Unable to load *ANY BASE MODEL* in 4bit

[ApoorvFrontera]

Hi VideoLLaMA Team,

I am facing issues while loading all the base models in 4-bit precision. The following lines try to load the mm_projector_weights which are stored in 16-bit precision into a model that requires the weights in 4bit leading to errors:

Code used for loading the models for inference

  model_path = 'DAMO-NLP-SG/VideoLLaMA2-8x7B-Base'
  model, processor, tokenizer = model_init(model_path, load_4bit=True)

Problematic part of the Code: Lines: https://github.com/DAMO-NLP-SG/VideoLLaMA2/blob/main/videollama2/model/__init__.py#L171-L172

mm_projector_weights = load_mm_projector(model_path, token=token)
model.load_state_dict(mm_projector_weights, strict=False)

Error:

RuntimeError: Error(s) in loading state_dict for Videollama2MistralForCausalLM:
size mismatch for model.mm_projector.readout.0.weight: copying a param with shape torch.Size([4096, 4096]) from checkpoint, the shape in current model is torch.Size([8388608, 1]).
size mismatch for model.mm_projector.readout.2.weight: copying a param with shape torch.Size([4096, 4096]) from checkpoint, the shape in current model is torch.Size([8388608, 1]).

How can we use the 16-bit stored weights of the mm_projector_weights in 4-bit models?

[clownrat6]

According to this document BitsAndBytesConfig, all of the linear layers will be replaced by FP4/NF4 layers if setting load_4bit. Therefore, it reports size mismatch error.

A temporary solution is to initialize a unquantified model, load projector weights, and save the whole model weights. The saved weights can be loaded successfully with load_4bit=True.

model, processor, tokenizer = model_init('DAMO-NLP-SG/VideoLLaMA2-7B-Base')
model.config.tune_mm_mlp_adapter = False
model.save_pretrained('VideoLLaMA2-7B-full')
tokenizer.save_pretrained('VideoLLaMA2-7B-full')

model, processor, tokenizer = model_init('VideoLLaMA2-7B-full', load_4bit=True)