glm-chat / app.py
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Update app.py
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import subprocess
subprocess.run(
'pip install flash-attn --no-build-isolation',
env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"},
shell=True
)
import torch
from PIL import Image
import gradio as gr
import spaces
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer, StoppingCriteriaList, StoppingCriteria
import os
from threading import Thread
HF_TOKEN = os.environ.get("HF_TOKEN", None)
MODEL_LIST = "THUDM/LongWriter-glm4-9b"
#MODELS = os.environ.get("MODELS")
#MODEL_NAME = MODELS.split("/")[-1]
TITLE = "<h1><center>GLM SPACE</center></h1>"
PLACEHOLDER = f'<h3><center>LongWriter-glm4-9b is trained based on glm-4-9b, and is capable of generating 10,000+ words at once.</center></h3>'
CSS = """
.duplicate-button {
margin: auto !important;
color: white !important;
background: black !important;
border-radius: 100vh !important;
}
"""
model = AutoModelForCausalLM.from_pretrained(
"THUDM/LongWriter-glm4-9b",
torch_dtype=torch.bfloat16,
device_map="auto",
trust_remote_code=True,
).eval()
tokenizer = AutoTokenizer.from_pretrained("THUDM/LongWriter-glm4-9b",trust_remote_code=True, use_fast=False)
class StopOnTokens(StoppingCriteria):
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
# stop_ids = model.config.eos_token_id
stop_ids = [tokenizer.eos_token_id, tokenizer.get_command("<|user|>"),
tokenizer.get_command("<|observation|>")]
for stop_id in stop_ids:
if input_ids[0][-1] == stop_id:
return True
return False
@spaces.GPU()
def stream_chat(message: str, history: list, temperature: float, max_new_tokens: int):
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})
print(f"Conversation is -\n{conversation}")
stop = StopOnTokens()
input_ids = tokenizer.build_chat_input(message, history=conversation, role='user').input_ids.to(next(model.parameters()).device)
#input_ids = tokenizer.apply_chat_template(conversation, tokenize=True, add_generation_prompt=True, return_tensors="pt", return_dict=True).to(model.device)
streamer = TextIteratorStreamer(tokenizer, timeout=60.0, skip_prompt=True, skip_special_tokens=True)
eos_token_id = [tokenizer.eos_token_id, tokenizer.get_command("<|user|>"),
tokenizer.get_command("<|observation|>")]
generate_kwargs = dict(
input_ids=input_ids,
streamer=streamer,
max_new_tokens=max_new_tokens,
do_sample=True,
top_k=1,
temperature=temperature,
repetition_penalty=1,
stopping_criteria=StoppingCriteriaList([stop]),
eos_token_id=eos_token_id,
)
#gen_kwargs = {**input_ids, **generate_kwargs}
thread = Thread(target=model.generate, kwargs=generate_kwargs)
thread.start()
buffer = ""
for new_token in streamer:
if new_token and '<|user|>' not in new_token:
buffer += new_token
yield buffer
chatbot = gr.Chatbot(height=600, placeholder = PLACEHOLDER)
with gr.Blocks(css=CSS) 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.Slider(
minimum=0,
maximum=1,
step=0.1,
value=0.5,
label="Temperature",
render=False,
),
gr.Slider(
minimum=1024,
maximum=32768,
step=1,
value=4096,
label="Max New Tokens",
render=False,
),
],
examples=[
["Help me study vocabulary: write a sentence for me to fill in the blank, and I'll try to pick the correct option."],
["What are 5 creative things I could do with my kids' art? I don't want to throw them away, but it's also so much clutter."],
["Tell me a random fun fact about the Roman Empire."],
["Show me a code snippet of a website's sticky header in CSS and JavaScript."],
],
cache_examples=False,
)
if __name__ == "__main__":
demo.launch()