OpenDeltaMirror/docs/source/notes/saveload.md

Save and Share the Delta

Space efficient saving without changing the code.

After a modified backbone model is trained, you can save only trained part without change to any code, because the state dict of the backbone model has been changed to the trainable parts

from opendelta import CompacterModel
from transformers import BertForMaskedLM
backbone_model = BertForMaskedLM.from_pretrained("bert-base-uncased")
delta_model = CompacterModel(backbone_model) # modify the default modules.

# freeze module
delta_model.freeze_module(exclude=["deltas"], set_state_dict=True)
# or
delta_model.freeze_module(exclude=["deltas"])

save the checkpoint.

now save the backbone_model in normal way, and the checkpoint is very space efficient.

# ...
# After some training pipeline
# ...
torch.save(backbone_model.state_dict(), "delta.ckpt")

# the checkpoint size
import os
print("checkpoint size: {:.2f}M".format(os.path.getsize("delta.ckpt")/1024**2))
# checkpoint size: 0.32M

load the checkpoint.

In order to load the checkpoint, you should make sure the backbone model is a modified ones (so that it can take in the delta parameters). Then load the checkpoint with strict=False.

backbone_model.load_state_dict(torch.load("delta.ckpt"), strict=False)
# this will return long string of warning about the 'missing key'.
# if you want to supress it, use
# _ = backbone_model.load_state_dict(torch.load("delta.ckpt"), strict=False)

Save/Load the entire model after training.

save a delta model.

delta_model.save_finetuned("delta_model")
# Configuration saved in delta_model/config.json
# Model weights saved in delta_model/pytorch_model.bin

This will save all the trained parameters and the configuration of the delta model to path delta_model/

load a delta model.

backbone_model = BertForMaskedLM.from_pretrained("bert-base-uncased")
delta_model.from_finetuned("delta_model", backbone_model, local_files_only=True) 
# passing local_files_only=True will save the time of checking in the web.

Share or download a model to/from the community.

Share.

delta_model.save_finetuned("test_delta_model", push_to_hub = True)

Download from community.

from transformers import AutoModelForSeq2SeqLM
t5 = AutoModelForSeq2SeqLM.from_pretrained("t5-base")
from opendelta import AutoDeltaModel
delta = AutoDeltaModel.from_finetuned("DeltaHub/lora_t5-base_mrpc", backbone_model=t5)
delta.log()

**Push to Hub**

Currently we only provide the option to push to huggingface model hub.

Before push to hub, you may need to register an account on Huggingface. You can refer to this [tutorial about model sharing and uploading](https://huggingface.co/docs/transformers/model_sharing)

In some cases, your checkpoint is still large for git, please install [`git-lfs`](https://git-lfs.github.com).

:::{admonition} Sharing with the Community :class: tip If you are satisfied with your checkpoint, do not forget to share your model to DeltaHub:

1. Add yourself to DeltaHub with the public link
2. Be sure to edit your model card to clearly illustrate the delta model before you share.
3. Click setting on the model
4. Transfer the model in rename or transfer this model section. :::

Save & Load for Composition of Delta

Currently save & load method is not suitable for composition of delta model. Please wait for future releases.