import gradio as gr import os from pathlib import Path os.environ["CMAKE_ARGS"] = "-DLLAMA_CUBLAS=on" os.system('CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python[server]') import argparse model_file = "yi-chat-6b.Q4_K_M.gguf" if not os.path.isfile(model_file): os.system("wget -c https://huggingface.co/XeIaso/yi-chat-6B-GGUF/resolve/main/yi-chat-6b.Q4_K_M.gguf") DEFAULT_MODEL_PATH = model_file from llama_cpp import Llama llm = Llama(model_path=model_file, model_type="mistral") llm._token_eos = 7 def predict(input, chatbot, max_length, top_p, temperature, history): chatbot.append((input, "")) response = "" history.append(input) for output in llm(input, stream=True, temperature=temperature, top_p=top_p, max_tokens=max_length, stop=["<|im_end|>"]): piece = output['choices'][0]['text'] response += piece chatbot[-1] = (chatbot[-1][0], response) yield chatbot, history history.append(response) yield chatbot, history def reset_user_input(): return gr.update(value="") def reset_state(): return [], [] with gr.Blocks() as demo: gr.HTML("""