Spaces:
Running
on
Zero
Running
on
Zero
import spaces | |
import os | |
import torch | |
from transformers import AutoTokenizer, AutoModelForCausalLM, TextIteratorStreamer | |
import gradio as gr | |
from threading import Thread | |
MODEL = "THUDM/LongWriter-glm4-9b" | |
TITLE = "<h1><center>LongWriter-glm4-9b</center></h1>" | |
PLACEHOLDER = """ | |
<center> | |
<p>Hi! I'm LongWriter-glm4-9b, capable of generating 10,000+ words. How can I assist you today?</p> | |
</center> | |
""" | |
CSS = """ | |
.duplicate-button { | |
margin: auto !important; | |
color: white !important; | |
background: black !important; | |
border-radius: 100vh !important; | |
} | |
h3 { | |
text-align: center; | |
} | |
""" | |
device = "cuda" if torch.cuda.is_available() else "cpu" | |
tokenizer = AutoTokenizer.from_pretrained(MODEL, trust_remote_code=True) | |
model = AutoModelForCausalLM.from_pretrained(MODEL, torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto") | |
model = model.eval() | |
def stream_chat( | |
message: str, | |
history: list, | |
system_prompt: str, | |
temperature: float = 0.5, | |
max_new_tokens: int = 32768, | |
top_p: float = 1.0, | |
top_k: int = 50, | |
): | |
print(f'message: {message}') | |
print(f'history: {history}') | |
chat_history = [] | |
for prompt, answer in history: | |
chat_history.append((prompt, answer)) | |
response, _ = model.chat( | |
tokenizer, | |
message, | |
history=chat_history, | |
max_new_tokens=max_new_tokens, | |
top_p=top_p, | |
top_k=top_k, | |
temperature=temperature, | |
) | |
yield response | |
chatbot = gr.Chatbot(height=600, placeholder=PLACEHOLDER) | |
with gr.Blocks(css=CSS, theme="soft") as demo: | |
gr.HTML(TITLE) | |
gr.DuplicateButton(value="Duplicate Space for private use", elem_classes="duplicate-button") | |
gr.ChatInterface( | |
fn=stream_chat, | |
chatbot=chatbot, | |
fill_height=True, | |
additional_inputs_accordion=gr.Accordion(label="⚙️ Parameters", open=False, render=False), | |
additional_inputs=[ | |
gr.Textbox( | |
value="You are a helpful assistant capable of generating long-form content.", | |
label="System Prompt", | |
render=False, | |
), | |
gr.Slider( | |
minimum=0, | |
maximum=1, | |
step=0.1, | |
value=0.5, | |
label="Temperature", | |
render=False, | |
), | |
gr.Slider( | |
minimum=1024, | |
maximum=32768, | |
step=1024, | |
value=32768, | |
label="Max new tokens", | |
render=False, | |
), | |
gr.Slider( | |
minimum=0.0, | |
maximum=1.0, | |
step=0.1, | |
value=1.0, | |
label="Top p", | |
render=False, | |
), | |
gr.Slider( | |
minimum=1, | |
maximum=100, | |
step=1, | |
value=50, | |
label="Top k", | |
render=False, | |
), | |
], | |
examples=[ | |
["Write a 10000-word comprehensive guide on artificial intelligence and its applications."], | |
["Create a detailed 5000-word business plan for a space tourism company."], | |
["Compose a 3000-word short story about time travel and its consequences."], | |
["Develop a 7000-word research proposal on the potential of quantum computing in cryptography."], | |
], | |
cache_examples=False, | |
) | |
if __name__ == "__main__": | |
demo.launch() | |