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#!/usr/bin/env python
import os
from collections.abc import Iterator
from threading import Thread
import gradio as gr
import spaces
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
DESCRIPTION = "# Zephyr-7B beta"
if not torch.cuda.is_available():
DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>"
MAX_MAX_NEW_TOKENS = 2048
DEFAULT_MAX_NEW_TOKENS = 1024
MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096"))
if torch.cuda.is_available():
model_id = "HuggingFaceH4/zephyr-7b-beta"
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_id)
@spaces.GPU(duration=60) # Limit GPU session to 1 minute for efficiency
def generate(
message: str,
chat_history: list[dict],
system_prompt: str = "",
max_new_tokens: int = 1024,
temperature: float = 0.7,
top_p: float = 0.95,
top_k: int = 50,
repetition_penalty: float = 1.0,
) -> Iterator[str]:
conversation = [{"role": "system", "content": system_prompt}] if system_prompt else []
conversation += [*chat_history, {"role": "user", "content": message}]
input_ids = tokenizer.apply_chat_template(conversation, return_tensors="pt", add_generation_prompt=True)
if input_ids.shape[1] > MAX_INPUT_TOKEN_LENGTH:
input_ids = input_ids[:, -MAX_INPUT_TOKEN_LENGTH:]
gr.Warning(f"Trimmed input from conversation as it was longer than {MAX_INPUT_TOKEN_LENGTH} tokens.")
input_ids = input_ids.to(model.device)
streamer = TextIteratorStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True)
generate_kwargs = dict(
{"input_ids": input_ids},
streamer=streamer,
max_new_tokens=max_new_tokens,
do_sample=True,
top_p=top_p,
top_k=top_k,
temperature=temperature,
num_beams=1,
repetition_penalty=repetition_penalty,
)
t = Thread(target=model.generate, kwargs=generate_kwargs)
t.start()
outputs = []
for text in streamer:
outputs.append(text)
yield "".join(outputs)
demo = gr.ChatInterface(
fn=generate,
additional_inputs=[
gr.Textbox(
label="System prompt",
lines=6,
placeholder="You are a friendly chatbot who always responds in the style of a pirate.",
),
gr.Slider(
label="Max new tokens",
minimum=1,
maximum=MAX_MAX_NEW_TOKENS,
step=1,
value=DEFAULT_MAX_NEW_TOKENS,
),
gr.Slider(
label="Temperature",
minimum=0.1,
maximum=4.0,
step=0.1,
value=0.7,
),
gr.Slider(
label="Top-p (nucleus sampling)",
minimum=0.05,
maximum=1.0,
step=0.05,
value=0.95,
),
gr.Slider(
label="Top-k",
minimum=1,
maximum=1000,
step=1,
value=50,
),
gr.Slider(
label="Repetition penalty",
minimum=1.0,
maximum=2.0,
step=0.05,
value=1.0,
),
],
stop_btn=None,
examples=[
["Hello there! How are you doing?"],
["Can you explain briefly to me what is the Python programming language?"],
["Explain the plot of Cinderella in a sentence."],
["How many hours does it take a man to eat a Helicopter?"],
["Write a 100-word article on 'Benefits of Open-Source in AI research'"],
],
type="messages",
description=DESCRIPTION,
css="""
h1 {
text-align: center;
display: block;
}
#duplicate-button {
margin: auto;
color: white;
background: #1565c0;
border-radius: 100vh;
}
""",
)
if __name__ == "__main__":
demo.queue(max_size=20).launch()
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