Spaces:
Running
Running
# 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 | |
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"], 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", 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=["none", "flashattn2", "unsloth"], value="none", scale=3) | |
visual_inputs = gr.Checkbox(scale=1) | |
model_name.change(get_model_info, [model_name], [model_path, template, visual_inputs], 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) | |
checkpoint_path.focus(list_checkpoints, [model_name, finetuning_type], [checkpoint_path], 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, | |
template=template, | |
rope_scaling=rope_scaling, | |
booster=booster, | |
visual_inputs=visual_inputs, | |
) | |