from __future__ import annotations import os import time from typing import Iterable import gradio as gr import pynvml # import torch from ctransformers import AutoModelForCausalLM from gradio.themes.base import Base from gradio.themes.utils import colors, fonts, sizes from huggingface_hub import hf_hub_download # snapshot_download, from loguru import logger os.environ["TZ"] = "Asia/Shanghai" try: time.tzset() # type: ignore logger.debug(f"Timezone set to {os.environ['TZ']=}") except AttributeError: ... # Windows repo_id = "TheBloke/openbuddy-mistral-7B-v13-GGUF" filename = "openbuddy-mistral-7b-v13.Q4_K_S.gguf" # 4.17G model_ready = True logger.debug("Start dl") # try to download 5 times: for idx in range(5): logger.debug(f"attempt {idx + 1}") try: model_path = hf_hub_download(repo_id=repo_id, filename=filename, revision="main") break except Exception as exc: logger.error(f"failed to download {filename}: {exc}") # raise SystemExit("hf acting up, can't donwload the model {filename=}, exiting") else: logger.warning("Tried 5 times to no vain") raise gr.Error(f"hf acting up, can't donwload the model {filename=}, exiting") # raise SystemExit("hf acting up, can't donwload the model {filename=}, exiting") model_ready = False logger.debug(f"Done dl, {model_ready=}") # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. # model = AutoModelForCausalLM.from_pretrained("TheBloke/openbuddy-mistral-7B-v13-GGUF", model_file="openbuddy-mistral-7b-v13.Q4_K_S.gguf", model_type="mistral", gpu_layers=0) has_cuda = False try: pynvml.nvmlInit() has_cuda = True logger.debug("has cuda") except pynvml.nvml.NVMLError_LibraryNotFound: # type: ignore logger.debug("no cuda") # if torch.cuda.is_available(): if has_cuda: gpu_layers = 50 # set to what you like for GPU else: gpu_layers = 0 logger.debug("Start loading the model") try: model = AutoModelForCausalLM.from_pretrained( model_path, model_type="mistral", gpu_layers=gpu_layers ) except Exception as exc: logger.error(exc) model_ready = False model = None logger.debug(f"Done loading the model, {model_ready=}") ins = """[INST] <> Remember that your English name is "openbuddy" and your name in Chinese is "开友". You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information. <> {} [/INST] """ theme = gr.themes.Monochrome( primary_hue="indigo", secondary_hue="blue", neutral_hue="slate", radius_size=gr.themes.sizes.radius_sm, font=[ gr.themes.GoogleFont("Open Sans"), "ui-sans-serif", "system-ui", "sans-serif", ], ) def response(question): if model is None: res = "model not ready (got a problem with downloading the file {filename=} from hf.co)" else: res = model(ins.format(question)) yield res examples = ["Hello!"] def process_example(args): x = None for x in response(args): pass return x css = ".generating {visibility: hidden}" # Based on the gradio theming guide and borrowed from https://huggingface.co/spaces/shivi/dolly-v2-demo class SeafoamCustom(Base): def __init__( self, *, primary_hue: colors.Color | str = colors.emerald, secondary_hue: colors.Color | str = colors.blue, neutral_hue: colors.Color | str = colors.blue, spacing_size: sizes.Size | str = sizes.spacing_md, radius_size: sizes.Size | str = sizes.radius_md, font: fonts.Font | str | Iterable[fonts.Font | str] = ( fonts.GoogleFont("Quicksand"), "ui-sans-serif", "sans-serif", ), font_mono: fonts.Font | str | Iterable[fonts.Font | str] = ( fonts.GoogleFont("IBM Plex Mono"), "ui-monospace", "monospace", ), ): """Init.""" super().__init__( primary_hue=primary_hue, secondary_hue=secondary_hue, neutral_hue=neutral_hue, spacing_size=spacing_size, radius_size=radius_size, font=font, font_mono=font_mono, ) super().set( button_primary_background_fill="linear-gradient(90deg, *primary_300, *secondary_400)", button_primary_background_fill_hover="linear-gradient(90deg, *primary_200, *secondary_300)", button_primary_text_color="white", button_primary_background_fill_dark="linear-gradient(90deg, *primary_600, *secondary_800)", block_shadow="*shadow_drop_lg", button_shadow="*shadow_drop_lg", input_background_fill="zinc", input_border_color="*secondary_300", input_shadow="*shadow_drop", input_shadow_focus="*shadow_drop_lg", ) seafoam = SeafoamCustom() with gr.Blocks(theme=seafoam, analytics_enabled=False, css=css) as demo: with gr.Column(): gr.Markdown( """ ## Testrun Type in the box below and click the button to generate answers to your most pressing questions! """ ) with gr.Row(): with gr.Column(scale=3): instruction = gr.Textbox( placeholder="Enter your question here", label="Question", elem_id="q-input", ) with gr.Box(): gr.Markdown("**Answer**") output = gr.Markdown(elem_id="q-output") submit = gr.Button("Generate", variant="primary") gr.Examples( examples=examples, inputs=[instruction], # cache_examples=True, cache_examples=False, fn=process_example, outputs=[output], ) submit.click(response, inputs=[instruction], outputs=[output]) instruction.submit(response, inputs=[instruction], outputs=[output]) demo.queue(concurrency_count=1, max_size=5).launch(debug=False, share=True)