Upload app.py

app.py

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+import time
+from threading import Thread
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
+import gradio as gr
+import torch
+
+model_id = r"/home/michele/PycharmProjects/mistral_finetuning/llama_ita_complete_v2"
+# model_id = r"/home/michele/PycharmProjects/mistral_finetuning/mistral_ita_complete_v5"
+
+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</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:
+    # Creare la struttura della conversazione
+    conversation = []
+    for user, assistant in history:
+        conversation.extend([{"role": "user", "content": user}, {"role": "assistant", "content": assistant}])
+    conversation.append({"role": "user", "content": message})
+
+    # Preparare gli input per il modello
+    input_ids = tokenizer.apply_chat_template(conversation, return_tensors="pt").to(model.device)
+
+    # Parametri per la generazione del testo
+    do_sample = True if temperature > 0 else False  # Usa il campionamento a meno che la temperatura non sia 0
+    real_temperature = max(temperature, 0.001)  # Evita temperatura 0 che disabilita il campionamento
+
+    # Generare una risposta dal modello
+    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
+    )
+
+    # Decodificare i token generati
+    decoded = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
+    prompt_end_index = decoded[0].find(message) + len(message)
+    final_response = decoded[0][prompt_end_index:] if prompt_end_index != -1 else decoded[0]
+
+    return final_response.strip("assistant")
+
+
+# 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 9.000 * 9.000?'],
+            ['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()