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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, BitsAndBytesConfig
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")
quantization_config = BitsAndBytesConfig(load_in_8bit=True,
llm_int8_threshold=200.0)
model = AutoModelForCausalLM.from_pretrained("databricks/dolly-v2-12b", device_map="auto", quantization_config=quantization_config)
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) |