Spaces:
Running
Running
File size: 3,149 Bytes
e81015c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 |
# Copyright 2025 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
from ...data import TEMPLATES
from ...extras.constants import METHODS, SUPPORTED_MODELS
from ...extras.packages import is_gradio_available
from ..common import save_config
from ..control import can_quantize, can_quantize_to, get_model_info, list_checkpoints
if is_gradio_available():
import gradio as gr
if TYPE_CHECKING:
from gradio.components import Component
def create_top() -> dict[str, "Component"]:
with gr.Row():
lang = gr.Dropdown(choices=["en", "ru", "zh", "ko", "ja"], value=None, scale=1)
available_models = list(SUPPORTED_MODELS.keys()) + ["Custom"]
model_name = gr.Dropdown(choices=available_models, value=None, 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.Row():
quantization_bit = gr.Dropdown(choices=["none", "8", "4"], value="none", allow_custom_value=True)
quantization_method = gr.Dropdown(choices=["bnb", "hqq", "eetq"], value="bnb")
template = gr.Dropdown(choices=list(TEMPLATES.keys()), value="default")
rope_scaling = gr.Dropdown(choices=["none", "linear", "dynamic", "yarn", "llama3"], value="none")
booster = gr.Dropdown(choices=["auto", "flashattn2", "unsloth", "liger_kernel"], value="auto")
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,
quantization_bit=quantization_bit,
quantization_method=quantization_method,
template=template,
rope_scaling=rope_scaling,
booster=booster,
)
|