Discord

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="bartowski/Meta-Llama-3-70B-Instruct-GGUF", filename="Meta-Llama-3-70B-Instruct-Q3_K_M.gguf", local_dir = "./models" ) hf_hub_download( repo_id="bartowski/Mistral-7B-Instruct-v0.3-GGUF", filename="Mistral-7B-Instruct-v0.3-f32.gguf", local_dir = "./models" ) css = """ .message-row { justify-content: space-evenly !important; } .message-bubble-border { border-radius: 6px !important; } .dark.message-bubble-border { border-color: #343140 !important; } .dark.user { background: #1e1c26 !important; } .dark.assistant.dark, .dark.pending.dark { background: #16141c !important; } """ def get_messages_formatter_type(model_name): if "Llama" in model_name: return MessagesFormatterType.LLAMA_3 elif "Mistral" in model_name: return MessagesFormatterType.MISTRAL else: raise ValueError(f"Unsupported model: {model_name}") @spaces.GPU(duration=120) def respond( message, history: list[tuple[str, str]], model, system_message, max_tokens, temperature, top_p, top_k, repeat_penalty, ): chat_template = get_messages_formatter_type(model) llm = Llama( model_path=f"models/{model}", flash_attn=True, n_threads=40, 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 PLACEHOLDER = """

llama-cpp-agent

The llama-cpp-agent framework simplifies interactions with Large Language Models (LLMs), providing an interface for chatting, executing function calls, generating structured output, performing retrieval augmented generation, and processing text using agentic chains with tools.

Mistral 7B Instruct v0.3 Meta Llama 3 70B Instruct

""" demo = gr.ChatInterface( respond, additional_inputs=[ gr.Dropdown([ 'Meta-Llama-3-70B-Instruct-Q3_K_M.gguf', 'Mistral-7B-Instruct-v0.3-f32.gguf' ], value="Meta-Llama-3-70B-Instruct-Q3_K_M.gguf", label="Model" ), gr.Textbox(value="You are a helpful assistant.", 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=0.7, 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", ), ], theme=gr.themes.Soft(primary_hue="violet", secondary_hue="violet", neutral_hue="gray",font=[gr.themes.GoogleFont("Exo"), "ui-sans-serif", "system-ui", "sans-serif"]).set( body_background_fill_dark="#16141c", block_background_fill_dark="#16141c", block_border_width="1px", block_title_background_fill_dark="#1e1c26", input_background_fill_dark="#292733", button_secondary_background_fill_dark="#24212b", border_color_primary_dark="#343140", background_fill_secondary_dark="#16141c", color_accent_soft_dark="transparent" ), css=css, retry_btn="Retry", undo_btn="Undo", clear_btn="Clear", submit_btn="Send", description="Llama-cpp-agent: Chat multi llm selection", chatbot=gr.Chatbot(scale=1, placeholder=PLACEHOLDER) ) if __name__ == "__main__": demo.launch()