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# Thank you code from https://huggingface.co/spaces/gokaygokay/Gemma-2-llamacpp
#import spaces
import os
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
# huggingface_token = os.getenv("HUGGINGFACE_TOKEN")
hf_hub_download(
repo_id="wannaphong/KhanomTanLLM-1B-Instruct-Q2_K-GGUF",
filename="khanomtanllm-1b-instruct-q2_k.gguf",
local_dir="./models"
)
hf_hub_download(
repo_id="wannaphong/KhanomTanLLM-3B-Instruct-Q2_K-GGUF",
filename="khanomtanllm-3b-instruct-q2_k.gguf",
local_dir="./models"
)
# hf_hub_download(
# repo_id="google/gemma-2-2b-it-GGUF",
# filename="2b_it_v2.gguf",
# local_dir="./models",
# token=huggingface_token
# )
llm = None
llm_model = None
#@spaces.GPU(duration=120)
def respond(
message,
history: list[tuple[str, str]],
model,
system_message,
max_tokens,
temperature,
min_p,
top_p,
top_k,
repeat_penalty,
):
chat_template = MessagesFormatterType.GEMMA_2
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=2048,
)
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.min_p = min_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.replace("<|assistant|>","")
yield outputs
description = """
"""
demo = gr.ChatInterface(
respond,
additional_inputs=[
gr.Dropdown([
'khanomtanllm-1b-instruct-q2_k.gguf',
'khanomtanllm-3b-instruct-q2_k.gguf',
],
value="khanomtanllm-1b-instruct-q2_k.gguf",
label="Model"
),
gr.Textbox(value="You are a helpful assistant.", label="System message"),
gr.Slider(minimum=1, maximum=2048, value=2048, step=1, label="Max tokens"),
gr.Slider(minimum=0.1, maximum=4.0, value=2.0, step=0.1, label="Temperature"),
gr.Slider(
minimum=0.1,
maximum=1.0,
value=0.7,
step=0.05,
label="min-p",
),
gr.Slider(
minimum=0.1,
maximum=1.0,
value=0.95,
step=0.05,
label="Top-p",
),
gr.Slider(
minimum=0,
maximum=100,
value=40,
step=1,
label="Top-k",
),
gr.Slider(
minimum=0.0,
maximum=2.0,
value=1.1,
step=0.1,
label="Repetition penalty",
),
],
retry_btn="Retry",
undo_btn="Undo",
clear_btn="Clear",
submit_btn="Send",
title="Chat with KhanomTanLLM using llama.cpp",
description=description,
chatbot=gr.Chatbot(
scale=1,
likeable=False,
show_copy_button=True
)
)
if __name__ == "__main__":
demo.launch() |