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import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
from threading import Thread
import gradio as gr
try:
import spaces
except:
class spaces:
@staticmethod
def GPU(duration: int):
return lambda x: x
MODEL_NAME = "hatakeyama-llm-team/Tanuki-8B-Instruct"
model = AutoModelForCausalLM.from_pretrained(
MODEL_NAME, load_in_8bit=True, device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
print(model.hf_device_map)
@spaces.GPU(duration=10)
def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p,
top_k,
):
messages = [{"role": "system", "content": system_message}]
for val in history:
if val[0]:
messages.append({"role": "user", "content": val[0]})
if val[1]:
messages.append({"role": "assistant", "content": val[1]})
messages.append({"role": "user", "content": message})
tokenized_input = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, tokenize=True, return_tensors="pt"
).to(model.device)
streamer = TextIteratorStreamer(
tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True
)
generate_kwargs = dict(
input_ids=tokenized_input,
streamer=streamer,
max_new_tokens=max_tokens,
do_sample=True,
temperature=temperature,
top_k=top_k,
top_p=top_p,
num_beams=1,
)
t = Thread(target=model.generate, kwargs=generate_kwargs)
t.start()
partial_message = ""
for new_token in streamer:
partial_message += new_token
yield partial_message
demo = gr.ChatInterface(
respond,
additional_inputs=[
gr.Textbox(
value="以下は、タスクを説明する指示と、文脈のある入力の組み合わせです。要求を適切に満たす応答を書きなさい。",
label="システムプロンプト",
),
gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
gr.Slider(
minimum=0.1,
maximum=1.0,
value=0.95,
step=0.05,
label="Top-p",
),
gr.Slider(minimum=1, maximum=2000, value=200, step=10, label="Top-k"),
],
examples=[
["たぬきってなんですか？"],
["情けは人の為ならずとはどういう意味ですか？"],
["明晰夢とはなんですか？"],
["シュレディンガー方程式とシュレディンガーの猫はどのような関係がありますか？"],
],
)
if __name__ == "__main__":
demo.launch()
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