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 load_model(
directory: str = ".",
model_name: str = "saiga_nemo_12b.Q4_K_M.gguf",
model_url: str = "https://huggingface.co/IlyaGusev/saiga_nemo_12b_gguf/resolve/main/saiga_nemo_12b.Q4_K_M.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=8192
)
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
messages = [{"role": "system", "content": SYSTEM_PROMPT}]
for user_message, bot_message in history[:-1]:
messages.append({"role": "user", "content": user_message})
if bot_message:
messages.append({"role": "assistant", "content": bot_message})
last_user_message = history[-1][0]
messages.append({"role": "user", "content": last_user_message})
partial_text = ""
for part in model.create_chat_completion(
messages,
temperature=temp,
top_k=top_k,
top_p=top_p,
stream=True,
):
delta = part["choices"][0]["delta"]
if "content" in delta:
partial_text += delta["content"]
history[-1][1] = partial_text
yield history
with gr.Blocks(
theme=gr.themes.Soft()
) as demo:
favicon = ''
gr.Markdown(
f"""
{favicon}Saiga Nemo 12B GGUF Q4_K_M
This is a demo of a **Russian**-speaking Mistral Nemo based model.
Это демонстрационная версия [квантованной Сайги Немо с 12 миллиардами параметров](https://huggingface.co/IlyaGusev/saiga_nemo_12b_gguf), работающая на CPU.
"""
)
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)