mirror of
https://github.com/hiyouga/LLaMA-Factory.git
synced 2025-08-04 20:52:59 +08:00
94 lines
3.4 KiB
Python
94 lines
3.4 KiB
Python
# Copyright 2024 the LlamaFactory team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from typing import TYPE_CHECKING, Dict
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from ...extras.packages import is_gradio_available
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from ..common import DEFAULT_DATA_DIR, list_datasets
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from .data import create_preview_box
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if is_gradio_available():
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import gradio as gr
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if TYPE_CHECKING:
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from gradio.components import Component
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from ..engine import Engine
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def create_eval_tab(engine: "Engine") -> Dict[str, "Component"]:
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input_elems = engine.manager.get_base_elems()
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elem_dict = dict()
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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, allow_custom_value=True, scale=4)
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preview_elems = create_preview_box(dataset_dir, dataset)
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input_elems.update({dataset_dir, dataset})
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elem_dict.update(dict(dataset_dir=dataset_dir, dataset=dataset, **preview_elems))
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with gr.Row():
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cutoff_len = gr.Slider(minimum=4, maximum=65536, value=1024, step=1)
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max_samples = gr.Textbox(value="100000")
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batch_size = gr.Slider(minimum=1, maximum=1024, value=2, step=1)
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predict = gr.Checkbox(value=True)
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input_elems.update({cutoff_len, max_samples, batch_size, predict})
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elem_dict.update(dict(cutoff_len=cutoff_len, max_samples=max_samples, batch_size=batch_size, predict=predict))
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with gr.Row():
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max_new_tokens = gr.Slider(minimum=8, maximum=4096, value=512, step=1)
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top_p = gr.Slider(minimum=0.01, maximum=1, value=0.7, step=0.01)
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temperature = gr.Slider(minimum=0.01, maximum=1.5, value=0.95, step=0.01)
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output_dir = gr.Textbox()
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input_elems.update({max_new_tokens, top_p, temperature, output_dir})
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elem_dict.update(dict(max_new_tokens=max_new_tokens, top_p=top_p, temperature=temperature, output_dir=output_dir))
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with gr.Row():
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cmd_preview_btn = gr.Button()
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start_btn = gr.Button(variant="primary")
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stop_btn = gr.Button(variant="stop")
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with gr.Row():
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resume_btn = gr.Checkbox(visible=False, interactive=False)
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progress_bar = gr.Slider(visible=False, interactive=False)
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with gr.Row():
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output_box = gr.Markdown()
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elem_dict.update(
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dict(
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cmd_preview_btn=cmd_preview_btn,
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start_btn=start_btn,
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stop_btn=stop_btn,
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resume_btn=resume_btn,
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progress_bar=progress_bar,
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output_box=output_box,
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)
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)
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output_elems = [output_box, progress_bar]
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cmd_preview_btn.click(engine.runner.preview_eval, input_elems, output_elems, concurrency_limit=None)
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start_btn.click(engine.runner.run_eval, input_elems, output_elems)
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stop_btn.click(engine.runner.set_abort)
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resume_btn.change(engine.runner.monitor, outputs=output_elems, concurrency_limit=None)
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dataset.focus(list_datasets, [dataset_dir], [dataset], queue=False)
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return elem_dict
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