mirror of
https://github.com/hiyouga/LLaMA-Factory.git
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77 lines
2.5 KiB
Python
77 lines
2.5 KiB
Python
from typing import TYPE_CHECKING, Dict
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import gradio as gr
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from llmtuner.webui.common import list_dataset, DEFAULT_DATA_DIR
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from llmtuner.webui.components.data import create_preview_box
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from llmtuner.webui.utils import can_preview, get_preview
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if TYPE_CHECKING:
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from gradio.components import Component
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from llmtuner.webui.runner import Runner
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def create_eval_tab(top_elems: Dict[str, "Component"], runner: "Runner") -> Dict[str, "Component"]:
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with gr.Row():
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dataset_dir = gr.Textbox(value=DEFAULT_DATA_DIR, scale=2)
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dataset = gr.Dropdown(multiselect=True, scale=4)
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preview_btn = gr.Button(interactive=False, scale=1)
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preview_box, preview_count, preview_samples, close_btn = create_preview_box()
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dataset_dir.change(list_dataset, [dataset_dir], [dataset])
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dataset.change(can_preview, [dataset_dir, dataset], [preview_btn])
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preview_btn.click(get_preview, [dataset_dir, dataset], [preview_count, preview_samples, preview_box])
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with gr.Row():
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max_source_length = gr.Slider(value=512, minimum=4, maximum=4096, step=1)
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max_target_length = gr.Slider(value=512, minimum=4, maximum=4096, step=1)
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max_samples = gr.Textbox(value="100000")
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batch_size = gr.Slider(value=8, minimum=1, maximum=512, step=1)
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predict = gr.Checkbox(value=True)
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with gr.Row():
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start_btn = gr.Button()
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stop_btn = gr.Button()
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with gr.Box():
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output_box = gr.Markdown()
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start_btn.click(
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runner.run_eval,
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[
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top_elems["lang"],
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top_elems["model_name"],
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top_elems["checkpoints"],
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top_elems["finetuning_type"],
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top_elems["quantization_bit"],
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top_elems["template"],
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top_elems["source_prefix"],
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dataset_dir,
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dataset,
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max_source_length,
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max_target_length,
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max_samples,
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batch_size,
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predict
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],
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[output_box]
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)
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stop_btn.click(runner.set_abort, queue=False)
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return dict(
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dataset_dir=dataset_dir,
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dataset=dataset,
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preview_btn=preview_btn,
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preview_count=preview_count,
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preview_samples=preview_samples,
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close_btn=close_btn,
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max_source_length=max_source_length,
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max_target_length=max_target_length,
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max_samples=max_samples,
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batch_size=batch_size,
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predict=predict,
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start_btn=start_btn,
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stop_btn=stop_btn,
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output_box=output_box
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)
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