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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
# Download models
hf_hub_download(
repo_id="OEvortex/HelpingAI-3B-chat",
filename="helpingai-3b-chat-iq4_xs-imat.gguf",
local_dir="./models"
)
hf_hub_download(
repo_id="OEvortex/HelpingAI-3B-chat",
filename="helpingai-3b-chat-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,
):
chat_template = MessagesFormatterType.CHATML
global llm
global llm_model
if llm is None or llm_model != model:
llm = Llama(
model_path=f"models/{model}",
n_ctx=2048, # Reduced context size for CPU
n_threads=4, # Adjust this based on your CPU cores
n_gpu_layers=50
)
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 = "HelpingAI-3B-chat: The Compact Yet Powerful Small Language Model (SLM) for Emotionally Intelligent Conversations 🌟"
demo = gr.ChatInterface(
respond,
additional_inputs=[
gr.Dropdown([
'helpingai-3b-chat-q4_k_m.gguf',
'helpingai-3b-chat-iq4_xs-imat.gguf'
],
value="helpingai-3b-chat-iq4_xs-imat.gguf",
label="Model"
),
gr.Textbox(value="You are HelpingAI a emotional AI always answer my question in HelpingAI style and to the point", label="System message"),
gr.Slider(minimum=1, maximum=2048, value=1024, step=1, label="Max tokens"),
gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
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 HelpingAI-3B using llama.cpp",
description=description,
chatbot=gr.Chatbot(
scale=1,
likeable=False,
show_copy_button=True
)
)
if __name__ == "__main__":
demo.launch() |