mirror of
https://github.com/hiyouga/LLaMA-Factory.git
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78 lines
3.3 KiB
Python
78 lines
3.3 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 ...data import TEMPLATES
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from ...extras.constants import METHODS, SUPPORTED_MODELS
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from ...extras.packages import is_gradio_available
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from ..common import get_model_info, list_checkpoints, save_config
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from ..utils import can_quantize, can_quantize_to
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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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def create_top() -> Dict[str, "Component"]:
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available_models = list(SUPPORTED_MODELS.keys()) + ["Custom"]
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with gr.Row():
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lang = gr.Dropdown(choices=["en", "ru", "zh"], scale=1)
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model_name = gr.Dropdown(choices=available_models, scale=3)
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model_path = gr.Textbox(scale=3)
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with gr.Row():
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finetuning_type = gr.Dropdown(choices=METHODS, value="lora", scale=1)
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checkpoint_path = gr.Dropdown(multiselect=True, allow_custom_value=True, scale=6)
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with gr.Accordion(open=False) as advanced_tab:
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with gr.Row():
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quantization_bit = gr.Dropdown(choices=["none", "8", "4"], value="none", allow_custom_value=True, scale=1)
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quantization_method = gr.Dropdown(choices=["bitsandbytes", "hqq", "eetq"], value="bitsandbytes", scale=1)
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template = gr.Dropdown(choices=list(TEMPLATES.keys()), value="default", scale=1)
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rope_scaling = gr.Radio(choices=["none", "linear", "dynamic"], value="none", scale=2)
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booster = gr.Radio(choices=["auto", "flashattn2", "unsloth"], value="auto", scale=2)
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visual_inputs = gr.Checkbox(scale=1)
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model_name.change(get_model_info, [model_name], [model_path, template, visual_inputs], queue=False).then(
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list_checkpoints, [model_name, finetuning_type], [checkpoint_path], queue=False
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)
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model_name.input(save_config, inputs=[lang, model_name], queue=False)
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model_path.input(save_config, inputs=[lang, model_name, model_path], queue=False)
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finetuning_type.change(can_quantize, [finetuning_type], [quantization_bit], queue=False).then(
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list_checkpoints, [model_name, finetuning_type], [checkpoint_path], queue=False
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)
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checkpoint_path.focus(list_checkpoints, [model_name, finetuning_type], [checkpoint_path], queue=False)
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quantization_method.change(can_quantize_to, [quantization_method], [quantization_bit], queue=False)
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return dict(
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lang=lang,
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model_name=model_name,
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model_path=model_path,
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finetuning_type=finetuning_type,
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checkpoint_path=checkpoint_path,
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advanced_tab=advanced_tab,
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quantization_bit=quantization_bit,
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quantization_method=quantization_method,
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template=template,
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rope_scaling=rope_scaling,
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booster=booster,
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visual_inputs=visual_inputs,
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)
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