How to save my model to use it later

Beginners
1

Hello Amazing people,
This is my first post and I am really new to machine learning and Hugginface.

I followed this awesome guide here multilabel Classification with DistilBert

and used my dataset and the results are very good. I am having a hard time know trying to understand how to save the model I trainned and all the artifacts needed to use my model later.

I tried at the end of the tutorial: torch.save(trainer, 'my_model') but I got this error msg:

AttributeError: Can't pickle local object 'get_linear_schedule_with_warmup.<locals>.lr_lambda'

I have the following files saved for each epoch:

config.json
    optimizer.pt
    pytorch_model.bin
    rng_state.pth
    special_tokens_map.json
    tokenizer.json
    tokenizer_config.json
    trainer_state.json
    training_args.bin
    vocab.txt

Can someone kindly guide me how to save this model to later use?
Thank you very much

4 Likes
2

Hello there,

You can save models with trainer.save_model("path_to_save"). Another cool thing you can do is you can push your model to the Hugging Face Hub as well. I added couple of lines to notebook to show you, here. You can find pushing there.

15 Likes
3

Thank you very much for helping me Merve. Huge Thanks.
Just one more question if you donā€™t mind: Iā€™ll now use my model locally at first. You helped me to save all the files I need to load it again.

So to use the same model I save with trainer.save_model(path) I just need to use trainer.load(path)?

Thank you very much :wink:

1 Like
4

Hello again,

You can simply load the model using the model classā€™ from_pretrained(model_path) method like below:
(you can either save locally and load from local or push to Hub and load from Hub)

from transformers import BertConfig, BertModel
# if model is on hugging face Hub
model = BertModel.from_pretrained("bert-base-uncased")
# from local folder
model = BertModel.from_pretrained("./test/saved_model/")

Another cool thing you can use is pipeline API, it will make your life much easier :slightly_smiling_face:. With pipelines, you will not have to deal with internals of the model or tokenizer to infer with the model, you simply give the folder and it will make the model ready to infer for you.

13 Likes
5

You are amazing merve :wink: Iā€™ll try do to this steps now. Letā€™s see how it goes.
Thank you again

2 Likes
6

Hello again,

So I followed that tutorial to train my model(using distilert-base-uncased).
saved the model with:

trainer.save_model("./my_model")

and then I loaded the model:

from transformers import DistilBertConfig, DistilBertModel
path = 'path_to_my_model'
model = DistilBertModel.from_pretrained(path)

Now I followed the same tutorial for inference but then I run:

encoding = tokenizer(text, return_tensors="pt")

encoding = {k: v.to(trainer.model.device) for k,v in encoding.items()}
outputs = trainer.model(**encoding)

and then:

logits = outputs.logits raises the followin error:

AttributeError: 'DistilBertModel' object has no attribute 'logits'

How can I fix this step?

Thank you very much

1 Like
7

I found the error: instead of
model = DistilBertModel.from_pretrained(path)
I changed to
model = AutoModelForSequenceClassification.from_pretrained(path)

3 Likes
8

@slowturtle Just to avoid confusion for future, the BertModel classes are simply BERT models without classification heads on top, so the heads include classification heads (and thus logit processors).

1 Like
9

Hi Merve!

I might be late but the tutorial that you have shared is excellent. My only questions is that can the same model be trained for a Multiclass text classification problem as well? If so, what parameters do I need to keep in mind while training this model? and also will this be successful for smaller datasets (<1000 records). It will be great to see if you have a notebook for this problem statement as well that I have just described

Thanks
Ishan

1 Like
10

Hi!

I run out of CUDA memory when saving a larger model using this. Is there a way I can move a gpu trained model to ā€˜cpuā€™ before saving using trainer.save_model(_). Appreciate the help, thanks!

1 Like
11

Hello. After running a distilbert model, finetuned with my own custom dataset for classification purposes, i try to save the model in a .pth file format (e.g. distilmodel.pth). After training the model using the Trainer from the pytorch library, it saves a couples of archives into a checkpoint output folder, as declared into the Trainerā€™s arguments.
Any help to convert the checkpoint into a model.pth format file?
Thanks in advance.

12

What if we want to take a base model from HuggingFace, train it, save the fine-tune model, and then train it further? I want to train the model iteratively on subsets of my data so I donā€™t have to train it all at once because it will take a few weeks to do it all at once and I am afraid it will crash towards the end and waste the experiment, as well as I want to be able to test the output in between subsets of data.

Currently, when I try to load a custom model and tokenizer, though I can generate text with the model no problem, I get the below error when I attempt to train it further:

Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu! (when checking argument for argument mat2 in method wrapper_CUDA_mm)

The thing is, this is not an issue when I train the base model model initially, but I have even tried forcing the data to be on the GPU before training and then just get the same error complaining about cuda:0 and cuda:3. I think the data moves to the GPU after training.Train() is called, and all my settings are the same besides the fact I am referencing my locally saved model and tokenizer path instead of the HuggingFace web path. Do I need to push my model to huggingface and then download from there? I looked at the folders that are cached from downloading the model and there are quite a few extra files that are cached aside from the files created when I save the model to a local folder, but any help would be very appreciated.

13

I am using this repo to run a translation task. Especially Iā€™m using it to build a diacritization model. I need to save the model after the process is done. How can I do that?

CUDA_VISIBLE_DEVICES=0 python run_translation.py --model_name_or_path Davlan/oyo-t5-small --do_train --do_eval --source_lang unyo --target_lang dcyo --source_prefix "<unyo2dcyo>: " --train_file data_prep_eng/output_data/bible_train.json --validation_file data_prep_eng/output_data/dev.json --test_file data_prep_eng/output_data/test.json --output_dir oyot5_small_unyo_dcyo_bible --max_source_length 512 --max_target_length 512 --per_device_train_batch_size=24 --per_device_eval_batch_size=24 --num_train_epochs 3 --overwrite_output_dir --predict_with_generate --save_steps 10000 --num_beams 10 --do_predict

Am I missing a flag like --save-model? I need the saved model to be part of the directory.

See what I have now:

Screenshot 2024-01-14 at 22.38.29
Screenshot 2024-01-14 at 22.38.29514Ɨ646 48.3 KB

14

Yes, you can. Assuming you are using torch:
DEVICE = ā€œcpuā€
#assuming huggingface model
your_model.to(DEVICE)

you can move the model back when loading:

GPU_DEVICE = ā€œcudaā€ if torch.cuda.is_available() else ā€œcpuā€

15

Hi, thanks for the answer. But is there a method or convention to NOT use trainer to save models?
I prefer to finetune my model by training in the traditional pytorch way because itā€™s more flexiable to add my own creativity. But I find it difficult to save it. The error message says that I shouldnā€™t use the identical checkpointing as the original model. What does it mean? Is there any method to solve it?

1 Like
16

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