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from pathlib import Path
from shiny import App, Inputs, Outputs, Session, ui, reactive
import shinyswatch
from htmltools import HTML
from utils import background_img, question_circle_fill, read_markdown_file
www_dir = Path(__file__).parent.resolve() / "www"
project_config = ui.accordion_panel(
"Project Config",
*[
ui.input_text(
id="project_name",
label=ui.tooltip(
ui.span("Project Name ", question_circle_fill()), "Additional info"
),
placeholder="Awesome Axolotl"
),
ui.input_password(
id="hf_token",
label=ui.tooltip(
ui.span("HuggingFace Token ", question_circle_fill()), "T"
)
),
ui.input_text(
id="base_model",
label=ui.tooltip(
ui.span("Base Model ", question_circle_fill()),
"This is the huggingface model that contains *.pt, *.safetensors, or *.bin files",
),
placeholder="meta-llama/Llama-2-7b-chat-hf",
),
ui.input_select(
id="model_type",
label=ui.tooltip(
ui.span("Model Type ", question_circle_fill()),
"If you want to specify the type of model to load, AutoModelForCausalLM is a good choice too.",
),
choices=["AutoModelForCausalLM", "LlamaForCausalLM"],
selected="AutoModelForCausalLM",
),
ui.input_select(
id="model_derivation",
label=ui.tooltip(
ui.span("Model Source ", question_circle_fill()),
'Used to identify which the model is based on. Please note that if you set this to mistral, `padding_side` will be set to "left" by default',
),
choices=["falcon", "llama", "mistra", "qwen"],
selected="llama",
),
ui.accordion(
ui.accordion_panel(
"Advanced Config",
ui.input_text(id="test1", label="Test 2", placeholder="placeholder"),
),
open=False,
),
]
)
dataset_config = ui.accordion_panel(
"Datasets",
*[
ui.input_text(
id="dataset_path",
label=ui.tooltip(
ui.span("Dataset Path ", question_circle_fill()), "A list of one or more paths to datasets to finetune the model with. HuggingFace dataset repo | s3://,gs:// path | \"json\" for local dataset, make sure to fill data_files"
),
placeholder="vicgalle/alpaca-gpt4"
),
ui.input_select(
id="dataset_type",
label=ui.tooltip(
ui.span("Dataset Type ", question_circle_fill()), "The type of prompt to use for training. [alpaca, sharegpt, gpteacher, oasst, reflection]"
),
choices=["alpaca", "sharegpt", "gpteacher", "oasst", "reflection"],
selected="alpaca"
)
]
)
app_ui = ui.page_fillable(
shinyswatch.theme.minty(),
ui.layout_sidebar(
ui.sidebar(
ui.h3("Axolotl Laucher 🚀"),
ui.accordion(project_config, id="project_config"),
ui.accordion(dataset_config, id="dataset_config", open=False),
ui.input_action_button(
"create_space",
"Create HF Space",
),
width=400
),
HTML(background_img(url="https://github.com/OpenAccess-AI-Collective/axolotl/raw/main/image/axolotl.png",
opacity=0.1)),
ui.markdown(read_markdown_file(path="README.md"))
)
)
def server(input: Inputs, output: Outputs, session: Session):
@reactive.Effect
@reactive.event(input.create_space)
def _():
ui.notification_show("This is not yet implemented.", type="warning")
app = App(
app_ui,
server,
static_assets=str(www_dir),
)
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