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app.py

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+import gradio as gr
+
+import copy
+import random
+import os
+import requests
+import time
+import sys
+
+from huggingface_hub import snapshot_download
+from llama_cpp import Llama
+
+
+SYSTEM_PROMPT = "Ты — Сайга, русскоязычный автоматический ассистент. Ты разговариваешь с людьми и помогаешь им."
+SYSTEM_TOKEN = 1788
+USER_TOKEN = 1404
+BOT_TOKEN = 9225
+LINEBREAK_TOKEN = 13
+
+
+def get_message_tokens(model, role, content):
+    message_tokens = model.tokenize(content.encode("utf-8"))
+    message_tokens.insert(1, ROLE_TOKENS[role])
+    message_tokens.insert(2, LINEBREAK_TOKEN)
+    message_tokens.append(model.token_eos())
+    return message_tokens
+
+
+def get_system_tokens(model):
+    system_message = {"role": "system", "content": SYSTEM_PROMPT}
+    return get_message_tokens(model, **system_message)
+
+
+repo_name = "IlyaGusev/saiga2_13b_ggml"
+model_name = "ggml-model-q4_1.bin"
+
+snapshot_download(repo_id=repo_name, local_dir=".", allow_patterns=model_name)
+
+model = Llama(
+    model_path=model_name,
+    n_ctx=2000,
+    n_parts=1,
+)
+
+max_new_tokens = 1500
+
+def user(message, history):
+    new_history = history + [[message, None]]
+    return "", new_history
+
+
+def bot(
+    history,
+    system_prompt,
+    top_p,
+    top_k,
+    temp
+)
+    tokens = get_system_tokens(model)[:]
+    tokens.append(LINEBREAK_TOKEN)
+
+    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]
+    if retrieved_docs:
+        last_user_message = f"Контекст: {retrieved_docs}\n\nИспользуя контекст, ответь на вопрос: {last_user_message}"
+    message_tokens = get_message_tokens(model=model, role="user", content=last_user_message)
+    tokens.extend(message_tokens)
+
+    role_tokens = [model.token_bos(), BOT_TOKEN, LINEBREAK_TOKEN]
+    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() or (max_new_tokens is not None and i >= max_new_tokens):
+            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:
+    conversation_id = gr.State(get_uuid)
+    favicon = '<img src="https://cdn.midjourney.com/b88e5beb-6324-4820-8504-a1a37a9ba36d/0_1.png" width="48px" style="display: inline">'
+    gr.Markdown(
+        f"""<h1><center>{favicon}Saiga2 13B</center></h1>
+        
+        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 версии [Сайги-2 с 13 миллиардами параметров](https://huggingface.co/IlyaGusev/saiga_13b_lora).
+        
+        Сайга — это разговорная языковая модель, которая основана на [LLaMA](https://research.facebook.com/publications/llama-open-and-efficient-foundation-language-models/) 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=0.1,
+                    step=0.1,
+                    interactive=True,
+                    label="Temp"
+                )
+    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, system_prompt],
+        outputs=[msg, chatbot],
+        queue=False,
+    ).success(
+        fn=retrieve,
+        inputs=[chatbot, db, retrieved_docs, k_documents],
+        outputs=[retrieved_docs],
+        queue=True,
+    ).success(
+        fn=bot,
+        inputs=[
+            chatbot,
+            system_prompt,
+            conversation_id,
+            retrieved_docs,
+            top_p,
+            top_k,
+            temp
+        ],
+        outputs=chatbot,
+        queue=True,
+    )
+
+    # Pressing the button
+    submit_click_event = submit.click(
+        fn=user,
+        inputs=[msg, chatbot, system_prompt],
+        outputs=[msg, chatbot],
+        queue=False,
+    ).success(
+        fn=retrieve,
+        inputs=[chatbot, db, retrieved_docs, k_documents],
+        outputs=[retrieved_docs],
+        queue=True,
+    ).success(
+        fn=bot,
+        inputs=[
+            chatbot,
+            system_prompt,
+            conversation_id,
+            retrieved_docs,
+            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, concurrency_count=1)
+demo.launch()