app.py

import time
from threading import Thread
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
import gradio as gr
import torch
import spaces

model_id = "DeepMount00/Llama-3-8b-Ita"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto").eval()  # to("cuda:0")

DESCRIPTION = '''
<div>
<h1 style="text-align: center;">Meta Llama3 8B ITA</h1>
<p>This Space demonstrates the instruction-tuned model <a href="https://huggingface.co/DeepMount00/Llama-3-8b-Ita"><b>Meta Llama3 8b Chat ITA</b></a>.</p>
</div>
<div>
  <p>This model, <strong>DeepMount00/Llama-3-8b-Ita</strong>, is currently the best open-source large language model for the Italian language. You can view its ranking and compare it with other models on the leaderboard at <a href="https://huggingface.co/spaces/FinancialSupport/open_ita_llm_leaderboard"><b>this site</b></a>.</p>
</div>
'''
PLACEHOLDER = """
<div style="padding: 30px; text-align: center; display: flex; flex-direction: column; align-items: center;">
   <img src="https://cdn-avatars.huggingface.co/v1/production/uploads/64f1bf6a8b550e875926a590/9IXg0qMUF0OV2cWPT8cZn.jpeg" style="width: 80%; max-width: 550px; height: auto; opacity: 0.50;  "> 
   <h1 style="font-size: 28px; margin-bottom: 2px; opacity: 0.55;">DeepMount00 llama3</h1>
   <p style="font-size: 18px; margin-bottom: 2px; opacity: 0.65;">Chiedimi qualsiasi cosa...</p>
</div>
"""


css = """
h1 {
  text-align: center;
  display: block;
}
"""

@spaces.GPU(duration=120)
def chat_llama3_8b(message: str, history: list, temperature: float, max_new_tokens: int) -> str:
    # Initialize the conversation with a system prompt
    conversation = [{"role": "system", "content": "Sei un assistente specializzato nella lingua italiana."}]

    flat_history = [item for sublist in history for item in sublist]

    if len(flat_history) > 16:
        flat_history = flat_history[-16:]

    # Rebuild the conversation from the trimmed history
    for i in range(0, len(flat_history), 2):
        conversation.extend([
            {"role": "user", "content": flat_history[i]},
            {"role": "assistant", "content": flat_history[i + 1]}
        ])

    # Add the current user message to the conversation
    conversation.append({"role": "user", "content": message})

    # Prepare the input for the model
    input_ids = tokenizer.apply_chat_template(conversation, return_tensors="pt").to(model.device)

    # Parameters for generating text
    do_sample = True if temperature > 0 else False  # Use sampling unless temperature is 0
    real_temperature = max(temperature, 0.001)  # Avoid zero temperature which disables sampling

    # Generate a response from the model
    generated_ids = model.generate(
        input_ids=input_ids,
        max_new_tokens=max_new_tokens,
        do_sample=do_sample,
        temperature=real_temperature,
        eos_token_id=tokenizer.eos_token_id
    )
    input_length = input_ids.size(1)
    new_tokens = generated_ids[:, input_length:]
    decoded = tokenizer.batch_decode(new_tokens, skip_special_tokens=True)[0]

    final_response = decoded.strip("assistant")
    if final_response.startswith(':'):
        final_response = final_response.lstrip(':').strip()

    return final_response
    


# Gradio block
chatbot = gr.Chatbot(height=450, placeholder=PLACEHOLDER, label='Gradio ChatInterface')

with gr.Blocks(fill_height=True, css=css) as demo:
    gr.Markdown(DESCRIPTION)
    gr.DuplicateButton(value="Duplicate Space for private use", elem_id="duplicate-button")
    gr.ChatInterface(
        fn=chat_llama3_8b,
        chatbot=chatbot,
        fill_height=True,
        additional_inputs_accordion=gr.Accordion(label="⚙️ Parameters", open=False, render=False),
        additional_inputs=[
            gr.Slider(minimum=0,
                      maximum=1,
                      step=0.1,
                      value=0.001,
                      label="Temperature",
                      render=False),
            gr.Slider(minimum=128,
                      maximum=4096,
                      step=1,
                      value=512,
                      label="Max new tokens",
                      render=False),
        ],
        examples=[
            ['Quanto è alta la torre di Pisa?'],
            ["Se un mattone pesa 1kg più mezzo mattone, quanto pesa il mattone? rispondi impostando l'equazione"],
            ['Quanto fa 2 * 9?'],
            ['Scrivi una funzione python che calcola i primi n numeri di fibonacci'],
            ['Inventa tre indovinelli tutti diversi con le relative risposte in formato json']
        ],
        cache_examples=False,
    )


if __name__ == "__main__":
    demo.launch()