新版本代替form_for_model

本文介绍Django 1.0中表单创建方式的变化,原先使用的newforms.form_for_model()和newforms.form_for_instance()已被移除,取而代之的是forms.models.modelform_factory()。

1.0之前的newforms.form_for_model()和 newforms.form_for_instance()在1.0的forms里面都没有了

可以用forms.models.modelform_factory()

报错[INFO|dynamic_module_utils.py:423] 2025-11-25 15:47:39,543 >> Could not locate the custom_generate/generate.py inside /home/z30041960/bdpv300/ai-data-vi/models/Qwen/Qwen3-8B/origin_model/Qwen3-8B-Thinking. [INFO|2025-11-25 15:47:39] llamafactory.model.model_utils.attention:143 >> Using torch SDPA for faster training and inference. Traceback (most recent call last): File "/home/z30041960/bdpv300/.venv/lib/python3.10/site-packages/peft/config.py", line 262, in _get_peft_type config_file = hf_hub_download( File "/home/z30041960/bdpv300/.venv/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 106, in _inner_fn validate_repo_id(arg_value) File "/home/z30041960/bdpv300/.venv/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 154, in validate_repo_id raise HFValidationError( huggingface_hub.errors.HFValidationError: Repo id must be in the form 'repo_name' or 'namespace/repo_name': '/home/z30041960/bdpv300/ai-data-vi/models/Qwen/Qwen3-8B/export_model/train_2025-11-21-17-45-43'. Use `repo_type` argument if needed. During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/home/z30041960/bdpv300/.venv/bin/llamafactory-cli", line 33, in <module> sys.exit(load_entry_point('llamafactory==0.9.4.dev0', 'console_scripts', 'llamafactory-cli')()) File "/home/z30041960/bdpv300/.venv/lib/python3.10/site-packages/llamafactory-0.9.4.dev0-py3.10.egg/llamafactory/cli.py", line 24, in main launcher.launch() File "/home/z30041960/bdpv300/.venv/lib/python3.10/site-packages/llamafactory-0.9.4.dev0-py3.10.egg/llamafactory/launcher.py", line 151, in launch export_model() File "/home/z30041960/bdpv300/.venv/lib/python3.10/site-packages/llamafactory-0.9.4.dev0-py3.10.egg/llamafactory/train/tuner.py", line 138, in export_model model = load_model(tokenizer, model_args, finetuning_args) # must after fixing tokenizer to resize vocab File "/home/z30041960/bdpv300/.venv/lib/python3.10/site-packages/llamafactory-0.9.4.dev0-py3.10.egg/llamafactory/model/loader.py", line 184, in load_model model = init_adapter(config, model, model_args, finetuning_args, is_trainable) File "/home/z30041960/bdpv300/.venv/lib/python3.10/site-packages/llamafactory-0.9.4.dev0-py3.10.egg/llamafactory/model/adapter.py", line 322, in init_adapter model = _setup_lora_tuning( File "/home/z30041960/bdpv300/.venv/lib/python3.10/site-packages/llamafactory-0.9.4.dev0-py3.10.egg/llamafactory/model/adapter.py", line 186, in _setup_lora_tuning model: LoraModel = PeftModel.from_pretrained(model, adapter, **init_kwargs) File "/home/z30041960/bdpv300/.venv/lib/python3.10/site-packages/peft/peft_model.py", line 440, in from_pretrained PeftConfig._get_peft_type( File "/home/z30041960/bdpv300/.venv/lib/python3.10/site-packages/peft/config.py", line 268, in _get_peft_type raise ValueError(f"Can't find '{CONFIG_NAME}' at '{model_id}'") ValueError: Can't find 'adapter_config.json' at '/home/z30041960/bdpv300/ai-data-vi/models/Qwen/Qwen3-8B/export_model/train_2025-11-21-17-45-43'
11-26
Internal Server Error: /admin/papers/paper/2/change/ Traceback (most recent call last): File "C:\Python310\lib\site-packages\django\core\handlers\exception.py", line 55, in inner response = get_response(request) File "C:\Python310\lib\site-packages\django\core\handlers\base.py", line 197, in _get_response response = wrapped_callback(request, *callback_args, **callback_kwargs) File "C:\Python310\lib\site-packages\django\contrib\admin\options.py", line 719, in wrapper return self.admin_site.admin_view(view)(*args, **kwargs) File "C:\Python310\lib\site-packages\django\utils\decorators.py", line 192, in _view_wrapper result = _process_exception(request, e) File "C:\Python310\lib\site-packages\django\utils\decorators.py", line 190, in _view_wrapper response = view_func(request, *args, **kwargs) File "C:\Python310\lib\site-packages\django\views\decorators\cache.py", line 80, in _view_wrapper response = view_func(request, *args, **kwargs) File "C:\Python310\lib\site-packages\django\contrib\admin\sites.py", line 246, in inner return view(request, *args, **kwargs) File "C:\Python310\lib\site-packages\django\contrib\admin\options.py", line 1987, in change_view return self.changeform_view(request, object_id, form_url, extra_context) File "C:\Python310\lib\site-packages\django\utils\decorators.py", line 48, in _wrapper return bound_method(*args, **kwargs) File "C:\Python310\lib\site-packages\django\utils\decorators.py", line 192, in _view_wrapper result = _process_exception(request, e) File "C:\Python310\lib\site-packages\django\utils\decorators.py", line 190, in _view_wrapper response = view_func(request, *args, **kwargs) File "C:\Python310\lib\site-packages\django\contrib\admin\options.py", line 1843, in changeform_view return self._changeform_view(request, object_id, form_url, extra_context) File "C:\Python310\lib\site-packages\django\contrib\admin\options.py", line 1895, in _changeform_view self.save_related(request, form, formsets, not add) File "C:\Python310\lib\site-packages\django\contrib\admin\options.py", line 1342, in save_related self.save_formset(request, form, formset, change=change) File "C:\Python310\lib\site-packages\django\contrib\admin\options.py", line 1330, in save_formset formset.save() File "C:\Python310\lib\site-packages\django\forms\models.py", line 796, in save return self.save_existing_objects(commit) + self.save_new_objects(commit) File "C:\Python310\lib\site-packages\django\forms\models.py", line 959, in save_new_objects self.new_objects.append(self.save_new(form, commit=commit)) File "D:\pythonSpace\hitest\apps\papers\admin.py", line 21, in save_new max_order = instance.order.aggregate( AttributeError: 'int' object has no attribute 'aggregate'
10-23
提供了基于BP(Back Propagation)神经网络结合PID(比例-积分-微分)控制策略的Simulink仿真模型。该模型旨在实现对杨艺所著论文《基于S函数的BP神经网络PID控制器及Simulink仿真》中的理论进行实践验证。在Matlab 2016b环境下开发,经过测试,确保能够正常运行,适合学习和研究神经网络在控制系统中的应用。 特点 集成BP神经网络:模型中集成了BP神经网络用于提升PID控制器的性能,使之能更好地适应复杂控制环境。 PID控制优化:利用神经网络的自学习能力,对传统的PID控制算法进行了智能调整,提高控制精度和稳定性。 S函数应用:展示了如何在Simulink中通过S函数嵌入MATLAB代码,实现BP神经网络的定制化逻辑。 兼容性说明:虽然开发于Matlab 2016b,但理论上兼容后续版本,可能会需要调整少量配置以适配不同版本的Matlab。 使用指南 环境要求:确保你的电脑上安装有Matlab 2016b或更高版本。 模型加载: 下载本仓库到本地。 在Matlab中打开.slx文件。 运行仿真: 调整模型参数前,请先熟悉各模块功能和输入输出设置。 运行整个模型,观察控制效果。 参数调整: 用户可以自由调节神经网络的层数、节点数以及PID控制器的参数,探索不同的控制性能。 学习和修改: 通过阅读模型中的注释和查阅相关文献,加深对BP神经网络与PID控制结合的理解。 如需修改S函数内的MATLAB代码,建议有一定的MATLAB编程基础。
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