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| # Copyright 2024 the LlamaFactory team. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| from typing import TYPE_CHECKING, Dict | |
| from ...data import TEMPLATES | |
| from ...extras.constants import METHODS, SUPPORTED_MODELS | |
| from ...extras.packages import is_gradio_available | |
| from ..common import get_model_info, list_checkpoints, save_config | |
| from ..utils import can_quantize, can_quantize_to | |
| if is_gradio_available(): | |
| import gradio as gr | |
| if TYPE_CHECKING: | |
| from gradio.components import Component | |
| def create_top() -> Dict[str, "Component"]: | |
| available_models = list(SUPPORTED_MODELS.keys()) + ["Custom"] | |
| with gr.Row(): | |
| lang = gr.Dropdown(choices=["en", "ru", "zh", "ko"], scale=1) | |
| model_name = gr.Dropdown(choices=available_models, scale=3) | |
| model_path = gr.Textbox(scale=3) | |
| with gr.Row(): | |
| finetuning_type = gr.Dropdown(choices=METHODS, value="lora", scale=1) | |
| checkpoint_path = gr.Dropdown(multiselect=True, allow_custom_value=True, scale=6) | |
| with gr.Accordion(open=False) as advanced_tab: | |
| with gr.Row(): | |
| quantization_bit = gr.Dropdown(choices=["none", "8", "4"], value="none", allow_custom_value=True, scale=2) | |
| quantization_method = gr.Dropdown(choices=["bitsandbytes", "hqq", "eetq"], value="bitsandbytes", scale=2) | |
| template = gr.Dropdown(choices=list(TEMPLATES.keys()), value="default", scale=2) | |
| rope_scaling = gr.Radio(choices=["none", "linear", "dynamic"], value="none", scale=3) | |
| booster = gr.Radio(choices=["auto", "flashattn2", "unsloth", "liger_kernel"], value="auto", scale=5) | |
| model_name.change(get_model_info, [model_name], [model_path, template], queue=False).then( | |
| list_checkpoints, [model_name, finetuning_type], [checkpoint_path], queue=False | |
| ) | |
| model_name.input(save_config, inputs=[lang, model_name], queue=False) | |
| model_path.input(save_config, inputs=[lang, model_name, model_path], queue=False) | |
| finetuning_type.change(can_quantize, [finetuning_type], [quantization_bit], queue=False).then( | |
| list_checkpoints, [model_name, finetuning_type], [checkpoint_path], queue=False | |
| ) | |
| checkpoint_path.focus(list_checkpoints, [model_name, finetuning_type], [checkpoint_path], queue=False) | |
| quantization_method.change(can_quantize_to, [quantization_method], [quantization_bit], queue=False) | |
| return dict( | |
| lang=lang, | |
| model_name=model_name, | |
| model_path=model_path, | |
| finetuning_type=finetuning_type, | |
| checkpoint_path=checkpoint_path, | |
| advanced_tab=advanced_tab, | |
| quantization_bit=quantization_bit, | |
| quantization_method=quantization_method, | |
| template=template, | |
| rope_scaling=rope_scaling, | |
| booster=booster, | |
| ) | |