weights of "vision_tower" is not exactly identically the same
dylanqyuan opened this issue · 0 comments
We tried the weights from huggingface and ModelScope, the same issue happened:
Some weights of the model checkpoint at /home/XXX/MLLM_all/Q_instruct_LLaVA-1.5-7B/llava_v1.5_7b_qinstruct_preview_v0.1_ModelScope were not used when initializing LlavaLlamaForCausalLM: ['model.vision_tower.vision_tower.vision_model.encoder.layers.14.layer_norm1.bias', 'model.vision_tower.vision_tower.vision_model.encoder.layers.7.layer_norm2.bias', 'model.vision_tower.vision_tower.vision_model.encoder.layers.12.layer_norm2.bias', 'model.vision_tower.vision_tower.vision_model.encoder.layers.7.self_attn.v_proj.bias', 'model.vision_tower.vision_tower.vision_model.encoder.layers.0.self_attn.q_proj.bias', 'model.vision_tower.vision_tower.vision_model.encoder.layers.16.layer_norm2.bias', 'model.vision_tower.vision_tower.vision_model.encoder.layers.2.self_attn.q_proj.bias', 'model.vision_tower.vision_tower.vision_model.encoder.layers.6.mlp.fc2.bias', 'model.vision_tower.vision_tower.vision_model.encoder.layers.18.layer_norm1.bias', 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'model.vision_tower.vision_tower.vision_model.encoder.layers.7.self_attn.k_proj.bias', 'model.vision_tower.vision_tower.vision_model.encoder.layers.8.self_attn.v_proj.weight', 'model.vision_tower.vision_tower.vision_model.encoder.layers.22.layer_norm1.bias', 'model.vision_tower.vision_tower.vision_model.encoder.layers.23.mlp.fc1.bias', 'model.vision_tower.vision_tower.vision_model.encoder.layers.22.self_attn.v_proj.bias', 'model.vision_tower.vision_tower.vision_model.encoder.layers.20.self_attn.k_proj.weight']`
`- 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).
`
The "vision_tower" is downloaded from huggingface (https://huggingface.co/openai/clip-vit-large-patch14-336),
Q_instruct can offer a response like following, but I do not know this results aligned with the real ability of Q-instruct 😆
The overall clarity of this image is acceptable, with sufficient lighting. The texture details of the photographed subject, the animal, are rich and clear. The colors are also rich. However, the composition is not good, as it is slightly tilted. The background is a bit blurry. Therefore, the image quality is good