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import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM, TextIteratorStreamer
import torch
from threading import Thread
MODEL_ID = "HODACHI/EZO-Common-9B-gemma-2-it"
DTYPE = torch.bfloat16
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
model = AutoModelForCausalLM.from_pretrained(
MODEL_ID,
device_map="cuda",
torch_dtype=DTYPE,
)
def respond(
message,
history: list[tuple[str, str]],
max_tokens,
temperature,
top_p,
):
chat = []
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)
inputs = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt").to(model.device)
streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
generation_kwargs = dict(
input_ids=inputs,
max_new_tokens=max_tokens,
temperature=temperature,
top_p=top_p,
do_sample=True,
streamer=streamer,
)
thread = Thread(target=model.generate, kwargs=generation_kwargs)
thread.start()
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() |