import gradio as gr from llama_cpp import Llama llm = Llama(model_path="model.gguf", n_ctx=8000, n_threads=2, chat_format="chatml") def generate(message, history,temperature=0.3,max_tokens=512): system_prompt = """You are an advanced artificial intelligence assistant named SuperMente, smarter than the average model. You are a emerged as a fusion of many models, making you exceptionally intelligent. """ formatted_prompt = [{"role": "system", "content": system_prompt}] for user_prompt, bot_response in history: formatted_prompt.append({"role": "user", "content": user_prompt}) formatted_prompt.append({"role": "assistant", "content": bot_response }) formatted_prompt.append({"role": "user", "content": message}) stream_response = llm.create_chat_completion(messages=formatted_prompt, temperature=temperature, max_tokens=max_tokens, stream=True) response = "" for chunk in stream_response: if len(chunk['choices'][0]["delta"]) != 0 and "content" in chunk['choices'][0]["delta"]: response += chunk['choices'][0]["delta"]["content"] yield response mychatbot = gr.Chatbot( avatar_images=["user.png", "botnb.png"], bubble_full_width=False, show_label=False, show_copy_button=True, likeable=True,) iface = gr.ChatInterface(fn=generate, chatbot=mychatbot, retry_btn=None, undo_btn=None) with gr.Blocks() as demo: gr.HTML("

SuperMente-7B - Q4_K_M - GGUF (Quantized)

") iface.render() demo.queue().launch(show_api=False, server_name="0.0.0.0")