| |
| from typing import Type |
|
|
| import gradio as gr |
|
|
| from swift.llm.dataset.register import get_dataset_list |
| from swift.ui.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) |
|
|
