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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("<p>You can duplicate this Space to run it privately without a queue for shorter queue times : <a style='display:inline-block' href='https://huggingface.co/spaces/RamAnanth1/Dolly-v2?duplicate=true'><img src='https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14' alt='Duplicate Space'></a> </p>") | |
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) |