Vikhr-Instruct2 / app.py
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Update app.py
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import gradio as gr
import os
from huggingface_hub.file_download import http_get
from llama_cpp import Llama
SYSTEM_PROMPT = "Ты — Вихрь, русскоязычный автоматический ассистент. Ты разговариваешь с людьми и помогаешь им."
def get_message_tokens(model, role, content):
content = f"{role}\n{content}\n</s>"
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 = "vikhr-7b-instruct_0.2.Q4_K_S.gguf",
model_url: str = "https://huggingface.co/pirbis/Vikhr-7B-instruct_0.2-GGUF/resolve/main/vikhr-7b-instruct_0.2.Q4_K_S.gguf"
):
final_model_path = os.path.join(directory, model_name)
#pirbis/Vikhr-7B-instruct_0.2-GGUF
print("Downloading all files...")
print(final_model_path)
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=1024
)
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 = '<img src="photo_2024-01-21_23-23-17.jpg" width="48px" style="display: inline">'
gr.Markdown(
f"""<h1><center>Vikhr Instruct2 GGUF Q4_K</center></h1>
This is a demo of a **Russian**-speaking Mistral-based model.
Это демонстрационная версия [квантованной Вихрь-0.2 с 7 миллиардами параметров](https://huggingface.co/pirbis/Vikhr-7B-instruct_0.2-GGUF/), работающая на CPU.
Bихрь — это семейство переведенных языковых моделей, которая основаных на Mistral, LLama, CPM, T5, имеют расширенные словари и дообучены на русски корпусах, инструктивные версии дообучены 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")
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,
)
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)