import gradio as gr import os from huggingface_hub.file_download import http_get from llama_cpp import Llama SYSTEM_PROMPT = "Ты — Сайга, русскоязычный автоматический ассистент. Ты разговариваешь с людьми и помогаешь им." def get_message_tokens(model, role, content): content = f"{role}\n{content}\n" content = content.encode("utf-8") return model.tokenize(content, special=True) def get_system_tokens(model): system_message = {"role": "system", "content": SYSTEM_PROMPT} return get_message_tokens(model, **system_message) def load_model( directory: str = ".", model_name: str = "model-q4_K.gguf", model_url: str = "https://huggingface.co/IlyaGusev/saiga2_13b_gguf/resolve/main/model-q4_K.gguf" ): final_model_path = os.path.join(directory, model_name) print("Downloading all files...") if not os.path.exists(final_model_path): with open(final_model_path, "wb") as f: http_get(model_url, f) os.chmod(final_model_path, 0o777) print("Files downloaded!") model = Llama( model_path=final_model_path, n_ctx=2048 ) print("Model loaded!") return model MODEL = load_model() def user(message, history): new_history = history + [[message, None]] return "", new_history def bot( history, system_prompt, top_p, top_k, temp ): model = MODEL tokens = get_system_tokens(model)[:] for user_message, bot_message in history[:-1]: message_tokens = get_message_tokens(model=model, role="user", content=user_message) tokens.extend(message_tokens) if bot_message: message_tokens = get_message_tokens(model=model, role="bot", content=bot_message) tokens.extend(message_tokens) last_user_message = history[-1][0] message_tokens = get_message_tokens(model=model, role="user", content=last_user_message) tokens.extend(message_tokens) role_tokens = model.tokenize("bot\n".encode("utf-8"), special=True) tokens.extend(role_tokens) generator = model.generate( tokens, top_k=top_k, top_p=top_p, temp=temp ) partial_text = "" for i, token in enumerate(generator): if token == model.token_eos(): break partial_text += model.detokenize([token]).decode("utf-8", "ignore") history[-1][1] = partial_text yield history with gr.Blocks( theme=gr.themes.Soft() ) as demo: favicon = '' gr.Markdown( f"""

{favicon}Saiga2 13B GGUF Q4_K

This is a demo of a **Russian**-speaking LLaMA2-based model. If you are interested in other languages, please check other models, such as [MPT-7B-Chat](https://huggingface.co/spaces/mosaicml/mpt-7b-chat). Это демонстрационная версия [квантованной Сайги-2 с 13 миллиардами параметров](https://huggingface.co/IlyaGusev/saiga2_13b_ggml), работающая на CPU. Сайга-2 — это разговорная языковая модель, которая основана на [LLaMA-2](https://ai.meta.com/llama/) и дообучена на корпусах, сгенерированных ChatGPT, таких как [ru_turbo_alpaca](https://huggingface.co/datasets/IlyaGusev/ru_turbo_alpaca), [ru_turbo_saiga](https://huggingface.co/datasets/IlyaGusev/ru_turbo_saiga) и [gpt_roleplay_realm](https://huggingface.co/datasets/IlyaGusev/gpt_roleplay_realm). """ ) with gr.Row(): with gr.Column(scale=5): system_prompt = gr.Textbox(label="Системный промпт", placeholder="", value=SYSTEM_PROMPT, interactive=False) chatbot = gr.Chatbot(label="Диалог").style(height=400) with gr.Column(min_width=80, scale=1): with gr.Tab(label="Параметры генерации"): top_p = gr.Slider( minimum=0.0, maximum=1.0, value=0.9, step=0.05, interactive=True, label="Top-p", ) top_k = gr.Slider( minimum=10, maximum=100, value=30, step=5, interactive=True, label="Top-k", ) temp = gr.Slider( minimum=0.0, maximum=2.0, value=0.01, step=0.01, interactive=True, label="Температура" ) with gr.Row(): with gr.Column(): msg = gr.Textbox( label="Отправить сообщение", placeholder="Отправить сообщение", show_label=False, ).style(container=False) with gr.Column(): with gr.Row(): submit = gr.Button("Отправить") stop = gr.Button("Остановить") clear = gr.Button("Очистить") with gr.Row(): gr.Markdown( """ПРЕДУПРЕЖДЕНИЕ: Модель может генерировать фактически или этически некорректные тексты. Мы не несём за это ответственность.""" ) # Pressing Enter submit_event = msg.submit( fn=user, inputs=[msg, chatbot], outputs=[msg, chatbot], queue=False, ).success( fn=bot, inputs=[ chatbot, system_prompt, top_p, top_k, temp ], outputs=chatbot, queue=True, ) # Pressing the button submit_click_event = submit.click( fn=user, inputs=[msg, chatbot], outputs=[msg, chatbot], queue=False, ).success( fn=bot, inputs=[ chatbot, system_prompt, top_p, top_k, temp ], outputs=chatbot, queue=True, ) # Stop generation stop.click( fn=None, inputs=None, outputs=None, cancels=[submit_event, submit_click_event], queue=False, ) # Clear history clear.click(lambda: None, None, chatbot, queue=False) demo.queue(max_size=128) demo.launch(show_error=True)