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#!/usr/bin/env python
import os
from threading import Thread
from typing import Iterator
import gradio as gr
import spaces
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
DESCRIPTION = "# CALM2-7B-chat"
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", "32768"))
if torch.cuda.is_available():
model_id = "cyberagent/calm2-7b-chat"
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_id)
def apply_chat_template(conversation: list[dict[str, str]]) -> str:
prompt = "\n".join([f"{c['role']}: {c['content']}" for c in conversation])
prompt = f"{prompt}\nASSISTANT: "
return prompt
@spaces.GPU
@torch.inference_mode()
def generate(
message: str,
chat_history: list[tuple[str, 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 = []
for user, assistant in chat_history:
conversation.extend([{"role": "USER", "content": user}, {"role": "ASSISTANT", "content": assistant}])
conversation.append({"role": "USER", "content": message})
prompt = apply_chat_template(conversation)
input_ids = tokenizer.encode(prompt, add_special_tokens=False, 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=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)
chat_interface = gr.ChatInterface(
fn=generate,
chatbot=gr.Chatbot(show_label=False, layout="panel", height=600),
additional_inputs_accordion_name="詳細設定",
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=0.7,
),
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=[
["東京の観光名所を教えて。"],
["落武者って何？"],
["暴れん坊将軍って誰のこと？"],
["人がヘリを食べるのにかかる時間は？"],
],
)
with gr.Blocks(css="style.css") as demo:
gr.Markdown(DESCRIPTION)
gr.DuplicateButton(
value="Duplicate Space for private use",
elem_id="duplicate-button",
visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1",
)
chat_interface.render()
if __name__ == "__main__":
demo.queue(max_size=20).launch()
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