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import gradio as gr
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline, StoppingCriteria, StoppingCriteriaList, TextIteratorStreamer
import time
import numpy as np
from torch.nn import functional as F
import os
from threading import Thread
print(f"Starting to load the model to memory")
m = AutoModelForCausalLM.from_pretrained(
"stabilityai/stablelm-2-1_6b-zephyr", torch_dtype=torch.float16, trust_remote_code=True)
tok = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-1_6b-zephyr", trust_remote_code=True)
generator = pipeline('text-generation', model=m, tokenizer=tok)
print(f"Sucessfully loaded the model to the memory")
start_message = ""
def user(message, history):
# Append the user's message to the conversation history
return "", history + [[message, ""]]
def chat(history):
chat = []
for item in history:
chat.append({"role": "user", "content": item[0]})
if item[1] is not None:
chat.append({"role": "assistant", "content": item[0]})
messages = tokenizer.apply_chat_template(chat, tokenize=False)
# Tokenize the messages string
model_inputs = tok([messages], return_tensors="pt")
streamer = TextIteratorStreamer(
tok, timeout=10., skip_prompt=True, skip_special_tokens=True)
generate_kwargs = dict(
model_inputs,
streamer=streamer,
max_new_tokens=1024,
do_sample=True,
top_p=0.95,
top_k=1000,
temperature=0.75,
num_beams=1,
)
t = Thread(target=m.generate, kwargs=generate_kwargs)
t.start()
# print(history)
# Initialize an empty string to store the generated text
partial_text = ""
for new_text in streamer:
# print(new_text)
partial_text += new_text
history[-1][1] = partial_text
# Yield an empty string to cleanup the message textbox and the updated conversation history
yield history
return partial_text
with gr.Blocks() as demo:
# history = gr.State([])
gr.Markdown("## Stable LM 1.6b Zephyr")
gr.HTML('''<center><a href="https://huggingface.co/spaces/stabilityai/stablelm-2-1_6b-zephyr?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>Duplicate the Space to skip the queue and run in a private space</center>''')
chatbot = gr.Chatbot().style(height=500)
with gr.Row():
with gr.Column():
msg = gr.Textbox(label="Chat Message Box", placeholder="Chat Message Box",
show_label=False).style(container=False)
with gr.Column():
with gr.Row():
submit = gr.Button("Submit")
stop = gr.Button("Stop")
clear = gr.Button("Clear")
submit_event = msg.submit(fn=user, inputs=[msg, chatbot], outputs=[msg, chatbot], queue=False).then(
fn=chat, inputs=[chatbot], outputs=[chatbot], queue=True)
submit_click_event = submit.click(fn=user, inputs=[msg, chatbot], outputs=[msg, chatbot], queue=False).then(
fn=chat, inputs=[chatbot], outputs=[chatbot], queue=True)
stop.click(fn=None, inputs=None, outputs=None, cancels=[
submit_event, submit_click_event], queue=False)
clear.click(lambda: None, None, [chatbot], queue=False)
demo.queue(max_size=32, concurrency_count=2)
demo.launch()
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