Different caption from https://llava.hliu.cc/ and llava.eval.run_llava

[viyjy]

Question

Hi, I have tried https://llava.hliu.cc/ and llava.eval.run_llava on the same image and query, but the generated captions are different, any ideas? Thanks

Since the model used in https://llava.hliu.cc/ is LLaVA-v1-13B-336px, I use this checkpoint for llava.eval.run_llava, is that correct?

[WLpub]

he same problem as yours, did you solve it? Also, my code is throwing errors, not sure if it's related to this.

Some weights of the model checkpoint at /copilot/149611/llava-v1-0719-336px-lora-merge-vicuna-13b-v1.3 were not used when initializing LlavaLlamaForCausalLM

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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). Some weights of the model checkpoint at /copilot/149611/llava-v1-0719-336px-lora-merge-vicuna-13b-v1.3/openai/clip-vit-large-patch14-336 were not used when initializing CLIPVisionModel: ['text_model.encoder.layers.2.self_attn.k_proj.bias', 'text_model.encoder.layers.4.self_attn.q_proj.weight', 'text_model.encoder.layers.10.self_attn.out_proj.weight', 'text_model.encoder.layers.6.self_attn.out_proj.bias', 'text_model.encoder.layers.11.layer_norm1.weight', 'text_model.encoder.layers.3.layer_norm2.weight', 'text_model.encoder.layers.7.mlp.fc1.weight', 'text_model.encoder.layers.2.layer_norm1.bias', 'text_model.encoder.layers.9.mlp.fc1.weight', 'text_model.encoder.layers.10.self_attn.v_proj.weight', 'text_model.encoder.layers.11.self_attn.out_proj.bias', 'text_model.encoder.layers.4.self_attn.out_proj.bias', 'text_model.encoder.layers.2.self_attn.out_proj.bias', 'text_model.encoder.layers.3.self_attn.out_proj.bias', 'text_model.embeddings.position_embedding.weight', 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• This IS expected if you are initializing CLIPVisionModel 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 CLIPVisionModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).

[yash-s20]

Same issue. I think the vision tower is not loading correctly for me

[LemonWade]

(llava) root@ubuntu:/data/zzy/LLaVA# python -m llava.serve.cli --model-path /data/zzy/LLaVA/checkpoints/llava-llama-2-7b-chat-finetune-robot --image-file /data/zzy/LLaVA/dataset_fintue/saved_images/000_000_075.png --load-4bit [2023-09-12 10:34:12,806] [INFO] [real_accelerator.py:110:get_accelerator] Setting ds_accelerator to cuda (auto detect) Loading checkpoint shards: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:10<00:00, 5.01s/it] Some weights of the model checkpoint at /data/zzy/LLaVA/checkpoints/llava-llama-2-7b-chat-finetune-robot were not used when initializing LlavaLlamaForCausalLM: ['model.vision_tower.vision_tower.vision_model.encoder.layers.14.layer_norm2.weight', 'model.vision_tower.vision_tower.vision_model.encoder.layers.3.self_attn.k_proj.bias', 'model.vision_tower.vision_tower.vision_model.encoder.layers.7.self_attn.v_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). Some weights of LlavaLlamaForCausalLM were not initialized from the model checkpoint at /data/zzy/LLaVA/checkpoints/llava-llama-2-7b-chat-finetune-robot and are newly initialized: ['model.layers.7.self_attn.rotary_emb.inv_freq', 'model.layers.4.self_attn.rotary_emb.inv_freq', 'model.layers.31.self_attn.rotary_emb.inv_freq', ..........] You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.

Although there are errors, the output indeed looks like it has been fine-tuned. How can these errors be fixed? Thanks in advance。

[FHL1998]

Facing the same issue here. Has anyone solved the problem?

[haotian-liu]

This may be related to https://github.com/haotian-liu/LLaVA/pull/508