Xenova's picture
Xenova HF staff
Update app.py
16143a7 verified
raw
history blame
4.45 kB
import os
from threading import Thread
from typing import Iterator
import gradio as gr
import spaces
import torch
from transformers import (
AutoModelForCausalLM,
BitsAndBytesConfig,
GemmaTokenizerFast,
TextIteratorStreamer,
)
DESCRIPTION = """\
# Gemma 2 2B IT
Gemma 2 is Google's latest iteration of open LLMs.
This is a demo of [`google/gemma-2-2b-it`](https://huggingface.co/google/gemma-2-2b-it), fine-tuned for instruction following.
For more details, please check [our post](https://huggingface.co/blog/gemma2).
👉 Looking for a larger and more powerful version? Try the 27B version in [HuggingChat](https://huggingface.co/chat/models/google/gemma-2-27b-it) and the 9B version in [this Space](https://huggingface.co/spaces/huggingface-projects/gemma-2-9b-it).
"""
MAX_MAX_NEW_TOKENS = 2048
DEFAULT_MAX_NEW_TOKENS = 1024
MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096"))
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model_id = "gg-hf/gemma-2-2b-it"
tokenizer = GemmaTokenizerFast.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
device_map="auto",
torch_dtype=torch.bfloat16,
quantization_config=BitsAndBytesConfig(load_in_8bit=True),
)
model.config.sliding_window = 4096
model.eval()
@spaces.GPU(duration=90)
def generate(
message: str,
chat_history: list[tuple[str, str]],
max_new_tokens: int = 1024,
temperature: float = 0.6,
top_p: float = 0.9,
top_k: int = 50,
repetition_penalty: float = 1.2,
) -> Iterator[str]:
conversation = []
for user, assistant in chat_history:
conversation.extend(
[
{"role": "user", "content": user},
{"role": "assistant", "content": assistant},
]
)
conversation.append({"role": "user", "content": message})
input_ids = tokenizer.apply_chat_template(conversation, add_generation_prompt=True, return_tensors="pt")
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=20.0, 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)
chat_interface = gr.ChatInterface(
fn=generate,
additional_inputs=[
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=0.6,
),
gr.Slider(
label="Top-p (nucleus sampling)",
minimum=0.05,
maximum=1.0,
step=0.05,
value=0.9,
),
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=1.2,
),
],
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'"],
],
cache_examples=False,
)
with gr.Blocks(css="style.css", fill_height=True) as demo:
gr.Markdown(DESCRIPTION)
gr.DuplicateButton(value="Duplicate Space for private use", elem_id="duplicate-button")
chat_interface.render()
if __name__ == "__main__":
demo.queue(max_size=20).launch()