I can't find `7b_v0` weight

[Rocky77JHxu]

I follow the following link（ https://huggingface.co/lmsys/vicuna-13b-v1.5-16k ）Download the corresponding model weights. But it seems to be inconsistent with the directory structure displayed in the README file. I encountered the following error:

Traceback (most recent call last): File "/public/home/lvshuhang/rocky/YuLian/ModaVerse/demo.py", line 8, in ModaVerse = ModaVerseAPI(model_path=pretrained_model) File "/public/home/lvshuhang/rocky/YuLian/ModaVerse/modaverse/api.py", line 21, in init self.model.load_state_dict(torch.load(osp.join(model_path, File "/public/home/lvshuhang/miniconda3/envs/modaverse/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1671, in load_state_dict raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format( RuntimeError: Error(s) in loading state_dict for ModaVerse: size mismatch for LLM.base_model.model.model.embed_tokens.weight: copying a param with shape torch.Size([32006, 4096]) from checkpoint, the shape in current model is torch.Size([32006, 5120]). size mismatch for LLM.base_model.model.model.layers.0.self_attn.q_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.0.self_attn.q_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.0.self_attn.k_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.0.self_attn.k_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.0.self_attn.v_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.0.self_attn.v_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.0.self_attn.o_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.0.self_attn.o_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.1.self_attn.q_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.1.self_attn.q_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.1.self_attn.k_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.1.self_attn.k_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.1.self_attn.v_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.1.self_attn.v_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.1.self_attn.o_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.1.self_attn.o_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.2.self_attn.q_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.2.self_attn.q_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.2.self_attn.k_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.2.self_attn.k_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.2.self_attn.v_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.2.self_attn.v_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.2.self_attn.o_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.2.self_attn.o_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.3.self_attn.q_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.3.self_attn.q_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.3.self_attn.k_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.3.self_attn.k_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.3.self_attn.v_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.3.self_attn.v_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.3.self_attn.o_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.3.self_attn.o_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.4.self_attn.q_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.4.self_attn.q_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.4.self_attn.k_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.4.self_attn.k_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.4.self_attn.v_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.4.self_attn.v_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.4.self_attn.o_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.4.self_attn.o_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.5.self_attn.q_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.5.self_attn.q_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.5.self_attn.k_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.5.self_attn.k_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.5.self_attn.v_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.5.self_attn.v_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.5.self_attn.o_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.5.self_attn.o_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.6.self_attn.q_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.6.self_attn.q_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.6.self_attn.k_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.6.self_attn.k_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.6.self_attn.v_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.6.self_attn.v_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.6.self_attn.o_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.6.self_attn.o_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.7.self_attn.q_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.7.self_attn.q_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.7.self_attn.k_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.7.self_attn.k_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.7.self_attn.v_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.7.self_attn.v_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.7.self_attn.o_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.7.self_attn.o_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.8.self_attn.q_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.8.self_attn.q_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.8.self_attn.k_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.8.self_attn.k_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.8.self_attn.v_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.8.self_attn.v_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.8.self_attn.o_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.8.self_attn.o_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.9.self_attn.q_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.9.self_attn.q_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.9.self_attn.k_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.9.self_attn.k_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.9.self_attn.v_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.9.self_attn.v_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.9.self_attn.o_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.9.self_attn.o_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.10.self_attn.q_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.10.self_attn.q_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.10.self_attn.k_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.10.self_attn.k_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.10.self_attn.v_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.10.self_attn.v_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.10.self_attn.o_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.10.self_attn.o_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.11.self_attn.q_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.11.self_attn.q_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.11.self_attn.k_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.11.self_attn.k_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.11.self_attn.v_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.11.self_attn.v_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.11.self_attn.o_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.11.self_attn.o_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.12.self_attn.q_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.12.self_attn.q_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.12.self_attn.k_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.12.self_attn.k_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.12.self_attn.v_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.12.self_attn.v_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.12.self_attn.o_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.12.self_attn.o_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.13.self_attn.q_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.13.self_attn.q_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.13.self_attn.k_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.13.self_attn.k_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.13.self_attn.v_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.13.self_attn.v_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.13.self_attn.o_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.13.self_attn.o_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.14.self_attn.q_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.14.self_attn.q_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.14.self_attn.k_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.14.self_attn.k_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.14.self_attn.v_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.14.self_attn.v_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.14.self_attn.o_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.14.self_attn.o_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.15.self_attn.q_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.15.self_attn.q_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.15.self_attn.k_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.15.self_attn.k_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.15.self_attn.v_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.15.self_attn.v_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.15.self_attn.o_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.15.self_attn.o_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.16.self_attn.q_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.16.self_attn.q_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.16.self_attn.k_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.16.self_attn.k_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.16.self_attn.v_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.16.self_attn.v_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.16.self_attn.o_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.16.self_attn.o_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.17.self_attn.q_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.17.self_attn.q_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.17.self_attn.k_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.17.self_attn.k_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.17.self_attn.v_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.17.self_attn.v_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.17.self_attn.o_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.17.self_attn.o_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.18.self_attn.q_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.18.self_attn.q_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.18.self_attn.k_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.18.self_attn.k_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.18.self_attn.v_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.18.self_attn.v_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.18.self_attn.o_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.18.self_attn.o_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.19.self_attn.q_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.19.self_attn.q_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.19.self_attn.k_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.19.self_attn.k_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.19.self_attn.v_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.19.self_attn.v_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.19.self_attn.o_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.19.self_attn.o_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.20.self_attn.q_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.20.self_attn.q_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.20.self_attn.k_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.20.self_attn.k_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.20.self_attn.v_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.20.self_attn.v_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.20.self_attn.o_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.20.self_attn.o_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.21.self_attn.q_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.21.self_attn.q_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.21.self_attn.k_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.21.self_attn.k_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.21.self_attn.v_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.21.self_attn.v_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.21.self_attn.o_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.21.self_attn.o_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.22.self_attn.q_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.22.self_attn.q_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.22.self_attn.k_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.22.self_attn.k_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.22.self_attn.v_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.22.self_attn.v_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.22.self_attn.o_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.22.self_attn.o_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.23.self_attn.q_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.23.self_attn.q_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.23.self_attn.k_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.23.self_attn.k_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.23.self_attn.v_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.23.self_attn.v_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.23.self_attn.o_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.23.self_attn.o_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.24.self_attn.q_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.24.self_attn.q_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.24.self_attn.k_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.24.self_attn.k_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.24.self_attn.v_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.24.self_attn.v_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.24.self_attn.o_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.24.self_attn.o_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.25.self_attn.q_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.25.self_attn.q_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.25.self_attn.k_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.25.self_attn.k_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.25.self_attn.v_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.25.self_attn.v_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.25.self_attn.o_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.25.self_attn.o_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.26.self_attn.q_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.26.self_attn.q_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.26.self_attn.k_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.26.self_attn.k_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.26.self_attn.v_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.26.self_attn.v_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.26.self_attn.o_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.26.self_attn.o_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.27.self_attn.q_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.27.self_attn.q_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.27.self_attn.k_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.27.self_attn.k_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.27.self_attn.v_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.27.self_attn.v_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.27.self_attn.o_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.27.self_attn.o_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.28.self_attn.q_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.28.self_attn.q_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.28.self_attn.k_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.28.self_attn.k_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.28.self_attn.v_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.28.self_attn.v_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.28.self_attn.o_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.28.self_attn.o_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.29.self_attn.q_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.29.self_attn.q_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.29.self_attn.k_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.29.self_attn.k_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.29.self_attn.v_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.29.self_attn.v_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.29.self_attn.o_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.29.self_attn.o_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.30.self_attn.q_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.30.self_attn.q_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.30.self_attn.k_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.30.self_attn.k_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.30.self_attn.v_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.30.self_attn.v_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.30.self_attn.o_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.30.self_attn.o_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.31.self_attn.q_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.31.self_attn.q_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.31.self_attn.k_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.31.self_attn.k_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.31.self_attn.v_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.31.self_attn.v_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.model.layers.31.self_attn.o_proj.lora_A.default.weight: copying a param with shape torch.Size([32, 4096]) from checkpoint, the shape in current model is torch.Size([32, 5120]). size mismatch for LLM.base_model.model.model.layers.31.self_attn.o_proj.lora_B.default.weight: copying a param with shape torch.Size([4096, 32]) from checkpoint, the shape in current model is torch.Size([5120, 32]). size mismatch for LLM.base_model.model.lm_head.weight: copying a param with shape torch.Size([32006, 4096]) from checkpoint, the shape in current model is torch.Size([32006, 5120]). size mismatch for linear_proj.weight: copying a param with shape torch.Size([4096, 1024]) from checkpoint, the shape in current model is torch.Size([5120, 1024]). size mismatch for linear_proj.bias: copying a param with shape torch.Size([4096]) from checkpoint, the shape in current model is torch.Size([5120]).

[xinke-wang]

It seems you are using Vicuna-v1.5-13B. However, our version was trained using Vicuna-v0-7B as the foundational model. You need to follow the instructions here to download the 7B-delta model and apply it to Llama to obtain the Vicuna-7B model.

[Ohzyang]

NameError: name 'ModaVerse' is not defined when I test something in the gradio (modaverse) root@autodl-container-433d4a8b09-28f47cc9:~/autodl-tmp/ModaVerse# python demo.py Traceback (most recent call last): File "/root/autodl-tmp/ModaVerse/demo.py", line 6, in from modaverse import ModaVerse ImportError: cannot import name 'ModaVerse' from 'modaverse' (/root/autodl-tmp/ModaVerse/modaverse/init.py) (modaverse) root@autodl-container-433d4a8b09-28f47cc9:~/autodl-tmp/ModaVerse# python demo.py /root/miniconda3/envs/modaverse/lib/python3.9/site-packages/transformers/utils/hub.py:124: FutureWarning: Using TRANSFORMERS_CACHE is deprecated and will be removed in v5 of Transformers. Use HF_HOME instead. warnings.warn( Setting ds_accelerator to cuda (auto detect) /root/miniconda3/envs/modaverse/lib/python3.9/site-packages/torchvision/transforms/_functional_video.py:6: UserWarning: The 'torchvision.transforms._functional_video' module is deprecated since 0.12 and will be removed in the future. Please use the 'torchvision.transforms.functional' module instead. warnings.warn( /root/miniconda3/envs/modaverse/lib/python3.9/site-packages/torchvision/transforms/_transforms_video.py:22: UserWarning: The 'torchvision.transforms._transforms_video' module is deprecated since 0.12 and will be removed in the future. Please use the 'torchvision.transforms' module instead. warnings.warn( Using pretrained model path: /root/autodl-tmp/ModaVerse/Model/ModaVerse-7b Initializing ModaVerseAPI with model_path: /root/autodl-tmp/ModaVerse/Model/ModaVerse-7b Configs being passed to ModaVerse: <pjtools.configurator.configurator.PyConfigurator object at 0x7ff7c95b3a00> Type of llm_path: <class 'str'>, value: /root/autodl-tmp/ModaVerse/Model/7b_v0 Type of sp_model_path: <class 'str'>, value: /root/autodl-tmp/ModaVerse/Model/7b_v0/tokenizer.model Contents of the model path (/root/autodl-tmp/ModaVerse/Model/7b_v0): ['tokenizer_config.json', 'special_tokens_map.json', 'tokenizer.model', 'tokenizer.json', 'config.json', 'generation_config.json', 'model-00001-of-00003.safetensors', 'model-00002-of-00003.safetensors', 'model-00003-of-00003.safetensors', 'model.safetensors.index.json'] Loading LlamaTokenizer from path: /root/autodl-tmp/ModaVerse/Model/7b_v0 and SentencePiece model from path: /root/autodl-tmp/ModaVerse/Model/7b_v0/tokenizer.model LlamaTokenizer loaded successfully. Loading checkpoint shards: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:03<00:00, 1.02s/it] Forcing training on layers: base_model.model.model.embed_tokens.weight base_model.model.lm_head.weight trainable params: 295,747,584 || all params: 6,772,019,200 || trainable%: 4.3672 Loading pipeline components...: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:01<00:00, 6.19it/s] Loading pipeline components...: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 8.82it/s] It seems like you have activated model offloading by calling enable_model_cpu_offload, but are now manually moving the pipeline to GPU. It is strongly recommended against doing so as memory gains from offloading are likely to be lost. Offloading automatically takes care of moving the individual components vae, text_encoder, tokenizer, unet, scheduler to GPU when needed. To make sure offloading works as expected, you should consider moving the pipeline back to CPU: pipeline.to('cpu') or removing the move altogether if you use offloading. unet/diffusion_pytorch_model.safetensors not found Loading pipeline components...: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████| 6/6 [00:01<00:00, 3.97it/s] ModaVerseAPI initialized successfully. Launching ModaVerse Demo... Running on local URL: http://127.0.0.1:7860 IMPORTANT: You are using gradio version 3.50.2, however version 4.29.0 is available, please upgrade.

Running on public URL: https://bb1cb0dabf89a83aa3.gradio.live

This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run gradio deploy from Terminal to deploy to Spaces (https://huggingface.co/spaces) gradio deploy Traceback (most recent call last): File "/root/miniconda3/envs/modaverse/lib/python3.9/site-packages/gradio/routes.py", line 534, in predict output = await route_utils.call_process_api( File "/root/miniconda3/envs/modaverse/lib/python3.9/site-packages/gradio/route_utils.py", line 226, in call_process_api output = await app.get_blocks().process_api( File "/root/miniconda3/envs/modaverse/lib/python3.9/site-packages/gradio/blocks.py", line 1550, in process_api result = await self.call_function( File "/root/miniconda3/envs/modaverse/lib/python3.9/site-packages/gradio/blocks.py", line 1185, in call_function prediction = await anyio.to_thread.run_sync( File "/root/miniconda3/envs/modaverse/lib/python3.9/site-packages/anyio/to_thread.py", line 56, in run_sync return await get_async_backend().run_sync_in_worker_thread( File "/root/miniconda3/envs/modaverse/lib/python3.9/site-packages/anyio/_backends/_asyncio.py", line 2177, in run_sync_in_worker_thread return await future File "/root/miniconda3/envs/modaverse/lib/python3.9/site-packages/anyio/_backends/_asyncio.py", line 859, in run result = context.run(func, args) File "/root/miniconda3/envs/modaverse/lib/python3.9/site-packages/gradio/utils.py", line 661, in wrapper response = f(args, **kwargs) File "/root/autodl-tmp/ModaVerse/demo.py", line 43, in process_input meta_response, final_responses = ModaVerse(instruction, media) NameError: name 'ModaVerse' is not defined

[xinke-wang]

NameError: name 'ModaVerse' is not defined when I test something in the gradio

(modaverse) root@autodl-container-433d4a8b09-28f47cc9:~/autodl-tmp/ModaVerse# python demo.py Traceback (most recent call last): File "/root/autodl-tmp/ModaVerse/demo.py", line 6, in from modaverse import ModaVerse ImportError: cannot import name 'ModaVerse' from 'modaverse' (/root/autodl-tmp/ModaVerse/modaverse/init.py) (modaverse) root@autodl-container-433d4a8b09-28f47cc9:~/autodl-tmp/ModaVerse# python demo.py /root/miniconda3/envs/modaverse/lib/python3.9/site-packages/transformers/utils/hub.py:124: FutureWarning: Using TRANSFORMERS_CACHE is deprecated and will be removed in v5 of Transformers. Use HF_HOME instead. warnings.warn( Setting ds_accelerator to cuda (auto detect) /root/miniconda3/envs/modaverse/lib/python3.9/site-packages/torchvision/transforms/_functional_video.py:6: UserWarning: The 'torchvision.transforms._functional_video' module is deprecated since 0.12 and will be removed in the future. Please use the 'torchvision.transforms.functional' module instead. warnings.warn( /root/miniconda3/envs/modaverse/lib/python3.9/site-packages/torchvision/transforms/_transforms_video.py:22: UserWarning: The 'torchvision.transforms._transforms_video' module is deprecated since 0.12 and will be removed in the future. Please use the 'torchvision.transforms' module instead. warnings.warn( Using pretrained model path: /root/autodl-tmp/ModaVerse/Model/ModaVerse-7b Initializing ModaVerseAPI with model_path: /root/autodl-tmp/ModaVerse/Model/ModaVerse-7b Configs being passed to ModaVerse: <pjtools.configurator.configurator.PyConfigurator object at 0x7ff7c95b3a00> Type of llm_path: <class 'str'>, value: /root/autodl-tmp/ModaVerse/Model/7b_v0 Type of sp_model_path: <class 'str'>, value: /root/autodl-tmp/ModaVerse/Model/7b_v0/tokenizer.model Contents of the model path (/root/autodl-tmp/ModaVerse/Model/7b_v0): ['tokenizer_config.json', 'special_tokens_map.json', 'tokenizer.model', 'tokenizer.json', 'config.json', 'generation_config.json', 'model-00001-of-00003.safetensors', 'model-00002-of-00003.safetensors', 'model-00003-of-00003.safetensors', 'model.safetensors.index.json'] Loading LlamaTokenizer from path: /root/autodl-tmp/ModaVerse/Model/7b_v0 and SentencePiece model from path: /root/autodl-tmp/ModaVerse/Model/7b_v0/tokenizer.model LlamaTokenizer loaded successfully. Loading checkpoint shards: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:03<00:00, 1.02s/it] Forcing training on layers: base_model.model.model.embed_tokens.weight base_model.model.lm_head.weight trainable params: 295,747,584 || all params: 6,772,019,200 || trainable%: 4.3672 Loading pipeline components...: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:01<00:00, 6.19it/s] Loading pipeline components...: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 8.82it/s] It seems like you have activated model offloading by calling enable_model_cpu_offload, but are now manually moving the pipeline to GPU. It is strongly recommended against doing so as memory gains from offloading are likely to be lost. Offloading automatically takes care of moving the individual components vae, text_encoder, tokenizer, unet, scheduler to GPU when needed. To make sure offloading works as expected, you should consider moving the pipeline back to CPU: pipeline.to('cpu') or removing the move altogether if you use offloading. unet/diffusion_pytorch_model.safetensors not found Loading pipeline components...: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████| 6/6 [00:01<00:00, 3.97it/s] ModaVerseAPI initialized successfully. Launching ModaVerse Demo... Running on local URL: http://127.0.0.1:7860 IMPORTANT: You are using gradio version 3.50.2, however version 4.29.0 is available, please upgrade. Running on public URL: https://bb1cb0dabf89a83aa3.gradio.live

This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run gradio deploy from Terminal to deploy to Spaces (https://huggingface.co/spaces) gradio deploy Traceback (most recent call last): File "/root/miniconda3/envs/modaverse/lib/python3.9/site-packages/gradio/routes.py", line 534, in predict output = await route_utils.call_process_api( File "/root/miniconda3/envs/modaverse/lib/python3.9/site-packages/gradio/route_utils.py", line 226, in call_process_api output = await app.get_blocks().process_api( File "/root/miniconda3/envs/modaverse/lib/python3.9/site-packages/gradio/blocks.py", line 1550, in process_api result = await self.call_function( File "/root/miniconda3/envs/modaverse/lib/python3.9/site-packages/gradio/blocks.py", line 1185, in call_function prediction = await anyio.to_thread.run_sync( File "/root/miniconda3/envs/modaverse/lib/python3.9/site-packages/anyio/to_thread.py", line 56, in run_sync return await get_async_backend().run_sync_in_worker_thread( File "/root/miniconda3/envs/modaverse/lib/python3.9/site-packages/anyio/_backends/_asyncio.py", line 2177, in run_sync_in_worker_thread return await future File "/root/miniconda3/envs/modaverse/lib/python3.9/site-packages/anyio/_backends/_asyncio.py", line 859, in run result = context.run(func, args) File "/root/miniconda3/envs/modaverse/lib/python3.9/site-packages/gradio/utils.py", line 661, in wrapper response = f(args, **kwargs) File "/root/autodl-tmp/ModaVerse/demo.py", line 43, in process_input meta_response, final_responses = ModaVerse(instruction, media) NameError: name 'ModaVerse' is not defined

Did you follow the installation steps to run pip install -e .?

[Ohzyang]

is this?I absoluately run this

------------------ 原始邮件 ------------------ 发件人: "xinke-wang/ModaVerse" @.>; 发送时间: 2024年7月8日(星期一) 下午4:31 @.>; @.**@.>; 主题: Re: [xinke-wang/ModaVerse] I can't find 7b_v0 weight (Issue #2)

NameError: name 'ModaVerse' is not defined when I test something in the gradio

(modaverse) @.:/autodl-tmp/ModaVerse# python demo.py Traceback (most recent call last): File "/root/autodl-tmp/ModaVerse/demo.py", line 6, in from modaverse import ModaVerse ImportError: cannot import name 'ModaVerse' from 'modaverse' (/root/autodl-tmp/ModaVerse/modaverse/init.py) (modaverse) @.:/autodl-tmp/ModaVerse# python demo.py /root/miniconda3/envs/modaverse/lib/python3.9/site-packages/transformers/utils/hub.py:124: FutureWarning: Using TRANSFORMERS_CACHE is deprecated and will be removed in v5 of Transformers. Use HF_HOME instead. warnings.warn( Setting ds_accelerator to cuda (auto detect) /root/miniconda3/envs/modaverse/lib/python3.9/site-packages/torchvision/transforms/_functional_video.py:6: UserWarning: The 'torchvision.transforms._functional_video' module is deprecated since 0.12 and will be removed in the future. Please use the 'torchvision.transforms.functional' module instead. warnings.warn( /root/miniconda3/envs/modaverse/lib/python3.9/site-packages/torchvision/transforms/_transforms_video.py:22: UserWarning: The 'torchvision.transforms._transforms_video' module is deprecated since 0.12 and will be removed in the future. Please use the 'torchvision.transforms' module instead. warnings.warn( Using pretrained model path: /root/autodl-tmp/ModaVerse/Model/ModaVerse-7b Initializing ModaVerseAPI with model_path: /root/autodl-tmp/ModaVerse/Model/ModaVerse-7b Configs being passed to ModaVerse: <pjtools.configurator.configurator.PyConfigurator object at 0x7ff7c95b3a00> Type of llm_path: <class 'str'>, value: /root/autodl-tmp/ModaVerse/Model/7b_v0 Type of sp_model_path: <class 'str'>, value: /root/autodl-tmp/ModaVerse/Model/7b_v0/tokenizer.model Contents of the model path (/root/autodl-tmp/ModaVerse/Model/7b_v0): ['tokenizer_config.json', 'special_tokens_map.json', 'tokenizer.model', 'tokenizer.json', 'config.json', 'generation_config.json', 'model-00001-of-00003.safetensors', 'model-00002-of-00003.safetensors', 'model-00003-of-00003.safetensors', 'model.safetensors.index.json'] Loading LlamaTokenizer from path: /root/autodl-tmp/ModaVerse/Model/7b_v0 and SentencePiece model from path: /root/autodl-tmp/ModaVerse/Model/7b_v0/tokenizer.model LlamaTokenizer loaded successfully. Loading checkpoint shards: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:03<00:00, 1.02s/it] Forcing training on layers: base_model.model.model.embed_tokens.weight base_model.model.lm_head.weight trainable params: 295,747,584 || all params: 6,772,019,200 || trainable%: 4.3672 Loading pipeline components...: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:01<00:00, 6.19it/s] Loading pipeline components...: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 8.82it/s] It seems like you have activated model offloading by calling enable_model_cpu_offload, but are now manually moving the pipeline to GPU. It is strongly recommended against doing so as memory gains from offloading are likely to be lost. Offloading automatically takes care of moving the individual components vae, text_encoder, tokenizer, unet, scheduler to GPU when needed. To make sure offloading works as expected, you should consider moving the pipeline back to CPU: pipeline.to('cpu') or removing the move altogether if you use offloading. unet/diffusion_pytorch_model.safetensors not found Loading pipeline components...: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████| 6/6 [00:01<00:00, 3.97it/s] ModaVerseAPI initialized successfully. Launching ModaVerse Demo... Running on local URL: http://127.0.0.1:7860 IMPORTANT: You are using gradio version 3.50.2, however version 4.29.0 is available, please upgrade. Running on public URL: https://bb1cb0dabf89a83aa3.gradio.live

This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run gradio deploy from Terminal to deploy to Spaces (https://huggingface.co/spaces) gradio deploy Traceback (most recent call last): File "/root/miniconda3/envs/modaverse/lib/python3.9/site-packages/gradio/routes.py", line 534, in predict output = await route_utils.call_process_api( File "/root/miniconda3/envs/modaverse/lib/python3.9/site-packages/gradio/route_utils.py", line 226, in call_process_api output = await app.get_blocks().process_api( File "/root/miniconda3/envs/modaverse/lib/python3.9/site-packages/gradio/blocks.py", line 1550, in process_api result = await self.call_function( File "/root/miniconda3/envs/modaverse/lib/python3.9/site-packages/gradio/blocks.py", line 1185, in call_function prediction = await anyio.to_thread.run_sync( File "/root/miniconda3/envs/modaverse/lib/python3.9/site-packages/anyio/to_thread.py", line 56, in run_sync return await get_async_backend().run_sync_in_worker_thread( File "/root/miniconda3/envs/modaverse/lib/python3.9/site-packages/anyio/_backends/_asyncio.py", line 2177, in run_sync_in_worker_thread return await future File "/root/miniconda3/envs/modaverse/lib/python3.9/site-packages/anyio/_backends/_asyncio.py", line 859, in run result = context.run(func, args) File "/root/miniconda3/envs/modaverse/lib/python3.9/site-packages/gradio/utils.py", line 661, in wrapper response = f(args, **kwargs) File "/root/autodl-tmp/ModaVerse/demo.py", line 43, in process_input meta_response, final_responses = ModaVerse(instruction, media) NameError: name 'ModaVerse' is not defined

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[Ohzyang]

another question please: 

this code is for vicuna-7b-delta-v1.1, then which code is for vicuna-7b-delta-v0? and which llama should I install,llama-7b or llama-2-7b? please answer my question,I will appreciate you very much! ------------------ 原始邮件 ------------------ 发件人: "xinke-wang/ModaVerse" @.>; 发送时间: 2024年7月8日(星期一) 下午4:31 @.>; @.**@.>; 主题: Re: [xinke-wang/ModaVerse] I can't find 7b_v0 weight (Issue #2)

NameError: name 'ModaVerse' is not defined when I test something in the gradio

(modaverse) @.:/autodl-tmp/ModaVerse# python demo.py Traceback (most recent call last): File "/root/autodl-tmp/ModaVerse/demo.py", line 6, in from modaverse import ModaVerse ImportError: cannot import name 'ModaVerse' from 'modaverse' (/root/autodl-tmp/ModaVerse/modaverse/init.py) (modaverse) @.:/autodl-tmp/ModaVerse# python demo.py /root/miniconda3/envs/modaverse/lib/python3.9/site-packages/transformers/utils/hub.py:124: FutureWarning: Using TRANSFORMERS_CACHE is deprecated and will be removed in v5 of Transformers. Use HF_HOME instead. warnings.warn( Setting ds_accelerator to cuda (auto detect) /root/miniconda3/envs/modaverse/lib/python3.9/site-packages/torchvision/transforms/_functional_video.py:6: UserWarning: The 'torchvision.transforms._functional_video' module is deprecated since 0.12 and will be removed in the future. Please use the 'torchvision.transforms.functional' module instead. warnings.warn( /root/miniconda3/envs/modaverse/lib/python3.9/site-packages/torchvision/transforms/_transforms_video.py:22: UserWarning: The 'torchvision.transforms._transforms_video' module is deprecated since 0.12 and will be removed in the future. Please use the 'torchvision.transforms' module instead. warnings.warn( Using pretrained model path: /root/autodl-tmp/ModaVerse/Model/ModaVerse-7b Initializing ModaVerseAPI with model_path: /root/autodl-tmp/ModaVerse/Model/ModaVerse-7b Configs being passed to ModaVerse: <pjtools.configurator.configurator.PyConfigurator object at 0x7ff7c95b3a00> Type of llm_path: <class 'str'>, value: /root/autodl-tmp/ModaVerse/Model/7b_v0 Type of sp_model_path: <class 'str'>, value: /root/autodl-tmp/ModaVerse/Model/7b_v0/tokenizer.model Contents of the model path (/root/autodl-tmp/ModaVerse/Model/7b_v0): ['tokenizer_config.json', 'special_tokens_map.json', 'tokenizer.model', 'tokenizer.json', 'config.json', 'generation_config.json', 'model-00001-of-00003.safetensors', 'model-00002-of-00003.safetensors', 'model-00003-of-00003.safetensors', 'model.safetensors.index.json'] Loading LlamaTokenizer from path: /root/autodl-tmp/ModaVerse/Model/7b_v0 and SentencePiece model from path: /root/autodl-tmp/ModaVerse/Model/7b_v0/tokenizer.model LlamaTokenizer loaded successfully. Loading checkpoint shards: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:03<00:00, 1.02s/it] Forcing training on layers: base_model.model.model.embed_tokens.weight base_model.model.lm_head.weight trainable params: 295,747,584 || all params: 6,772,019,200 || trainable%: 4.3672 Loading pipeline components...: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:01<00:00, 6.19it/s] Loading pipeline components...: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 8.82it/s] It seems like you have activated model offloading by calling enable_model_cpu_offload, but are now manually moving the pipeline to GPU. It is strongly recommended against doing so as memory gains from offloading are likely to be lost. Offloading automatically takes care of moving the individual components vae, text_encoder, tokenizer, unet, scheduler to GPU when needed. To make sure offloading works as expected, you should consider moving the pipeline back to CPU: pipeline.to('cpu') or removing the move altogether if you use offloading. unet/diffusion_pytorch_model.safetensors not found Loading pipeline components...: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████| 6/6 [00:01<00:00, 3.97it/s] ModaVerseAPI initialized successfully. Launching ModaVerse Demo... Running on local URL: http://127.0.0.1:7860 IMPORTANT: You are using gradio version 3.50.2, however version 4.29.0 is available, please upgrade. Running on public URL: https://bb1cb0dabf89a83aa3.gradio.live

This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run gradio deploy from Terminal to deploy to Spaces (https://huggingface.co/spaces) gradio deploy Traceback (most recent call last): File "/root/miniconda3/envs/modaverse/lib/python3.9/site-packages/gradio/routes.py", line 534, in predict output = await route_utils.call_process_api( File "/root/miniconda3/envs/modaverse/lib/python3.9/site-packages/gradio/route_utils.py", line 226, in call_process_api output = await app.get_blocks().process_api( File "/root/miniconda3/envs/modaverse/lib/python3.9/site-packages/gradio/blocks.py", line 1550, in process_api result = await self.call_function( File "/root/miniconda3/envs/modaverse/lib/python3.9/site-packages/gradio/blocks.py", line 1185, in call_function prediction = await anyio.to_thread.run_sync( File "/root/miniconda3/envs/modaverse/lib/python3.9/site-packages/anyio/to_thread.py", line 56, in run_sync return await get_async_backend().run_sync_in_worker_thread( File "/root/miniconda3/envs/modaverse/lib/python3.9/site-packages/anyio/_backends/_asyncio.py", line 2177, in run_sync_in_worker_thread return await future File "/root/miniconda3/envs/modaverse/lib/python3.9/site-packages/anyio/_backends/_asyncio.py", line 859, in run result = context.run(func, args) File "/root/miniconda3/envs/modaverse/lib/python3.9/site-packages/gradio/utils.py", line 661, in wrapper response = f(args, **kwargs) File "/root/autodl-tmp/ModaVerse/demo.py", line 43, in process_input meta_response, final_responses = ModaVerse(instruction, media) NameError: name 'ModaVerse' is not defined

Did you follow the installation steps to run pip install -e .?

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