import gradio as gr import yaml from huggingface_hub import hf_hub_download from huggingface_hub.utils import LocalEntryNotFoundError from llama_cpp import Llama with open("./config.yml", "r") as f: config = yaml.load(f, Loader=yaml.Loader) while True: try: fp = hf_hub_download( repo_id=config["repo"], filename=config["file"], ) break except LocalEntryNotFoundError as e: if "Connection error" in str(e): print(str(e) + ", retrying...") else: raise(e) llm = Llama(model_path=fp, **config["llama_cpp"]) def user(message, history): history = history or [] # Append the user's message to the conversation history history.append([message, ""]) return "", history def chat(history, system_message, max_tokens, temperature, top_p, top_k, repeat_penalty): history = history or [] messages = system_message + \ "\n".join(["\n".join(["USER: "+item[0], "ASSISTANT: "+item[1]]) for item in history]) # remove last space from assistant messages = messages[:-1] history[-1][1] = "" for output in llm( messages, echo=False, stream=True, max_tokens=max_tokens, temperature=temperature, top_p=top_p, top_k=top_k, repeat_penalty=repeat_penalty, **config['chat'] ): answer = output['choices'][0]['text'] print(output['choices']) history[-1][1] += answer # stream the response yield history, history def clear_chat(chat_history_state, chat_message): chat_history_state = [] chat_message = '' return chat_history_state, chat_message start_message = "" def generate_text_instruct(input_text): response = "" for output in llm(f"### Instruction:\n{input_text}\n\n### Response:\n", echo=False, stream=True, **config['chat']): answer = output['choices'][0]['text'] response += answer yield response instruct_interface = gr.Interface( fn=generate_text_instruct, inputs=gr.inputs.Textbox(lines= 10, label="Enter your input text"), outputs=gr.outputs.Textbox(label="Output text"), ) with gr.Blocks() as demo: with gr.Row(): with gr.Column(): gr.Markdown(f""" ### brought to you by OpenAccess AI Collective - Unquantized model available at https://huggingface.co/openaccess-ai-collective/manticore-13b - This is the [{config["repo"]}](https://huggingface.co/{config["repo"]}) model file [{config["file"]}](https://huggingface.co/{config["repo"]}/blob/main/{config["file"]}) - This Space uses GGML with GPU support, so it can quickly run larger models on smaller GPUs & VRAM. - This is running on a smaller, shared GPU, so it may take a few seconds to respond. - [Duplicate the Space](https://huggingface.co/spaces/openaccess-ai-collective/ggml-ui?duplicate=true) to skip the queue and run in a private space or to use your own GGML models. - When using your own models, simply update the [config.yml](https://huggingface.co/spaces/openaccess-ai-collective/ggml-ui/blob/main/config.yml) - Contribute at [https://github.com/OpenAccess-AI-Collective/ggml-webui](https://github.com/OpenAccess-AI-Collective/ggml-webui) - Many thanks to [TheBloke](https://huggingface.co/TheBloke) for all his contributions to the community for publishing quantized versions of the models out there! """) with gr.Tab("Instruct"): gr.Markdown("# GGML Spaces Instruct Demo") instruct_interface.render() with gr.Tab("Chatbot"): gr.Markdown("# GGML Spaces Chatbot Demo") chatbot = gr.Chatbot() with gr.Row(): message = gr.Textbox( label="What do you want to chat about?", placeholder="Ask me anything.", lines=1, ) with gr.Row(): submit = gr.Button(value="Send message", variant="secondary").style(full_width=True) clear = gr.Button(value="New topic", variant="secondary").style(full_width=False) stop = gr.Button(value="Stop", variant="secondary").style(full_width=False) with gr.Row(): with gr.Column(): max_tokens = gr.Slider(20, 500, label="Max Tokens", step=20, value=300) temperature = gr.Slider(0.2, 2.0, label="Temperature", step=0.1, value=0.8) top_p = gr.Slider(0.0, 1.0, label="Top P", step=0.05, value=0.95) top_k = gr.Slider(0, 100, label="Top K", step=1, value=40) repeat_penalty = gr.Slider(0.0, 2.0, label="Repetition Penalty", step=0.1, value=1.1) system_msg = gr.Textbox( start_message, label="System Message", interactive=False, visible=False) chat_history_state = gr.State() clear.click(clear_chat, inputs=[chat_history_state, message], outputs=[chat_history_state, message], queue=False) clear.click(lambda: None, None, chatbot, queue=False) submit_click_event = submit.click( fn=user, inputs=[message, chat_history_state], outputs=[message, chat_history_state], queue=True ).then( fn=chat, inputs=[chat_history_state, system_msg, max_tokens, temperature, top_p, top_k, repeat_penalty], outputs=[chatbot, chat_history_state], queue=True ) message_submit_event = message.submit( fn=user, inputs=[message, chat_history_state], outputs=[message, chat_history_state], queue=True ).then( fn=chat, inputs=[chat_history_state, system_msg, max_tokens, temperature, top_p, top_k, repeat_penalty], outputs=[chatbot, chat_history_state], queue=True ) stop.click(fn=None, inputs=None, outputs=None, cancels=[submit_click_event, message_submit_event], queue=False) demo.queue(**config["queue"]).launch(debug=True, server_name="0.0.0.0", server_port=7860)