Spaces:
Running
on
Zero
Running
on
Zero
File size: 5,357 Bytes
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import torch
import gradio as gr
import spaces
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
import os
from threading import Thread
import random
from datasets import load_dataset
HF_TOKEN = os.environ.get("HF_TOKEN", None)
MODEL_ID = "CohereForAI/c4ai-command-r7b-12-2024"
MODELS = os.environ.get("MODELS")
MODEL_NAME = MODEL_ID.split("/")[-1]
TITLE = "<h1><center>์๋ก์ด ์ผ๋ณธ์ด LLM ๋ชจ๋ธ ์น UI</center></h1>"
DESCRIPTION = f"""
<h3>๋ชจ๋ธ: <a href="https://huggingface.co/CohereForAI/c4ai-command-r7b-12-2024">CohereForAI/c4ai-command-r7b-12-2024</a></h3>
<center>
<p>
<br>
cc-by-nc
</p>
</center>
"""
CSS = """
.duplicate-button {
margin: auto !important;
color: white !important;
background: black !important;
border-radius: 100vh !important;
}
h3 {
text-align: center;
}
.chatbox .messages .message.user {
background-color: #e1f5fe;
}
.chatbox .messages .message.bot {
background-color: #eeeeee;
}
"""
# ๋ชจ๋ธ๊ณผ ํ ํฌ๋์ด์ ๋ก๋
model = AutoModelForCausalLM.from_pretrained(
MODEL_ID,
torch_dtype=torch.bfloat16,
device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
# ๋ฐ์ดํฐ์
๋ก๋
dataset = load_dataset("elyza/ELYZA-tasks-100")
print(dataset)
split_name = "train" if "train" in dataset else "test"
examples_list = list(dataset[split_name])
examples = random.sample(examples_list, 50)
example_inputs = [[example['input']] for example in examples]
@spaces.GPU
def stream_chat(message: str, history: list, temperature: float, max_new_tokens: int, top_p: float, top_k: int, penalty: float):
print(f'message is - {message}')
print(f'history is - {history}')
conversation = []
for prompt, answer in history:
conversation.extend([{"role": "user", "content": prompt}, {"role": "assistant", "content": answer}])
conversation.append({"role": "user", "content": message})
input_ids = tokenizer.apply_chat_template(conversation, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(input_ids, return_tensors="pt").to(0)
streamer = TextIteratorStreamer(tokenizer, timeout=10., skip_prompt=True, skip_special_tokens=True)
generate_kwargs = dict(
inputs,
streamer=streamer,
top_k=top_k,
top_p=top_p,
repetition_penalty=penalty,
max_new_tokens=max_new_tokens,
do_sample=True,
temperature=temperature,
eos_token_id=[255001],
)
thread = Thread(target=model.generate, kwargs=generate_kwargs)
thread.start()
buffer = ""
for new_text in streamer:
buffer += new_text
yield buffer
chatbot = gr.Chatbot(height=500)
with gr.Blocks(css=CSS) as demo:
gr.HTML(TITLE)
gr.HTML(DESCRIPTION)
gr.ChatInterface(
fn=stream_chat,
chatbot=chatbot,
fill_height=True,
theme="soft",
additional_inputs_accordion=gr.Accordion(label="โ๏ธ ๋งค๊ฐ๋ณ์", open=False, render=False),
additional_inputs=[
gr.Slider(
minimum=0,
maximum=1,
step=0.1,
value=0.8,
label="์จ๋",
render=False,
),
gr.Slider(
minimum=128,
maximum=1000000,
step=1,
value=100000,
label="์ต๋ ํ ํฐ ์",
render=False,
),
gr.Slider(
minimum=0.0,
maximum=1.0,
step=0.1,
value=0.8,
label="์์ ํ๋ฅ ",
render=False,
),
gr.Slider(
minimum=1,
maximum=20,
step=1,
value=20,
label="์์ K",
render=False,
),
gr.Slider(
minimum=0.0,
maximum=2.0,
step=0.1,
value=1.0,
label="๋ฐ๋ณต ํจ๋ํฐ",
render=False,
),
],
examples=[
["์์ด์ ์ฌ๋ฆ๋ฐฉํ ๊ณผํ ํ๋ก์ ํธ๋ฅผ ์ํ 5๊ฐ์ง ์์ด๋์ด๋ฅผ ์ฃผ์ธ์."],
["๋งํฌ๋ค์ด์ ์ฌ์ฉํ์ฌ ๋ธ๋ ์ดํฌ์์ ๊ฒ์ ๋ง๋ค๊ธฐ ํํ ๋ฆฌ์ผ์ ์์ฑํด์ฃผ์ธ์."],
["์ด๋ฅ๋ ฅ์ ๊ฐ์ง ์ฃผ์ธ๊ณต์ SF ์ด์ผ๊ธฐ ์๋๋ฆฌ์ค๋ฅผ ์์ฑํด์ฃผ์ธ์. ๋ณต์ ์ค์ , ํ
๋ง์ ๋ก๊ทธ๋ผ์ธ์ ๋
ผ๋ฆฌ์ ์ผ๋ก ์ฌ์ฉํด์ฃผ์ธ์"],
["์์ด์ ์ฌ๋ฆ๋ฐฉํ ์์ ์ฐ๊ตฌ๋ฅผ ์ํ 5๊ฐ์ง ์์ด๋์ด์ ๊ทธ ๋ฐฉ๋ฒ์ ๊ฐ๋จํ ์๋ ค์ฃผ์ธ์."],
["ํผ์ฆ ๊ฒ์ ์คํฌ๋ฆฝํธ ์์ฑ์ ์ํ ์กฐ์ธ ๋ถํ๋๋ฆฝ๋๋ค"],
["๋งํฌ๋ค์ด ํ์์ผ๋ก ๋ธ๋ก ๊นจ๊ธฐ ๊ฒ์ ์ ์ ๊ต๊ณผ์๋ฅผ ์์ฑํด์ฃผ์ธ์"],
["์ค๋ฒ ๅทๆณ๋ฅผ ์๊ฐํด์ฃผ์ธ์"],
["์ผ๋ณธ์ด ๊ด์ฉ๊ตฌ, ์๋ด์ ๊ดํ ์ํ ๋ฌธ์ ๋ฅผ ๋ง๋ค์ด์ฃผ์ธ์"],
["๋๋ผ์๋ชฝ์ ๋ฑ์ฅ์ธ๋ฌผ์ ์๋ ค์ฃผ์ธ์"],
["์ค์ฝ๋
ธ๋ฏธ์ผํค ๋ง๋๋ ๋ฐฉ๋ฒ์ ์๋ ค์ฃผ์ธ์"],
["๋ฌธ์ 9.11๊ณผ 9.9 ์ค ์ด๋ ๊ฒ์ด ๋ ํฐ๊ฐ์? step by step์ผ๋ก ๋
ผ๋ฆฌ์ ์ผ๋ก ์๊ฐํด์ฃผ์ธ์."],
],
cache_examples=False,
)
if __name__ == "__main__":
demo.launch() |