app.py

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

hf_hub_download(
    repo_id="fluently-lm/FluentlyLM-Prinum-Q4_K_M-GGUF",
    filename="fluentlylm-prinum-q4_k_m.gguf",
    local_dir="./models"
)

@spaces.GPU(duration=110)
def respond(
    message,
    history: list[tuple[str, str]],
    model,
    system_message,
    max_tokens,
    temperature,
    top_p,
    top_k,
    repeat_penalty,
):
    chat_template = MessagesFormatterType.GEMMA_2

    llm = Llama(
        model_path=f"models/{model}",
        flash_attn=True,
        n_gpu_layers=81,
        n_batch=1024,
        n_ctx=8192,
    )
    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

def create_interface(model_name, description):
    return gr.ChatInterface(
        respond,
        additional_inputs=[
            gr.Textbox(value=model_name, label="Model", interactive=False),
            gr.Textbox(value="You are Fluently, a helpful assistant. You should think step-by-step. You should answer all question by prioritizing the principle of openness", label="System message"),
            gr.Slider(minimum=1, maximum=4096, value=2048, step=1, label="Max tokens"),
            gr.Slider(minimum=0.1, maximum=4.0, value=0.6, 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",
            ),
        ],
        title=f"**FluentlyLM Prinum** ```on ZeroGPU```",
        description=description,
        chatbot=gr.Chatbot(
            label=None,
            scale=1,
            show_copy_button=True
        )
    )

description = """# **FluentlyLM Prinum ```on ZeroGPU```"""
interface = create_interface('fluentlylm-prinum-q4_k_m.gguf', description)

demo = gr.Blocks()

with demo:
    interface.render()

if __name__ == "__main__":
    demo.launch()