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| import math | |
| import gradio as gr | |
| import torch | |
| 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]) | |
| 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): | |
| output = llm(f"### Instruction: {input_text}\n\n### Response: ", echo=False, **config['chat']) | |
| return output['choices'][0]['text'] | |
| instruct_description = f""" | |
| ### brought to you by OpenAccess AI Collective | |
| - 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. | |
| - Due to a [missing feature in Gradio](https://github.com/gradio-app/gradio/issues/3914), the chatbot interface will not show you your status in the queue. If it's stuck, be patient. | |
| - [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) | |
| """ | |
| 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"), | |
| title="GGML UI Chatbot Demo", | |
| description=instruct_description, | |
| ) | |
| with gr.Blocks() as demo: | |
| 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(): | |
| gr.Markdown(f""" | |
| ### brought to you by OpenAccess AI Collective | |
| - 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) | |
| """) | |
| 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=0.2) | |
| 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 L", 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]) | |
| 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) | |
| # figure out how much VRAM is available to see if we can increase concurrency | |
| concurrency_count = 1 | |
| model_vram_size_in_gb = 11 | |
| if torch.cuda.is_available(): | |
| device = torch.cuda.current_device() | |
| total_memory = torch.cuda.get_device_properties(device).total_memory | |
| total_memory_in_gb = total_memory / 1024**3 | |
| concurrency_count = int(math.floor(total_memory_in_gb / model_vram_size_in_gb)) | |
| demo.queue(max_size=16, concurrency_count=1).launch(debug=True, server_name="0.0.0.0", server_port=7860) | |