mirror of
https://github.com/hiyouga/LLaMA-Factory.git
synced 2025-08-02 19:52:50 +08:00
77 lines
2.6 KiB
Python
77 lines
2.6 KiB
Python
from typing import TYPE_CHECKING, Dict
|
|
|
|
import gradio as gr
|
|
|
|
from ..common import DEFAULT_DATA_DIR, list_dataset
|
|
from .data import create_preview_box
|
|
|
|
|
|
if TYPE_CHECKING:
|
|
from gradio.components import Component
|
|
|
|
from ..engine import Engine
|
|
|
|
|
|
def create_eval_tab(engine: "Engine") -> Dict[str, "Component"]:
|
|
input_elems = engine.manager.get_base_elems()
|
|
elem_dict = dict()
|
|
|
|
with gr.Row():
|
|
dataset_dir = gr.Textbox(value=DEFAULT_DATA_DIR, scale=2)
|
|
dataset = gr.Dropdown(multiselect=True, scale=4)
|
|
preview_elems = create_preview_box(dataset_dir, dataset)
|
|
|
|
input_elems.update({dataset_dir, dataset})
|
|
elem_dict.update(dict(dataset_dir=dataset_dir, dataset=dataset, **preview_elems))
|
|
|
|
with gr.Row():
|
|
cutoff_len = gr.Slider(value=1024, minimum=4, maximum=8192, step=1)
|
|
max_samples = gr.Textbox(value="100000")
|
|
batch_size = gr.Slider(value=8, minimum=1, maximum=512, step=1)
|
|
predict = gr.Checkbox(value=True)
|
|
|
|
input_elems.update({cutoff_len, max_samples, batch_size, predict})
|
|
elem_dict.update(dict(cutoff_len=cutoff_len, max_samples=max_samples, batch_size=batch_size, predict=predict))
|
|
|
|
with gr.Row():
|
|
max_new_tokens = gr.Slider(10, 2048, value=128, step=1)
|
|
top_p = gr.Slider(0.01, 1, value=0.7, step=0.01)
|
|
temperature = gr.Slider(0.01, 1.5, value=0.95, step=0.01)
|
|
output_dir = gr.Textbox()
|
|
|
|
input_elems.update({max_new_tokens, top_p, temperature, output_dir})
|
|
elem_dict.update(dict(max_new_tokens=max_new_tokens, top_p=top_p, temperature=temperature, output_dir=output_dir))
|
|
|
|
with gr.Row():
|
|
cmd_preview_btn = gr.Button()
|
|
start_btn = gr.Button(variant="primary")
|
|
stop_btn = gr.Button(variant="stop")
|
|
|
|
with gr.Row():
|
|
resume_btn = gr.Checkbox(visible=False, interactive=False)
|
|
process_bar = gr.Slider(visible=False, interactive=False)
|
|
|
|
with gr.Row():
|
|
output_box = gr.Markdown()
|
|
|
|
output_elems = [output_box, process_bar]
|
|
elem_dict.update(
|
|
dict(
|
|
cmd_preview_btn=cmd_preview_btn,
|
|
start_btn=start_btn,
|
|
stop_btn=stop_btn,
|
|
resume_btn=resume_btn,
|
|
process_bar=process_bar,
|
|
output_box=output_box,
|
|
)
|
|
)
|
|
|
|
cmd_preview_btn.click(engine.runner.preview_eval, input_elems, output_elems, concurrency_limit=None)
|
|
start_btn.click(engine.runner.run_eval, input_elems, output_elems)
|
|
stop_btn.click(engine.runner.set_abort)
|
|
resume_btn.change(engine.runner.monitor, outputs=output_elems, concurrency_limit=None)
|
|
|
|
dataset_dir.change(list_dataset, [dataset_dir], [dataset], queue=False)
|
|
|
|
return elem_dict
|