
|
|
|
|
|
tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-large", padding_side="left") |
|
model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-large") |
|
|
|
client = InferenceClient( |
|
"HuggingFaceH4/zephyr-7b-alpha" |
|
) |
|
|
|
def format_prompt(message, history): |
|
system = "\nYou are a helpful virtual assistant that answers user's questions with easy-to-understand words.</s>\n" |
|
prompt = "" |
|
for user_prompt, bot_response in history: |
|
prompt += f"\n{user_prompt}</s>\n" |
|
prompt += f"\n{bot_response}</s>\n" |
|
prompt += f"\n{message}</s>\n" |
|
return prompt |
|
|
|
def generate( |
|
prompt, |
|
history, |
|
temperature=0.9, |
|
max_new_tokens=500, |
|
top_p=0.95, |
|
repetition_penalty=1.0, |
|
): |
|
temperature = float(temperature) |
|
if temperature < 1e-2: |
|
temperature = 1e-2 |
|
top_p = float(top_p) |
|
|
|
generate_kwargs = dict( |
|
temperature=temperature, |
|
max_new_tokens=max_new_tokens, |
|
top_p=top_p, |
|
repetition_penalty=repetition_penalty, |
|
do_sample=True, |
|
seed=42, |
|
) |
|
|
|
formatted_prompt = format_prompt(prompt, history) |
|
|
|
stream = client.text_generation( |
|
formatted_prompt, |
|
**generate_kwargs, |
|
stream=True, |
|
details=True, |
|
return_full_text=False, |
|
) |
|
output = "" |
|
|
|
for response in stream: |
|
output += response.token.text |
|
yield output |
|
return output |
|
|
|
additional_inputs = [ |
|
gr.Slider( |
|
label="Temperature", |
|
value=0.9, |
|
minimum=0.0, |
|
maximum=1.0, |
|
step=0.05, |
|
interactive=True, |
|
info="Higher values produce more diverse outputs", |
|
), |
|
gr.Slider( |
|
label="Max new tokens", |
|
value=256, |
|
minimum=0, |
|
maximum=1048, |
|
step=64, |
|
interactive=True, |
|
info="The maximum number of new tokens", |
|
), |
|
gr.Slider( |
|
label="Top-p (nucleus sampling)", |
|
value=0.90, |
|
minimum=0.0, |
|
maximum=1, |
|
step=0.05, |
|
interactive=True, |
|
info="Higher values sample more low-probability tokens", |
|
), |
|
gr.Slider( |
|
label="Repetition penalty", |
|
value=1.2, |
|
minimum=1.0, |
|
maximum=2.0, |
|
step=0.05, |
|
interactive=True, |
|
info="Penalize repeated tokens", |
|
), |
|
] |
|
|
|
css = """ |
|
#mkd { |
|
height: 500px; |
|
overflow: auto; |
|
border: 1px solid #ccc; |
|
} |
|
""" |
|
|
|
with gr.Blocks(css=css) as inf: |
|
gr.HTML("<h1><center>DialoGPT-large<h1><center>") |
|
gr.HTML( |
|
"<h3><center>In this demo, you can chat with <a href='https://huggingface.co/microsoft/DialoGPT-large'>DialoGPT-large</a> model. 💬<h3><center>" |
|
) |
|
gr.ChatInterface( |
|
generate, |
|
additional_inputs=additional_inputs, |
|
examples=[ |
|
["Can a squirrel swim?"], |
|
["Write a poem about a squirrel."], |
|
], |
|
) |
|
|
|
inf.queue().launch(share=True) |
