warnings.warn(
C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\torch\nn\modules\module.py:2441: UserWarning: for vision_model.encoder.layers.19.self_attn.q_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
warnings.warn(
C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\torch\nn\modules\module.py:2441: UserWarning: for vision_model.encoder.layers.19.self_attn.q_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
warnings.warn(
C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\torch\nn\modules\module.py:2441: UserWarning: for vision_model.encoder.layers.19.self_attn.out_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
warnings.warn(
C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\torch\nn\modules\module.py:2441: UserWarning: for vision_model.encoder.layers.19.self_attn.out_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
warnings.warn(
C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\torch\nn\modules\module.py:2441: UserWarning: for vision_model.encoder.layers.19.layer_norm1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
warnings.warn(
C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\torch\nn\modules\module.py:2441: UserWarning: for vision_model.encoder.layers.19.layer_norm1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
warnings.warn(
C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\torch\nn\modules\module.py:2441: UserWarning: for vision_model.encoder.layers.19.mlp.fc1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
warnings.warn(
C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\torch\nn\modules\module.py:2441: UserWarning: for vision_model.encoder.layers.19.mlp.fc1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
warnings.warn(
C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\torch\nn\modules\module.py:2441: UserWarning: for vision_model.encoder.layers.19.mlp.fc2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
warnings.warn(
C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\torch\nn\modules\module.py:2441: UserWarning: for vision_model.encoder.layers.19.mlp.fc2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
warnings.warn(
C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\torch\nn\modules\module.py:2441: UserWarning: for vision_model.encoder.layers.19.layer_norm2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
warnings.warn(
C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\torch\nn\modules\module.py:2441: UserWarning: for vision_model.encoder.layers.19.layer_norm2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
warnings.warn(
C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\torch\nn\modules\module.py:2441: UserWarning: for vision_model.encoder.layers.20.self_attn.k_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
warnings.warn(
C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\torch\nn\modules\module.py:2441: UserWarning: for vision_model.encoder.layers.20.self_attn.k_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
warnings.warn(
C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\torch\nn\modules\module.py:2441: UserWarning: for vision_model.encoder.layers.20.self_attn.v_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
warnings.warn(
C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\torch\nn\modules\module.py:2441: UserWarning: for vision_model.encoder.layers.20.self_attn.v_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
warnings.warn(
C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\torch\nn\modules\module.py:2441: UserWarning: for vision_model.encoder.layers.20.self_attn.q_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
warnings.warn(
C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\torch\nn\modules\module.py:2441: UserWarning: for vision_model.encoder.layers.20.self_attn.q_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
warnings.warn(
C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\torch\nn\modules\module.py:2441: UserWarning: for vision_model.encoder.layers.20.self_attn.out_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
warnings.warn(
C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\torch\nn\modules\module.py:2441: UserWarning: for vision_model.encoder.layers.20.self_attn.out_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
warnings.warn(
C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\torch\nn\modules\module.py:2441: UserWarning: for vision_model.encoder.layers.20.layer_norm1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
warnings.warn(
C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\torch\nn\modules\module.py:2441: UserWarning: for vision_model.encoder.layers.20.layer_norm1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
warnings.warn(
C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\torch\nn\modules\module.py:2441: UserWarning: for vision_model.encoder.layers.20.mlp.fc1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
warnings.warn(
C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\torch\nn\modules\module.py:2441: UserWarning: for vision_model.encoder.layers.20.mlp.fc1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
warnings.warn(
C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\torch\nn\modules\module.py:2441: UserWarning: for vision_model.encoder.layers.20.mlp.fc2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
warnings.warn(
C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\torch\nn\modules\module.py:2441: UserWarning: for vision_model.encoder.layers.20.mlp.fc2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
warnings.warn(
C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\torch\nn\modules\module.py:2441: UserWarning: for vision_model.encoder.layers.20.layer_norm2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
warnings.warn(
C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\torch\nn\modules\module.py:2441: UserWarning: for vision_model.encoder.layers.20.layer_norm2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
warnings.warn(
C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\torch\nn\modules\module.py:2441: UserWarning: for vision_model.encoder.layers.21.self_attn.k_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
warnings.warn(
C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\torch\nn\modules\module.py:2441: UserWarning: for vision_model.encoder.layers.21.self_attn.k_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
warnings.warn(
C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\torch\nn\modules\module.py:2441: UserWarning: for vision_model.encoder.layers.21.self_attn.v_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
warnings.warn(
C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\torch\nn\modules\module.py:2441: UserWarning: for vision_model.encoder.layers.21.self_attn.v_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
warnings.warn(
C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\torch\nn\modules\module.py:2441: UserWarning: for vision_model.encoder.layers.21.self_attn.q_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
warnings.warn(
C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\torch\nn\modules\module.py:2441: UserWarning: for vision_model.encoder.layers.21.self_attn.q_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
warnings.warn(
C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\torch\nn\modules\module.py:2441: UserWarning: for vision_model.encoder.layers.21.self_attn.out_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
warnings.warn(
C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\torch\nn\modules\module.py:2441: UserWarning: for vision_model.encoder.layers.21.self_attn.out_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
warnings.warn(
C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\torch\nn\modules\module.py:2441: UserWarning: for vision_model.encoder.layers.21.layer_norm1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
warnings.warn(
C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\torch\nn\modules\module.py:2441: UserWarning: for vision_model.encoder.layers.21.layer_norm1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
warnings.warn(
C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\torch\nn\modules\module.py:2441: UserWarning: for vision_model.encoder.layers.21.mlp.fc1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
warnings.warn(
C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\torch\nn\modules\module.py:2441: UserWarning: for vision_model.encoder.layers.21.mlp.fc1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
warnings.warn(
C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\torch\nn\modules\module.py:2441: UserWarning: for vision_model.encoder.layers.21.mlp.fc2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
warnings.warn(
C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\torch\nn\modules\module.py:2441: UserWarning: for vision_model.encoder.layers.21.mlp.fc2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
warnings.warn(
C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\torch\nn\modules\module.py:2441: UserWarning: for vision_model.encoder.layers.21.layer_norm2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
warnings.warn(
C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\torch\nn\modules\module.py:2441: UserWarning: for vision_model.encoder.layers.21.layer_norm2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
warnings.warn(
C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\torch\nn\modules\module.py:2441: UserWarning: for vision_model.encoder.layers.22.self_attn.k_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
warnings.warn(
C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\torch\nn\modules\module.py:2441: UserWarning: for vision_model.encoder.layers.22.self_attn.k_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
warnings.warn(
C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\torch\nn\modules\module.py:2441: UserWarning: for vision_model.encoder.layers.22.self_attn.v_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
warnings.warn(
C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\torch\nn\modules\module.py:2441: UserWarning: for vision_model.encoder.layers.22.self_attn.v_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
warnings.warn(
C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\torch\nn\modules\module.py:2441: UserWarning: for vision_model.encoder.layers.22.self_attn.q_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
warnings.warn(
C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\torch\nn\modules\module.py:2441: UserWarning: for vision_model.encoder.layers.22.self_attn.q_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
warnings.warn(
C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\torch\nn\modules\module.py:2441: UserWarning: for vision_model.encoder.layers.22.self_attn.out_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
warnings.warn(
C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\torch\nn\modules\module.py:2441: UserWarning: for vision_model.encoder.layers.22.self_attn.out_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
warnings.warn(
C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\torch\nn\modules\module.py:2441: UserWarning: for vision_model.encoder.layers.22.layer_norm1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
warnings.warn(
C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\torch\nn\modules\module.py:2441: UserWarning: for vision_model.encoder.layers.22.layer_norm1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
warnings.warn(
C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\torch\nn\modules\module.py:2441: UserWarning: for vision_model.encoder.layers.22.mlp.fc1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
warnings.warn(
C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\torch\nn\modules\module.py:2441: UserWarning: for vision_model.encoder.layers.22.mlp.fc1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
warnings.warn(
C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\torch\nn\modules\module.py:2441: UserWarning: for vision_model.encoder.layers.22.mlp.fc2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
warnings.warn(
C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\torch\nn\modules\module.py:2441: UserWarning: for vision_model.encoder.layers.22.mlp.fc2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
warnings.warn(
C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\torch\nn\modules\module.py:2441: UserWarning: for vision_model.encoder.layers.22.layer_norm2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
warnings.warn(
C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\torch\nn\modules\module.py:2441: UserWarning: for vision_model.encoder.layers.22.layer_norm2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
warnings.warn(
C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\torch\nn\modules\module.py:2441: UserWarning: for vision_model.encoder.layers.23.self_attn.k_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
warnings.warn(
C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\torch\nn\modules\module.py:2441: UserWarning: for vision_model.encoder.layers.23.self_attn.k_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
warnings.warn(
C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\torch\nn\modules\module.py:2441: UserWarning: for vision_model.encoder.layers.23.self_attn.v_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
warnings.warn(
C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\torch\nn\modules\module.py:2441: UserWarning: for vision_model.encoder.layers.23.self_attn.v_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
warnings.warn(
C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\torch\nn\modules\module.py:2441: UserWarning: for vision_model.encoder.layers.23.self_attn.q_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
warnings.warn(
C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\torch\nn\modules\module.py:2441: UserWarning: for vision_model.encoder.layers.23.self_attn.q_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
warnings.warn(
C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\torch\nn\modules\module.py:2441: UserWarning: for vision_model.encoder.layers.23.self_attn.out_proj.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
warnings.warn(
C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\torch\nn\modules\module.py:2441: UserWarning: for vision_model.encoder.layers.23.self_attn.out_proj.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
warnings.warn(
C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\torch\nn\modules\module.py:2441: UserWarning: for vision_model.encoder.layers.23.layer_norm1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
warnings.warn(
C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\torch\nn\modules\module.py:2441: UserWarning: for vision_model.encoder.layers.23.layer_norm1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
warnings.warn(
C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\torch\nn\modules\module.py:2441: UserWarning: for vision_model.encoder.layers.23.mlp.fc1.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
warnings.warn(
C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\torch\nn\modules\module.py:2441: UserWarning: for vision_model.encoder.layers.23.mlp.fc1.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
warnings.warn(
C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\torch\nn\modules\module.py:2441: UserWarning: for vision_model.encoder.layers.23.mlp.fc2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
warnings.warn(
C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\torch\nn\modules\module.py:2441: UserWarning: for vision_model.encoder.layers.23.mlp.fc2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
warnings.warn(
C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\torch\nn\modules\module.py:2441: UserWarning: for vision_model.encoder.layers.23.layer_norm2.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
warnings.warn(
C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\torch\nn\modules\module.py:2441: UserWarning: for vision_model.encoder.layers.23.layer_norm2.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
warnings.warn(
C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\torch\nn\modules\module.py:2441: UserWarning: for vision_model.post_layernorm.weight: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
warnings.warn(
C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\torch\nn\modules\module.py:2441: UserWarning: for vision_model.post_layernorm.bias: copying from a non-meta parameter in the checkpoint to a meta parameter in the current model, which is a no-op. (Did you mean to pass `assign=True` to assign items in the state dictionary to their corresponding key in the module instead of copying them in place?)
warnings.warn(
Traceback (most recent call last):
File "C:\Users\25461\Desktop\多模态\1\GeoChat-main\geochat_demo.py", line 53, in <module>
tokenizer, model, image_processor, context_len = load_pretrained_model(args.model_path, args.model_base, model_name, args.load_8bit, args.load_4bit, device=args.device,)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\25461\Desktop\多模态\1\GeoChat-main\geochat\model\builder.py", line 125, in load_pretrained_model
model = AutoModelForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\transformers\models\auto\auto_factory.py", line 493, in from_pretrained
return model_class.from_pretrained(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\transformers\modeling_utils.py", line 2903, in from_pretrained
) = cls._load_pretrained_model(
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\25461\AppData\Roaming\Python\Python311\site-packages\transformers\modeling_utils.py", line 3002, in _load_pretrained_model
raise ValueError(
ValueError: The current `device_map` had weights offloaded to the disk. Please provide an `offload_folder` for them. Alternatively, make sure you have `safetensors` installed if the model you are using offers the weights in this format.