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import spaces
import json
import subprocess
from llama_cpp import Llama
from llama_cpp_agent import LlamaCppAgent, MessagesFormatterType
from llama_cpp_agent.providers import LlamaCppPythonProvider
from llama_cpp_agent.chat_history import BasicChatHistory
from llama_cpp_agent.chat_history.messages import Roles
import gradio as gr
from huggingface_hub import hf_hub_download
# モデルのダウンロード
hf_hub_download(
repo_id="Aratako/Oumuamua-7b-RP-GGUF",
filename="Oumuamua-7b-RP_Q4_K_M.gguf",
local_dir="./models"
)
hf_hub_download(
repo_id="bartowski/Oumuamua-7b-instruct-v2-GGUF",
filename="Oumuamua-7b-instruct-v2-Q4_K_M.gguf",
local_dir="./models"
)
hf_hub_download(
repo_id="mmnga/umiyuki-Umievo-itr012-Gleipnir-7B-gguf",
filename="umiyuki-Umievo-itr012-Gleipnir-7B-Q4_K_M.gguf",
local_dir="./models"
)
hf_hub_download(
repo_id="Local-Novel-LLM-project/Ninja-V3-GGUF",
filename="Ninja-V3-Q4_K_M.gguf",
local_dir="./models"
)
hf_hub_download(
repo_id="Local-Novel-LLM-project/Kagemusya-7B-v1-GGUF",
filename="kagemusya-7b-v1Q8_0.gguf",
local_dir="./models"
)
hf_hub_download(
repo_id="elyza/Llama-3-ELYZA-JP-8B-GGUF",
filename="Llama-3-ELYZA-JP-8B-q4_k_m.gguf",
local_dir="./models"
)
llm = None
llm_model = None
def respond(
message,
history: list[tuple[str, str]],
model,
system_message,
max_tokens,
temperature,
top_p,
top_k,
repeat_penalty,
template,
):
chat_template = MessagesFormatterType[template]
global llm
global llm_model
if llm is None or llm_model != model:
llm = Llama(
model_path=f"models/{model}",
flash_attn=True,
n_gpu_layers=81,
n_batch=1024,
n_ctx=8192,
)
llm_model = model
provider = LlamaCppPythonProvider(llm)
agent = LlamaCppAgent(
provider,
system_prompt=f"{system_message}",
predefined_messages_formatter_type=chat_template,
debug_output=True
)
settings = provider.get_provider_default_settings()
settings.temperature = temperature
settings.top_k = top_k
settings.top_p = top_p
settings.max_tokens = max_tokens
settings.repeat_penalty = repeat_penalty
settings.stream = True
messages = BasicChatHistory()
for msn in history:
user = {
'role': Roles.user,
'content': msn[0]
}
assistant = {
'role': Roles.assistant,
'content': msn[1]
}
messages.add_message(user)
messages.add_message(assistant)
stream = agent.get_chat_response(
message,
llm_sampling_settings=settings,
chat_history=messages,
returns_streaming_generator=True,
print_output=False
)
outputs = ""
for output in stream:
outputs += output
yield outputs
description = """<p align="center">Defaults to Oumuamua-7b-RP (you can switch to other models from additional inputs)</p>
<p><center>
<a href="https://huggingface.co/Aratako/Oumuamua-7b-RP-GGUF" target="_blank">[Oumuamua-7b-RP Model]</a>
<a href="https://huggingface.co/bartowski/Oumuamua-7b-instruct-v2-GGUF" target="_blank">[Oumuamua-7b-instruct-v2 Model]</a>
<a href="https://huggingface.co/mmnga/umiyuki-Umievo-itr012-Gleipnir-7B-gguf" target="_blank">[Umievo-itr012-Gleipnir-7B Model]</a>
<a href="https://huggingface.co/Local-Novel-LLM-project/Ninja-V3-GGUF" target="_blank">[Ninja-V3 Model]</a>
<a href="https://huggingface.co/Local-Novel-LLM-project/Kagemusya-7B-v1-GGUF" target="_blank">[Kagemusya-7B-v1 Model]</a>
<a href="https://huggingface.co/elyza/Llama-3-ELYZA-JP-8B-GGUF" target="_blank">[Llama-3-ELYZA-JP-8B Model]</a>
</center></p>
"""
templates = [
"MISTRAL", "CHATML", "VICUNA", "LLAMA_2", "SYNTHIA",
"NEURAL_CHAT", "SOLAR", "OPEN_CHAT", "ALPACA", "CODE_DS",
"B22", "LLAMA_3", "PHI_3"
]
with gr.Blocks() as demo:
gr.Markdown(description)
with gr.Row():
model_dropdown = gr.Dropdown(
choices=[
'Oumuamua-7b-RP_Q4_K_M.gguf',
'Oumuamua-7b-instruct-v2-Q4_K_M.gguf',
'umiyuki-Umievo-itr012-Gleipnir-7B-Q4_K_M.gguf',
'Ninja-V3-Q4_K_M.gguf',
'kagemusya-7b-v1Q8_0.gguf',
'Llama-3-ELYZA-JP-8B-q4_k_m.gguf'
],
value="Oumuamua-7b-RP_Q4_K_M.gguf",
label="Model"
)
template_dropdown = gr.Dropdown(
choices=templates,
value="LLAMA_3",
label="Template"
)
chatbot = gr.Chatbot()
with gr.Row():
system_message = gr.Textbox(value="You are a helpful assistant.", label="System message")
with gr.Row():
max_tokens = gr.Slider(minimum=1, maximum=4096, value=2048, step=1, label="Max tokens")
temperature = gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature")
with gr.Row():
top_p = gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p")
top_k = gr.Slider(minimum=0, maximum=100, value=40, step=1, label="Top-k")
with gr.Row():
repeat_penalty = gr.Slider(minimum=0.0, maximum=2.0, value=1.1, step=0.1, label="Repetition penalty")
submit_btn = gr.Button("Send")
clear_btn = gr.Button("Clear")
undo_btn = gr.Button("Undo")
retry_btn = gr.Button("Retry")
def handle_response(message, history):
return list(respond(
message,
history,
model_dropdown.value,
system_message.value,
max_tokens.value,
temperature.value,
top_p.value,
top_k.value,
repeat_penalty.value,
template_dropdown.value
))
submit_btn.click(fn=handle_response, inputs=[chatbot.input, chatbot.history], outputs=chatbot)
clear_btn.click(lambda: None, None, chatbot)
undo_btn.click(lambda x: x[:-1] if x else None, chatbot, chatbot)
retry_btn.click(lambda x: x[:-1] if x else None, chatbot, chatbot)
demo.launch()