from __future__ import annotations from typing import Iterable import gradio as gr from gradio.themes.base import Base from gradio.themes.utils import colors, fonts, sizes from instruct_pipeline import InstructionTextGenerationPipeline from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline import torch 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"], ) tokenizer = AutoTokenizer.from_pretrained("databricks/dolly-v2-12b", padding_side="left") model = AutoModelForCausalLM.from_pretrained("databricks/dolly-v2-12b", device_map="auto", load_in_8bit=True) generate_text = InstructionTextGenerationPipeline(model=model, tokenizer=tokenizer) #generate_text = pipeline(model="databricks/dolly-v2-12b", torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto") def generate(instruction): response = generate_text(instruction) result = "" for word in response.split(" "): result += word + " " yield result examples = [ "Instead of making a peanut butter and jelly sandwich, what else could I combine peanut butter with in a sandwich? Give five ideas", "How do I make a campfire?", "Write me a tweet about the release of Dolly 2.0, a new LLM", "Explain to me the difference between nuclear fission and fusion.", "I'm selling my Nikon D-750, write a short blurb for my ad." ] def process_example(args): for x in generate(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", ), ): 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( """ ## Dolly 2.0 Dolly 2.0 is a 12B parameter language model based on the EleutherAI pythia model family and fine-tuned exclusively on a new, high-quality human generated instruction following dataset, crowdsourced among Databricks employees. For more details, please refer to the [model card](https://huggingface.co/databricks/dolly-v2-12b) Type in the box below and click the button to generate answers to your most pressing questions! """ ) gr.HTML("

You can duplicate this Space to run it privately without a queue for shorter queue times : Duplicate Space

") 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=False, fn=process_example, outputs=[output], ) submit.click(generate, inputs=[instruction], outputs=[output]) instruction.submit(generate, inputs=[instruction], outputs=[output]) demo.queue(concurrency_count=16).launch(debug=True)