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import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig, pipeline, TextIteratorStreamer
import gradio as gr
from torch import bfloat16
from threading import Thread
MODEL_NAME = "HuggingFaceH4/zephyr-7b-beta"
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForCausalLM.from_pretrained(
MODEL_NAME, device_map="auto", torch_dtype=torch.float16, load_in_4bit=True
)
# Chat Interface
system_prompt = "You are a helpful assistant who helps user to complete its query. If you don't know the answer be honet don't provide false information."
def prompt_build(system_prompt, user_inp, hist):
prompt = f"""<|system|>\n{system_prompt}\n"""
for pair in hist:
prompt += f"""<|user|>\n{pair[0]}\n<|assistant|>\n{pair[1]}"""
prompt += f"""<|user|>\n{user_inp}\n<|assistant|>\n"""
return prompt
def chat(user_input, history):
if not user_input:
yield "Please write your query so that I can assist you even better."
return
prompt = prompt_build(system_prompt, user_input, history)
model_inputs = tokenizer([prompt], return_tensors="pt").to("cuda")
streamer = TextIteratorStreamer(tokenizer, timeout=10., skip_prompt=True, skip_special_tokens=True)
generate_kwargs = dict(
model_inputs,
streamer=streamer,
max_new_tokens=2000,
do_sample=True,
top_p=0.7,
temperature=0.8
)
t = Thread(target=model.generate, kwargs=generate_kwargs)
t.start()
model_output = ""
for new_text in streamer:
model_output += new_text
yield model_output
return model_output
with gr.Blocks() as demo:
chatbot = gr.ChatInterface(fn=chat,examples=[["Hello, Good Morning!"],["Who is Virat Kohli?"],["Write an email to Client call Darshan Kholakiya whose email address is darshan.kholakiya@dell.com and address is Alaknanda Building, Rawalapada, Borivali East, Mumbai-400008. My name is Vijay and I am a sales person from Nividia, my phone number is 7710020978 and email is vijay.shah@nividia.com and I want to pitch Darshan to buy semi conductor chips from us."]],
title="Marketing Email Generator")
demo.queue().launch(share=True,debug=True)
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