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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-tuned-alpha-3b", torch_dtype=torch.float16).cuda()
#tok = AutoTokenizer.from_pretrained("stabilityai/stablelm-tuned-alpha-7b")
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
quantization_config = BitsAndBytesConfig(llm_int8_enable_fp32_cpu_offload=True)
tok = AutoTokenizer.from_pretrained("stabilityai/stablelm-tuned-alpha-3b", device_map="auto", load_in_8bit=True, torch_dtype=torch.float16 )
m = AutoModelForCausalLM.from_pretrained("stabilityai/stablelm-tuned-alpha-3b", device_map= "auto", quantization_config=quantization_config,
offload_folder="./")
# generator = pipeline('text-generation', model=m, tokenizer=tok, device=1)
print(f"Sucessfully loaded the model to the memory")
start_message = """<|SYSTEM|># StableAssistant
- StableAssistant is A helpful and harmless Open Source AI Language Model developed by Stability and CarperAI.
- StableAssistant is excited to be able to help the user, but will refuse to do anything that could be considered harmful to the user.
- StableAssistant is more than just an information source, StableAssistant is also able to write poetry, short stories, and make jokes.
- StableAssistant will refuse to participate in anything that could harm a human."""
class StopOnTokens(StoppingCriteria):
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
stop_ids = [50278, 50279, 50277, 1, 0]
for stop_id in stop_ids:
if input_ids[0][-1] == stop_id:
return True
return False
def user(message, history):
# Append the user's message to the conversation history
return "", history + [[message, ""]]
def chat(curr_system_message, history):
# Initialize a StopOnTokens object
stop = StopOnTokens()
# Construct the input message string for the model by concatenating the current system message and conversation history
messages = curr_system_message + \
"".join(["".join(["<|USER|>"+item[0], "<|ASSISTANT|>"+item[1]])
for item in history])
# Tokenize the messages string
model_inputs = tok([messages], return_tensors="pt")
streamer = TextIteratorStreamer(
tok, 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=1.0,
num_beams=1,
stopping_criteria=StoppingCriteriaList([stop])
)
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("## Start your Chat")
gr.HTML('''<center>Enter your text. Inference might be slower. \n<a href="https://www.linkedin.com/in/khadke-chetan/">Follow me</a></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")
system_msg = gr.Textbox(
start_message, label="System Message", interactive=False, visible=False)
submit_event = msg.submit(fn=user, inputs=[msg, chatbot], outputs=[msg, chatbot], queue=False).then(
fn=chat, inputs=[system_msg, chatbot], outputs=[chatbot], queue=True)
submit_click_event = submit.click(fn=user, inputs=[msg, chatbot], outputs=[msg, chatbot], queue=False).then(
fn=chat, inputs=[system_msg, 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() |