huggingface runtime_error ai_generated true

RuntimeError: PEFT adapter weight shape mismatch: expected [4096, 4096] but got [4096, 2048]

ID: huggingface/peft-adapter-shape-mismatch

Also available as: JSON · Markdown · 中文
92%Fix Rate
88%Confidence
1Evidence
2024-02-10First Seen

Version Compatibility

VersionStatusIntroducedDeprecatedNotes
peft>=0.5.0 active — — —
transformers>=4.30.0 active — — —
torch>=1.13.0 active — — —

Root Cause

The PEFT adapter was trained on a model with different hidden dimensions (e.g., a smaller variant) and is being loaded onto a model with incompatible dimensions.

generic

中文

PEFT 适配器是在不同隐藏维度的模型上训练的（例如较小的变体），并被加载到维度不兼容的模型上。

Official Documentation

https://huggingface.co/docs/peft/troubleshooting#adapter-weight-shape-mismatch

Workarounds

  1. 95% success Verify the base model used for adapter training: load the correct base model with matching hidden size: from transformers import AutoModel; model = AutoModel.from_pretrained('original-base-model'); model.load_adapter('./adapter_path') 
    Verify the base model used for adapter training: load the correct base model with matching hidden size: from transformers import AutoModel; model = AutoModel.from_pretrained('original-base-model'); model.load_adapter('./adapter_path')
  2. 90% success Check adapter config metadata: print(PeftConfig.from_pretrained('./adapter_path').base_model_name_or_path) to identify the correct base model. 
    Check adapter config metadata: print(PeftConfig.from_pretrained('./adapter_path').base_model_name_or_path) to identify the correct base model.

中文步骤

  1. 验证适配器训练使用的基础模型：加载具有匹配隐藏大小的正确基础模型：from transformers import AutoModel; model = AutoModel.from_pretrained('original-base-model'); model.load_adapter('./adapter_path')
  2. 检查适配器配置元数据：print(PeftConfig.from_pretrained('./adapter_path').base_model_name_or_path) 以识别正确的基础模型。

Dead Ends

Common approaches that don't work:

  1. Force load the adapter with `strict=False` to ignore mismatched layers 90% fail

    The model will silently drop or partially load weights, leading to undefined behavior and poor performance.

  2. Manually resize the adapter weights using interpolation 85% fail

    Adapters are not spatially structured; interpolation can break the learned patterns and cause numerical instability.

  3. Set `torch.set_default_dtype(torch.float16)` to avoid shape errors 100% fail

    Dtype does not affect tensor shape; shape mismatch is a structural issue, not a precision issue.