wehub-resource-sync
89 lines
2.7 KiB
Python
89 lines
2.7 KiB
Python
# Copyright (c) ModelScope Contributors. All rights reserved.
|
|
import gradio as gr
|
|
from typing import Type
|
|
|
|
from swift.dataset import get_dataset_list
|
|
from ..base import BaseUI
|
|
|
|
|
|
class Export(BaseUI):
|
|
|
|
group = 'llm_export'
|
|
|
|
locale_dict = {
|
|
'merge_lora': {
|
|
'label': {
|
|
'zh': '合并LoRA',
|
|
'en': 'Merge LoRA'
|
|
},
|
|
'info': {
|
|
'zh':
|
|
'LoRA合并的路径在填入的checkpoint同级目录，请查看运行时log获取更具体的信息',
|
|
'en':
|
|
'The output path is in the sibling directory as the input checkpoint. '
|
|
'Please refer to the runtime log for more specific information.'
|
|
},
|
|
},
|
|
'device_map': {
|
|
'label': {
|
|
'zh': '合并LoRA使用的device_map',
|
|
'en': 'The device_map when merge-lora'
|
|
},
|
|
'info': {
|
|
'zh': '如果显存不够请填入cpu',
|
|
'en': 'If GPU memory is not enough, fill in cpu'
|
|
},
|
|
},
|
|
'quant_bits': {
|
|
'label': {
|
|
'zh': '量化比特数',
|
|
'en': 'Quantize bits'
|
|
},
|
|
},
|
|
'quant_method': {
|
|
'label': {
|
|
'zh': '量化方法',
|
|
'en': 'Quantize method'
|
|
},
|
|
},
|
|
'quant_n_samples': {
|
|
'label': {
|
|
'zh': '量化集采样数',
|
|
'en': 'Sampled rows from calibration dataset'
|
|
},
|
|
},
|
|
'max_length': {
|
|
'label': {
|
|
'zh': '量化集的max-length',
|
|
'en': 'The quantize sequence length'
|
|
},
|
|
},
|
|
'output_dir': {
|
|
'label': {
|
|
'zh': '输出路径',
|
|
'en': 'Output dir'
|
|
},
|
|
},
|
|
'dataset': {
|
|
'label': {
|
|
'zh': '校准数据集',
|
|
'en': 'Calibration datasets'
|
|
},
|
|
},
|
|
}
|
|
|
|
@classmethod
|
|
def do_build_ui(cls, base_tab: Type['BaseUI']):
|
|
with gr.Row():
|
|
gr.Checkbox(elem_id='merge_lora', scale=10)
|
|
gr.Textbox(elem_id='device_map', scale=20)
|
|
with gr.Row():
|
|
gr.Dropdown(elem_id='quant_bits', scale=20)
|
|
gr.Dropdown(elem_id='quant_method', scale=20)
|
|
gr.Textbox(elem_id='quant_n_samples', scale=20)
|
|
gr.Textbox(elem_id='max_length', scale=20)
|
|
with gr.Row():
|
|
gr.Textbox(elem_id='output_dir', scale=20)
|
|
gr.Dropdown(
|
|
elem_id='dataset', multiselect=True, allow_custom_value=True, choices=get_dataset_list(), scale=20)
|