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)