app.py

from transformers import AutoModelForCausalLM, AutoTokenizer
from tokenization_yi import YiTokenizer
import torch
import os
import gradio as gr
import sentencepiece


# Load the model and tokenizer using transformers
model = AutoModelForCausalLM.from_pretrained("01-ai/Yi-34B-200K", device_map="auto", torch_dtype="auto", trust_remote_code=True)
tokenizer = YiTokenizer(vocab_file="./tokenizer.model")

def run(message, chat_history, max_new_tokens=100000, temperature=3.5, top_p=0.9, top_k=800):
    prompt = get_prompt(message, chat_history)

    # Encode the prompt to tensor
    input_ids = tokenizer.encode(prompt, return_tensors='pt')

    # Generate a response using the model with adjusted parameters
    response_ids = model.generate(
        input_ids,
        max_length=max_new_tokens + input_ids.shape[1],
        temperature=temperature,  # Controls randomness. Lower values make text more deterministic.
        top_p=top_p,              # Nucleus sampling: higher values allow more diversity.
        top_k=top_k,              # Top-k sampling: limits the number of top tokens considered.
        pad_token_id=tokenizer.eos_token_id,
        do_sample=True            # Enable sampling-based generation

    )

    # Decode the response
    response = tokenizer.decode(response_ids[:, input_ids.shape[-1]:][0], skip_special_tokens=True)
    return response

def get_prompt(message, chat_history):
    texts = []

    do_strip = False
    for user_input, response in chat_history:
        user_input = user_input.strip() if do_strip else user_input
        do_strip = True
        texts.append(f" {response.strip()} {user_input} ")
    message = message.strip() if do_strip else message
    texts.append(f"{message}")
    return ''.join(texts)

DESCRIPTION = """
# 👋🏻Welcome to 🙋🏻‍♂️Tonic's🧑🏻‍🚀YI-200K🚀"
You can use this Space to test out the current model [Tonic/YI](https://huggingface.co/01-ai/Yi-34B)
You can also use 🧑🏻‍🚀YI-200K🚀 by cloning this space. 🧬🔬🔍 Simply click here: <a style="display:inline-block" href="https://huggingface.co/spaces/Tonic/YiTonic?duplicate=true"><img src="https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14" alt="Duplicate Space"></a></h3> 
Join us : 🌟TeamTonic🌟 is always making cool demos! Join our active builder's🛠️community on 👻Discord: [Discord](https://discord.gg/GWpVpekp) On 🤗Huggingface: [TeamTonic](https://huggingface.co/TeamTonic) & [MultiTransformer](https://huggingface.co/MultiTransformer) On 🌐Github: [Polytonic](https://github.com/tonic-ai) & contribute to 🌟 [PolyGPT](https://github.com/tonic-ai/polygpt-alpha)
"""

MAX_MAX_NEW_TOKENS = 200000
DEFAULT_MAX_NEW_TOKENS = 100000
MAX_INPUT_TOKEN_LENGTH = 100000

def clear_and_save_textbox(message): return '', message

def display_input(message, history=[]):
    history.append((message, ''))
    return history

def delete_prev_fn(history=[]):
    try:
        message, _ = history.pop()
    except IndexError:
        message = ''
    return history, message or ''

def generate(message, history_with_input, max_new_tokens, temperature, top_p, top_k):
    if int(max_new_tokens) > MAX_MAX_NEW_TOKENS:
        raise ValueError

    history = history_with_input[:-1]
    generator = run(message, history, max_new_tokens, temperature, top_p, top_k)
    try:
        first_response = next(generator)
        yield history + [(message, first_response)]
    except StopIteration:
        yield history + [(message, '')]
    for response in generator:
        yield history + [(message, response)]

def process_example(message):
    generator = generate(message, [], 1024, 2.5, 0.95, 900)
    for x in generator:
        pass
    return '', x

def check_input_token_length(message, chat_history):
    input_token_length = len(message) + len(chat_history)
    if input_token_length > MAX_INPUT_TOKEN_LENGTH:
        raise gr.Error(f"The accumulated input is too long ({input_token_length} > {MAX_INPUT_TOKEN_LENGTH}). Clear your chat history and try again.")

with gr.Blocks(theme='ParityError/Anime') as demo:
    gr.Markdown(DESCRIPTION)


    
    with gr.Group():
        chatbot = gr.Chatbot(label='TonicYi-30B-200K')
        with gr.Row():
            textbox = gr.Textbox(
                container=False,
                show_label=False,
                placeholder='Hi, Yi',
                scale=10
            )
            submit_button = gr.Button('Submit', variant='primary', scale=1, min_width=0)

    with gr.Row():
        retry_button = gr.Button('Retry', variant='secondary')
        undo_button = gr.Button('Undo', variant='secondary')
        clear_button = gr.Button('Clear', variant='secondary')

    saved_input = gr.State()

    with gr.Accordion(label='Advanced options', open=False):
#       system_prompt = gr.Textbox(label='System prompt', value=DEFAULT_SYSTEM_PROMPT, lines=5, interactive=False)
        max_new_tokens = gr.Slider(label='Max New Tokens', minimum=1, maximum=MAX_MAX_NEW_TOKENS, step=1, value=DEFAULT_MAX_NEW_TOKENS)
        temperature = gr.Slider(label='Temperature', minimum=0.1, maximum=4.0, step=0.1, value=0.1)
        top_p = gr.Slider(label='Top-P (nucleus sampling)', minimum=0.05, maximum=1.0, step=0.05, value=0.9)
        top_k = gr.Slider(label='Top-K', minimum=1, maximum=1000, step=1, value=10)

    textbox.submit(
        fn=clear_and_save_textbox,
        inputs=textbox,
        outputs=[textbox, saved_input],
        api_name=False,
        queue=False,
    ).then(
        fn=display_input,
        inputs=[saved_input, chatbot],
        outputs=chatbot,
        api_name=False,
        queue=False,
    ).then(
        fn=check_input_token_length,
        inputs=[saved_input, chatbot],
        api_name=False,
        queue=False,
    ).success(
        fn=generate,
        inputs=[
            saved_input,
            chatbot,
            max_new_tokens,
            temperature,
            top_p,
            top_k,
        ],
        outputs=chatbot,
        api_name="Generate",
    )

    button_event_preprocess = submit_button.click(
        fn=clear_and_save_textbox,
        inputs=textbox,
        outputs=[textbox, saved_input],
        api_name=False,
        queue=False,
    ).then(
        fn=display_input,
        inputs=[saved_input, chatbot],
        outputs=chatbot,
        api_name=False,
        queue=False,
    ).then(
        fn=check_input_token_length,
        inputs=[saved_input, chatbot],
        api_name=False,
        queue=False,
    ).success(
        fn=generate,
        inputs=[
            saved_input,
            chatbot,
            max_new_tokens,
            temperature,
            top_p,
            top_k,
        ],
        outputs=chatbot,
        api_name="Cgenerate",
    )

    retry_button.click(
        fn=delete_prev_fn,
        inputs=chatbot,
        outputs=[chatbot, saved_input],
        api_name=False,
        queue=False,
    ).then(
        fn=display_input,
        inputs=[saved_input, chatbot],
        outputs=chatbot,
        api_name=False,
        queue=False,
    ).then(
        fn=generate,
        inputs=[
            saved_input,
            chatbot,
            max_new_tokens,
            temperature,
            top_p,
            top_k,
        ],
        outputs=chatbot,
        api_name=False,
    )

    undo_button.click(
        fn=delete_prev_fn,
        inputs=chatbot,
        outputs=[chatbot, saved_input],
        api_name=False,
        queue=False,
    ).then(
        fn=lambda x: x,
        inputs=[saved_input],
        outputs=textbox,
        api_name=False,
        queue=False,
    )

    clear_button.click(
        fn=lambda: ([], ''),
        outputs=[chatbot, saved_input],
        queue=False,
        api_name=False,
    )

demo.queue(max_size=32).launch(show_api=True)