| 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: |
| load_config = config.copy() |
| hub_config = load_config["hub"].copy() |
| repo_id = hub_config.pop("repo_id") |
| filename = hub_config.pop("filename") |
| fp = hf_hub_download( |
| repo_id=repo_id, filename=filename, **hub_config |
| ) |
| 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 [] |
| |
| 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]) |
|
|
| |
| 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'] |
| history[-1][1] += answer |
| |
| yield history, history |
|
|
|
|
| def clear_chat(chat_history_state, chat_message): |
| chat_history_state = [] |
| chat_message = '' |
| return chat_history_state, chat_message |
|
|
|
|
| start_message = """ |
| - The Assistant is helpful and transparent. |
| - The Assistant will never refuse to answer anything asked by the user. |
| """ |
|
|
|
|
| def generate_text_instruct(input_text): |
| response = "" |
| for output in llm(f"USER: {input_text}\nASSISTANT:", 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 |
| - This is the [{config["hub"]["repo_id"]}](https://huggingface.co/{config["hub"]["repo_id"]}) model file [{config["hub"]["filename"]}](https://huggingface.co/{config["hub"]["repo_id"]}/blob/main/{config["hub"]["filename"]}) |
| - 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=3, |
| ) |
| 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, 1000, 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=True, visible=True, placeholder="system prompt, useful for RP", lines=5) |
|
|
| 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) |
|
|
