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| import gradio as gr | |
| import torch | |
| from transformers import pipeline | |
| model_name = "eljanmahammadli/AzLlama-152M-Alpaca" | |
| model = pipeline("text-generation", model=model_name, torch_dtype=torch.float16) | |
| logo_path = "AzLlama-logo.webp" | |
| def get_prompt(question): | |
| base_instruction = "Aşağıda tapşırığı təsvir edən təlimat və əlavə kontekst təmin edən giriş verilmiştir. Sorğunu uyğun şəkildə tamamlayan cavab yazın." | |
| prompt = f"""{base_instruction} | |
| ### Təlimat: | |
| {question} | |
| ### Cavab: | |
| """ | |
| return prompt | |
| def get_answer(llm_output): | |
| return llm_output.split("### Cavab:")[1].strip() | |
| def answer_question(history, temperature, top_p, repetition_penalty, top_k, question): | |
| model_params = { | |
| "temperature": temperature, | |
| "top_p": top_p, | |
| "repetition_penalty": repetition_penalty, | |
| "top_k": top_k, | |
| "max_length": 512, # Adjust based on your needs | |
| "do_sample": True, | |
| } | |
| prompt = get_prompt(question) | |
| llm_output = model(prompt, **model_params)[0] | |
| answer = get_answer(llm_output["generated_text"]) | |
| divider = "\n\n" if history else "" | |
| print(answer) | |
| new_history = history + divider + f"USER: {question}\nASSISTANT: {answer}\n" | |
| return new_history, "" # Return updated history and clear the question input | |
| def send_action(_=None): | |
| send_button.click() | |
| with gr.Blocks() as app: | |
| gr.Markdown("# AzLlama-150M Chatbot\n\n") | |
| with gr.Row(): | |
| with gr.Column(scale=0.2, min_width=200): | |
| gr.Markdown("### Model Logo") | |
| gr.Image( | |
| value=logo_path, | |
| ) | |
| # write info about the model | |
| gr.Markdown( | |
| "### Model Info\n" | |
| "This model is a 150M paramater LLaMA2 model trained from scratch on Azerbaijani text. It can be used to generate text based on the given prompt. " | |
| ) | |
| with gr.Column(scale=0.6): | |
| gr.Markdown("### Chat with the Assistant") | |
| history = gr.Textbox( | |
| label="Chat History", value="", lines=20, interactive=False | |
| ) | |
| question = gr.Textbox( | |
| label="Your question", | |
| placeholder="Type your question and press enter", | |
| ) | |
| send_button = gr.Button("Send") | |
| with gr.Column(scale=0.2, min_width=200): | |
| gr.Markdown("### Model Settings") | |
| temperature = gr.Slider( | |
| minimum=0.1, maximum=1.0, value=0.9, label="Temperature" | |
| ) | |
| gr.Markdown( | |
| "Controls the randomness of predictions. Lower values make the model more deterministic." | |
| ) | |
| top_p = gr.Slider(minimum=0.1, maximum=1.0, value=0.95, label="Top P") | |
| gr.Markdown( | |
| "Nucleus sampling. Lower values focus on more likely predictions." | |
| ) | |
| repetition_penalty = gr.Slider( | |
| minimum=1.0, maximum=2.0, value=1.2, label="Repetition Penalty" | |
| ) | |
| gr.Markdown( | |
| "Penalizes repeated words. Higher values discourage repetition." | |
| ) | |
| top_k = gr.Slider(minimum=0, maximum=100, value=50, label="Top K") | |
| gr.Markdown("Keeps only the top k predictions. Set to 0 for no limit.") | |
| question.submit(send_action) | |
| send_button.click( | |
| fn=answer_question, | |
| inputs=[history, temperature, top_p, repetition_penalty, top_k, question], | |
| outputs=[history, question], | |
| ) | |
| app.launch() | |