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