请问chatglm模型Lora微调完成之后，如何加载新模型？ about self-llm

[Hongru0306]

可以先设置save_strategy=5,看一下输出的路径在哪里。curl是指加载你自己lora后的模型吗？这个需要你合并模型后将相应部分推到modelscope或者hf上才可以的。

[waynetest2024]

好的，我再试一下。之前试过调用save_pretrained()一直报错，提示不是json格式。
curl指的是“ChatGLM3-6B FastApi 部署调用”这一章里面介绍的办法，不过这个应该不是大问题，主要是前一步没有解决。
感谢回复！

[waynetest2024]

可以先设置save_strategy=5,看一下输出的路径在哪里。curl是指加载你自己lora后的模型吗？这个需要你合并模型后将相应部分推到modelscope或者hf上才可以的。

现在这个参数改为save_strategy='epoch'了，不过加上去之后依然报错，报错信息如下。感觉像是版本的问题，不过python包我都是按照项目示例给的版本安装的。
Traceback (most recent call last):
File "train.py", line 79, in
trainer.train()
File "/root/miniconda3/lib/python3.8/site-packages/transformers/trainer.py", line 1539, in train
return inner_training_loop(
File "/root/miniconda3/lib/python3.8/site-packages/transformers/trainer.py", line 1944, in _inner_training_loop
self._maybe_log_save_evaluate(tr_loss, model, trial, epoch, ignore_keys_for_eval)
File "/root/miniconda3/lib/python3.8/site-packages/transformers/trainer.py", line 2302, in _maybe_log_save_evaluate
self._save_checkpoint(model, trial, metrics=metrics)
File "/root/miniconda3/lib/python3.8/site-packages/transformers/trainer.py", line 2378, in _save_checkpoint
self.save_model(staging_output_dir, _internal_call=True)
File "/root/miniconda3/lib/python3.8/site-packages/transformers/trainer.py", line 2886, in save_model
self._save(output_dir)
File "/root/miniconda3/lib/python3.8/site-packages/transformers/trainer.py", line 2958, in _save
self.model.save_pretrained(
File "/root/miniconda3/lib/python3.8/site-packages/peft/peft_model.py", line 201, in save_pretrained
peft_config.save_pretrained(output_dir, auto_mapping_dict=auto_mapping_dict)
File "/root/miniconda3/lib/python3.8/site-packages/peft/utils/config.py", line 92, in save_pretrained
writer.write(json.dumps(output_dict, indent=2, sort_keys=True))
File "/root/miniconda3/lib/python3.8/json/init.py", line 234, in dumps
return cls(
File "/root/miniconda3/lib/python3.8/json/encoder.py", line 201, in encode
chunks = list(chunks)
File "/root/miniconda3/lib/python3.8/json/encoder.py", line 431, in _iterencode
yield from _iterencode_dict(o, _current_indent_level)
File "/root/miniconda3/lib/python3.8/json/encoder.py", line 405, in _iterencode_dict
yield from chunks
File "/root/miniconda3/lib/python3.8/json/encoder.py", line 438, in _iterencode
o = _default(o)
File "/root/miniconda3/lib/python3.8/json/encoder.py", line 179, in default
raise TypeError(f'Object of type {o.class.name} '
TypeError: Object of type set is not JSON serializable
100%|██████████| 466/466 [07:32<00:00, 1.03it/s]

[liyunhan]

@waynetest2024 我想知道您微调多大的模型，多少数据大概用了多久？ 我1W的训练数据，LoRA微调qwen1.5-32b-chat在A6000上慢的要死....batch我设置的16，一个batch就恨不得一分钟

[waynetest2024]

@waynetest2024 我想知道您微调多大的模型，多少数据大概用了多久？ 我1W的训练数据，LoRA微调qwen1.5-32b-chat在A6000上慢的要死....batch我设置的16，一个batch就恨不得一分钟

就是demo里的模型和数据，chatglm3-6b、huanhuan.json，4090上几分钟跑一趟吧。我只是熟悉下基本流程，要求比较低

[Hongru0306]

现在这个参数改为save_strategy='epoch'了，不过加上去之后依然报错，报错信息如下。感觉像是版本的问题，不过python包我都是按照项目示例给的版本安装的。

您好，测试的话不用设置为epoch的，直接设置为iter，然后每5个iter就保存一下，看看是否正常。如果实在解决不了，后续我会创一个没问题的环境推到autodl上，更新后附到repo的相关链接中。

[Hongru0306]

可以先设置save_strategy=5,看一下输出的路径在哪里。curl是指加载你自己lora后的模型吗？这个需要你合并模型后将相应部分推到modelscope或者hf上才可以的。

现在这个参数改为save_strategy='epoch'了，不过加上去之后依然报错，报错信息如下。感觉像是版本的问题，不过python包我都是按照项目示例给的版本安装的。 Traceback (most recent call last): File "train.py", line 79, in trainer.train() File "/root/miniconda3/lib/python3.8/site-packages/transformers/trainer.py", line 1539, in train return inner_training_loop( File "/root/miniconda3/lib/python3.8/site-packages/transformers/trainer.py", line 1944, in _inner_training_loop self._maybe_log_save_evaluate(tr_loss, model, trial, epoch, ignore_keys_for_eval) File "/root/miniconda3/lib/python3.8/site-packages/transformers/trainer.py", line 2302, in _maybe_log_save_evaluate self._save_checkpoint(model, trial, metrics=metrics) File "/root/miniconda3/lib/python3.8/site-packages/transformers/trainer.py", line 2378, in _save_checkpoint self.save_model(staging_output_dir, _internal_call=True) File "/root/miniconda3/lib/python3.8/site-packages/transformers/trainer.py", line 2886, in save_model self._save(output_dir) File "/root/miniconda3/lib/python3.8/site-packages/transformers/trainer.py", line 2958, in _save self.model.save_pretrained( File "/root/miniconda3/lib/python3.8/site-packages/peft/peft_model.py", line 201, in save_pretrained peft_config.save_pretrained(output_dir, auto_mapping_dict=auto_mapping_dict) File "/root/miniconda3/lib/python3.8/site-packages/peft/utils/config.py", line 92, in save_pretrained writer.write(json.dumps(output_dict, indent=2, sort_keys=True)) File "/root/miniconda3/lib/python3.8/json/init.py", line 234, in dumps return cls( File "/root/miniconda3/lib/python3.8/json/encoder.py", line 201, in encode chunks = list(chunks) File "/root/miniconda3/lib/python3.8/json/encoder.py", line 431, in _iterencode yield from _iterencode_dict(o, _current_indent_level) File "/root/miniconda3/lib/python3.8/json/encoder.py", line 405, in _iterencode_dict yield from chunks File "/root/miniconda3/lib/python3.8/json/encoder.py", line 438, in _iterencode o = _default(o) File "/root/miniconda3/lib/python3.8/json/encoder.py", line 179, in default raise TypeError(f'Object of type {o.class.name} ' TypeError: Object of type set is not JSON serializable 100%|██████████| 466/466 [07:32<00:00, 1.03it/s]

还有一个解决思路，使用提供的ipynb进行训练，然后在训练结束后手动保存model的权重，看下保存路径在哪里。

[waynetest2024]

现在这个参数改为save_strategy='epoch'了，不过加上去之后依然报错，报错信息如下。感觉像是版本的问题，不过python包我都是按照项目示例给的版本安装的。

您好，测试的话不用设置为epoch的，直接设置为iter，然后每5个iter就保存一下，看看是否正常。如果实在解决不了，后续我会创一个没问题的环境推到autodl上，更新后附到repo的相关链接中。

哦哦，但是设置save_strategy=5会直接报错