Can I load the parameters of llava1.5 with full parameter fine-tuning

[zhangzef]

Describe the issue

Issue: I want to fine-tune the full parameters of llava1.5, not only the llm and projector, but also the vision module. However, after I saved the trained parameters into ckpt files, I found that when loading llava in load_pretrained_model function, vision tower would not be initialized from the saved ckpt, but only from the original parameters of vit, because I found that after I passed the ckpt path to the load_pretrained_model function, It will still download vit parameters from huggingface, and will report the following warning:

Command:

tokenizer, model, image_processor, context_len = load_pretrained_model(
        model_path, model_base=None,model_name=model_name, device_map={"": 'cuda'})

Log:

Some weights of the model checkpoint at ./models/RLAIF-V-7B were not used when initializing LlavaLlamaForCausalLM: ['model.vision_tower.vision_tower.vision_model.encoder.layers.7.mlp.fc2.weight', 'model.vision_tower.vision_tower.vision_model.encoder.layers.7.layer_norm1.bias', 'model.vision_tower.vision_tower.vision_model.encoder.layers.20.self_attn.v_proj.bias', 'model.vision_tower.vision_tower.vision_model.encoder.layers.20.self_attn.out_proj.weight', 'model.vision_tower.vision_tower.vision_model.encoder.layers.6.layer_norm2.bias', 'model.vision_tower.vision_tower.vision_model.encoder.layers.9.mlp.fc1.bias', 'model.vision_tower.vision_tower.vision_model.encoder.layers.13.mlp.fc2.bias', 'model.vision_tower.vision_tower.vision_model.encoder.layers.14.layer_norm1.bias', 'model.vision_tower.vision_tower.vision_model.encoder.layers.21.mlp.fc2.weight', 'model.vision_tower.vision_tower.vision_model.encoder.layers.5.mlp.fc2.weight', 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- This IS expected if you are initializing LlavaLlamaForCausalLM from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing LlavaLlamaForCausalLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
/root/miniconda3/lib/python3.12/site-packages/transformers/modeling_utils.py:484: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.

Looking at the load_pretrained_model function source code, I found that there was no mechanism to modify the vision tower name when initializing vision tower, and there was no local ckpt code logic to load. I can only change the loading address of vision tower by changing mm_vision_tower in llava config, but if I change mm_vision_tower to the path of ckpt, the model will fail to load.

Screenshots: You may attach screenshots if it better explains the issue.