app.py

# https://www.gradio.app/guides/using-hugging-face-integrations

import gradio as gr
import logging
import html
from   pprint import pprint
import time
import torch
from   threading import Thread
from   transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, TextIteratorStreamer

# Model
model_name = "augmxnt/shisa-7b-v1"

# UI Settings
title = "Shisa 7B"
description = "Test out Shisa 7B in either English or Japanese. If you aren't getting the right language outputs, you can try changing the system prompt to the appropriate language. Note, we are running `load_in_4bit` to fit in 16GB of VRAM"
placeholder = "Type Here / ここに入力してください" 
examples = [
    ["What are the best slices of pizza in New York City?"],
    ['How do I program a simple "hello world" in Python?'],
    ["東京でおすすめのラーメン屋ってどこ？"],
    ["Pythonでシンプルな「ハローワールド」をプログラムするにはどうすればいいですか？"],
]

# LLM Settings
# Initial
system_prompt = 'You are a helpful, bilingual assistant. Reply in the same language as the user.'
default_prompt = system_prompt

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    # load_in_8bit=True,
    quantization_config = BitsAndBytesConfig(
        load_in_4bit=True,
        bnb_4bit_quant_type='nf4',
        bnb_4bit_use_double_quant=True,
        bnb_4bit_compute_dtype=torch.bfloat16
    ),
)
streamer = TextIteratorStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True)

def chat(message, history, system_prompt):
    print('---')
    pprint(history)
    if not system_prompt:
        system_prompt = default_prompt

    # Let's just rebuild every time it's easier
    chat_history = [{"role": "system", "content": system_prompt}]
    for h in history:
        chat_history.append({"role": "user", "content": h[0]})
        chat_history.append({"role": "assistant", "content": h[1]})
    chat_history.append({"role": "user", "content": message})

    input_ids = tokenizer.apply_chat_template(chat_history, add_generation_prompt=True, return_tensors="pt")

    # for multi-gpu, find the device of the first parameter of the model
    first_param_device = next(model.parameters()).device
    input_ids = input_ids.to(first_param_device)

    generate_kwargs = dict(
        inputs=input_ids,
        streamer=streamer,
        max_new_tokens=200,
        do_sample=True,
        temperature=0.7,
        repetition_penalty=1.15,
        top_p=0.95,
        eos_token_id=tokenizer.eos_token_id,
        pad_token_id=tokenizer.eos_token_id,
    )
    # https://www.gradio.app/main/guides/creating-a-chatbot-fast#example-using-a-local-open-source-llm-with-hugging-face
    t = Thread(target=model.generate, kwargs=generate_kwargs)
    t.start()
    partial_message = ""
    for new_token in streamer:
        partial_message += new_token # html.escape(new_token)
        yield partial_message


chat_interface = gr.ChatInterface(
    chat,
    chatbot=gr.Chatbot(height=400),
    textbox=gr.Textbox(placeholder=placeholder, container=False, scale=7),
    title=title,
    description=description,
    theme="soft",
    examples=examples,
    cache_examples=False,
    undo_btn="Delete Previous",
    clear_btn="Clear",
    additional_inputs=[
        gr.Textbox(system_prompt, label="System Prompt (Change the language of the prompt for better replies)"),
    ],
)

# https://huggingface.co/spaces/ysharma/Explore_llamav2_with_TGI/blob/main/app.py#L219 - we use this with construction b/c Gradio barfs on autoreload otherwise
with gr.Blocks() as demo:
    chat_interface.render()
    gr.Markdown("You can try asking this question in Japanese or English. We limit output to 200 tokens.")

demo.queue().launch()