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import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM, TextIteratorStreamer, pipeline
import torch
from threading import Thread
MODEL_ID = "HODACHI/Llama-3.1-8B-EZO-1.1-it"
DTYPE = torch.bfloat16
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
model = AutoModelForCausalLM.from_pretrained(
MODEL_ID,
torch_dtype=DTYPE,
device_map="auto",
low_cpu_mem_usage=True,
)
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
device_map="auto",
)
def generate_text(prompt, max_new_tokens, temperature, top_p):
streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
generation_kwargs = dict(
max_new_tokens=max_new_tokens,
temperature=temperature,
top_p=top_p,
do_sample=True,
streamer=streamer,
)
thread = Thread(target=pipe, kwargs=dict(text_inputs=prompt, **generation_kwargs))
thread.start()
return streamer
def respond(message, history, max_tokens, temperature, top_p):
chat = []
chat.append({"role": "system", "content": "あなたは誠実で優秀な日本人のアシスタントです。特に指示が無い場合は、原則日本語で回答してください。"})
for user, assistant in history:
chat.append({"role": "user", "content": user})
chat.append({"role": "assistant", "content": assistant})
chat.append({"role": "user", "content": message})
prompt = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
streamer = generate_text(prompt, max_tokens, temperature, top_p)
response = ""
for new_text in streamer:
response += new_text
yield response
demo = gr.ChatInterface(
respond,
additional_inputs=[
gr.Slider(minimum=1, maximum=2048, value=150, 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 (nucleus sampling)"),
],
)
if __name__ == "__main__":
demo.launch() |