Comments (2)
First a clarifying question: When you speak of adapter heads, do you mean that you added them when training the model by setting LoraConfig(modules_to_save=...)
? Note that if you did not set it explicitly but used a preconfigured model, it might have been set automatically (check the adapter_config.json
).
2. When merging multiple adapters using
add_weighted_adapter
, I would expect the corresponding adapter heads to be merged as well so that the merged adapter can handle the combined tasks of the individual adapters.
This is definitely not done. In fact, there is no trivial way to merge adapter heads in a way that would preserve their functionality.
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Related Issues (20)
- [doc] issues with rendering docs in FF HOT 6
- [docs] usability HOT 2
- Adapters cannot be correctly loaded using PeftModel.from_pretrained() HOT 3
- Problem with merging AdaLoRA adapters HOT 1
- model prediction differs before and after saving the model HOT 2
- Backward compatibility on saved config. HOT 4
- Training with out Trainer class. (VRAM usage issue) HOT 4
- Integrating ReFT HOT 5
- Learning the combination weights of pre-trained LoRA Modules HOT 4
- RuntimeError: CUDA error: invalid device ordinal HOT 1
- TypeError: PeftConfig.__init__() got an unexpected keyword argument '_name_or_path' 报错怎么解决 HOT 6
- `bitsandbytes` is imported eagerly in `peft/utils/loftq_utils.py` HOT 5
- Inquiry on Inserting P-Tuning Soft Prompts at Any Position in Input HOT 1
- Updating only one adapter using multi adapter HOT 1
- Using PEFT causes model to not predict EOS HOT 10
- How to set Lora_dropout=0 when loading trained peft model for inference? HOT 2
- [FSDP+QLoRA] ValueError: Expected a cuda device, but got: cpu HOT 5
- DoRA support for Embedding HOT 1
- Loading trained peft model results in random adapter weights each time HOT 4
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