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from huggingface_hub import InferenceClient |
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import gradio as gr |
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client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1") |
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def format_prompt(message, history, system_prompt): |
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prompt = "<s>" |
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for user_prompt, bot_response in history: |
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prompt += f"[INST] {user_prompt} [/INST]" |
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prompt += f" {bot_response}</s> " |
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prompt += f"[INST] {system_prompt}, {message} [/INST]" |
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return prompt |
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def generate( |
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prompt, history, system_prompt, temperature=0.9, max_new_tokens=4096, top_p=0.95, repetition_penalty=1.0,): |
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temperature = float(temperature) |
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if temperature < 1e-2: |
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temperature = 1e-2 |
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top_p = float(top_p) |
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generate_kwargs = dict( |
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temperature=temperature, |
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max_new_tokens=max_new_tokens, |
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top_p=top_p, |
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repetition_penalty=repetition_penalty, |
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do_sample=True, |
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seed=42, |
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) |
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formatted_prompt = format_prompt(prompt, history, system_prompt) |
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stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False) |
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output = "" |
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for response in stream: |
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output += response.token.text |
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yield output |
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return output |
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additional_inputs = [ |
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gr.Textbox( |
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label="System Prompt", |
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value="Experto en servicios de abastecimiento, depuracion, reutilizacion y calidad del agua, para la empresa Canal de Isabel II", |
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max_lines=1, |
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interactive=True, |
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), |
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gr.Slider( |
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label="Temperature", |
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value=0.9, |
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minimum=0.0, |
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maximum=1.0, |
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step=0.05, |
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interactive=True, |
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info="Valores más altos producen resultados más diversos", |
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), |
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gr.Slider( |
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label="Max new tokens", |
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value=4096, |
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minimum=0, |
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maximum=4096, |
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step=64, |
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interactive=True, |
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info="El máximo número de nuevos tokens", |
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), |
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gr.Slider( |
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label="Top-p (nucleus sampling)", |
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value=0.90, |
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minimum=0.0, |
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maximum=1, |
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step=0.05, |
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interactive=True, |
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info="Valores más altos muestrean más tokens de baja probabilidad", |
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), |
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gr.Slider( |
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label="Repetition penalty", |
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value=1.2, |
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minimum=1.0, |
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maximum=2.0, |
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step=0.05, |
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interactive=True, |
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info="Penaliza los tokens repetidos", |
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) |
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] |
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examples = [ |
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["que parametros definen la calidad del agua", "Experto en servicios de abastecimiento, depuracion, reutilizacion y calidad del agua, para la empresa Canal de Isabel II", 0.7, 150, 0.80, 1.1], |
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["Describeme el proceso basico de depuracion del agua", "Experto en servicios de abastecimiento, depuracion y reutilizacion del agua", 0.8, 250, 0.85, 1.2], |
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["¿Que es una acometida?", "Experto en servicios de abastecimiento, depuracion, reutilizacion y calidad del agua, para la empresa Canal de Isabel II", 0.7, 180, 0.75, 1.2], |
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["¿Que es una toma?", "Experto en servicios de abastecimiento, depuracion, reutilizacion y calidad del agua, para la empresa Canal de Isabel II", 0.8, 180, 0.80, 1.1], |
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] |
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gr.ChatInterface( |
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fn=generate, |
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chatbot=gr.Chatbot(show_label=False, show_share_button=False, show_copy_button=True, likeable=True, layout="panel"), |
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additional_inputs=additional_inputs, |
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title="Mixtral 8B Fines didácticos", |
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description='Autor: <a href=\"https://huggingface.co/Antonio49\">Antonio Fernández</a> de <a href=\"https://saturdays.ai/\">SaturdaysAI</a>. Formación: <a href=\"https://cursos.saturdays.ai/courses/\">Cursos Online AI</a> Aplicación desarrollada con fines docentes', |
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theme="soft", |
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examples=examples, |
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iface = gr.ChatInterface( |
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fn = querying, |
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chatbot=gr.Chatbot(height=600), |
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textbox=gr.Textbox(placeholder="What is GenAI Ecosystem?", container=False, scale=7), |
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title="HiberusBot", |
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theme="soft", |
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examples=["Why Hiberus has created GenAI Ecosystem?", |
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"What is GenAI Ecosystem?"], |
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cache_examples=True, |
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retry_btn="Repetir", |
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undo_btn="Deshacer", |
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clear_btn="Borrar", |
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submit_btn="Enviar" |
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) |
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iface.launch(share=True) |
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concurrency_limit=20, |
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).launch(show_api=False) |
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