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create a temporary directory: /tmp/tmpc0jeswnf
loading /media/hangyu5/Home/Documents/Hugging-Face/LM_cocktail/Mistral-7B-Instruct-v0.2 -----------------
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loading /media/hangyu5/Home/Documents/Hugging-Face/LM_cocktail/xDAN-L1-Chat-RL-v1 -----------------
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  Merging models: 94%|█████████▍| 274/291 [01:16<00:04, 3.57it/s]
 Processing model.layers.16.self_attn.v_proj.weight: 0%| | 0/2 [00:00<?, ?it/s]
 Processing model.layers.16.self_attn.v_proj.weight: 100%|██████████| 2/2 [00:00<00:00, 10.54it/s]
  Merging models: 95%|█████████▍| 275/291 [01:16<00:04, 3.95it/s]
 Processing model.layers.15.input_layernorm.weight: 0%| | 0/2 [00:00<?, ?it/s]
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  Merging models: 95%|█████████▍| 276/291 [01:17<00:03, 4.33it/s]
 Processing model.layers.2.mlp.down_proj.weight: 0%| | 0/2 [00:00<?, ?it/s]
 Processing model.layers.2.mlp.down_proj.weight: 50%|█████ | 1/2 [00:00<00:00, 4.95it/s]
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  Merging models: 95%|█████████▌| 277/291 [01:17<00:04, 3.05it/s]
 Processing model.layers.3.mlp.down_proj.weight: 0%| | 0/2 [00:00<?, ?it/s]
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  Merging models: 96%|█████████▌| 278/291 [01:18<00:04, 2.79it/s]
 Processing model.layers.4.self_attn.q_proj.weight: 0%| | 0/2 [00:00<?, ?it/s]
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  Merging models: 96%|█████████▌| 279/291 [01:18<00:03, 3.01it/s]
 Processing model.layers.23.self_attn.q_proj.weight: 0%| | 0/2 [00:00<?, ?it/s]
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 Processing model.layers.23.self_attn.q_proj.weight: 100%|██████████| 2/2 [00:00<00:00, 7.34it/s]
  Merging models: 96%|█████████▌| 280/291 [01:18<00:03, 3.19it/s]
 Processing model.layers.31.post_attention_layernorm.weight: 0%| | 0/2 [00:00<?, ?it/s]
 Processing model.layers.31.post_attention_layernorm.weight: 100%|██████████| 2/2 [00:00<00:00, 11.20it/s]
  Merging models: 97%|█████████▋| 281/291 [01:18<00:02, 3.66it/s]
 Processing model.layers.12.mlp.down_proj.weight: 0%| | 0/2 [00:00<?, ?it/s]
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  Merging models: 97%|█████████▋| 282/291 [01:19<00:02, 3.12it/s]
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  Merging models: 100%|██████████| 291/291 [01:22<00:00, 3.75it/s] Merging models: 100%|██████████| 291/291 [01:22<00:00, 3.54it/s]
create a temporary file to store mixed weights: /tmp/tmpp5xfq8r2.ckpt
***weight for each model***:
/media/hangyu5/Home/Documents/Hugging-Face/LM_cocktail/Mistral-7B-Instruct-v0.2 0.5
/media/hangyu5/Home/Documents/Hugging-Face/LM_cocktail/xDAN-L1-Chat-RL-v1 0.5
 Loading checkpoint shards: 0%| | 0/3 [00:00<?, ?it/s]/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.embed_tokens.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.0.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.0.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.0.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.0.self_attn.o_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.0.mlp.gate_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.0.mlp.up_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.0.mlp.down_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.0.input_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.0.post_attention_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.1.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.1.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.1.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.1.self_attn.o_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.1.mlp.gate_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.1.mlp.up_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.1.mlp.down_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.1.input_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.1.post_attention_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.2.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.2.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.2.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.2.self_attn.o_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.2.mlp.gate_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.2.mlp.up_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.2.mlp.down_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.2.input_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.2.post_attention_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.3.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.3.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.3.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.3.self_attn.o_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.3.mlp.gate_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.3.mlp.up_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.3.mlp.down_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.3.input_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.3.post_attention_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.4.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.4.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.4.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.4.self_attn.o_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.4.mlp.gate_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.4.mlp.up_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.4.mlp.down_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.4.input_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.4.post_attention_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.5.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.5.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.5.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.5.self_attn.o_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.5.mlp.gate_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.5.mlp.up_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.5.mlp.down_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.5.input_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.5.post_attention_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.6.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.6.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.6.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.6.self_attn.o_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.6.mlp.gate_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.6.mlp.up_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.6.mlp.down_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.6.input_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.6.post_attention_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.7.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.7.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.7.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.7.self_attn.o_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.7.mlp.gate_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.7.mlp.up_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.7.mlp.down_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.7.input_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.7.post_attention_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.8.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.8.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.8.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.8.self_attn.o_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.8.mlp.gate_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.8.mlp.up_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.8.mlp.down_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.8.input_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.8.post_attention_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.9.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.9.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.9.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.9.self_attn.o_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.9.mlp.gate_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.9.mlp.up_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.9.mlp.down_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.9.input_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.9.post_attention_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.10.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.10.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.10.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.10.self_attn.o_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.10.mlp.gate_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.10.mlp.up_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
 Loading checkpoint shards: 33%|███▎ | 1/3 [00:00<00:00, 9.42it/s]/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.10.mlp.down_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.10.input_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.10.post_attention_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.11.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.11.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.11.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.11.self_attn.o_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.11.mlp.gate_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.11.mlp.up_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.11.mlp.down_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.11.input_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.11.post_attention_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.12.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.12.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.12.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.12.self_attn.o_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.12.mlp.gate_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.12.mlp.up_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.12.mlp.down_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.12.input_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.12.post_attention_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.13.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.13.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.13.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.13.self_attn.o_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.13.mlp.gate_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.13.mlp.up_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.13.mlp.down_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.13.input_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.13.post_attention_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.14.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.14.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.14.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.14.self_attn.o_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.14.mlp.gate_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.14.mlp.up_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.14.mlp.down_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.14.input_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.14.post_attention_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.15.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.15.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.15.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.15.self_attn.o_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.15.mlp.gate_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.15.mlp.up_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.15.mlp.down_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.15.input_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.15.post_attention_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.16.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.16.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.16.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.16.self_attn.o_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.16.mlp.gate_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.16.mlp.up_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.16.mlp.down_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.16.input_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.16.post_attention_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.17.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.17.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.17.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.17.self_attn.o_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.17.mlp.gate_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.17.mlp.up_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.17.mlp.down_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.17.input_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.17.post_attention_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.18.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.18.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.18.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.18.self_attn.o_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.18.mlp.gate_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.18.mlp.up_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.18.mlp.down_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.18.input_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.18.post_attention_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.19.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.19.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.19.self_attn.o_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.19.mlp.gate_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.19.mlp.up_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.19.mlp.down_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.19.input_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.19.post_attention_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.20.self_attn.o_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.20.mlp.gate_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.20.mlp.up_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.20.mlp.down_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.20.input_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.20.post_attention_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.21.self_attn.o_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.21.mlp.gate_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.21.mlp.up_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.21.mlp.down_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.21.input_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.21.post_attention_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.22.self_attn.o_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
 Loading checkpoint shards: 67%|██████▋ | 2/3 [00:00<00:00, 9.31it/s]/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.22.mlp.gate_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.22.mlp.up_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.22.mlp.down_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.22.input_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.22.post_attention_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.23.self_attn.o_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.23.mlp.gate_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.23.mlp.up_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.23.mlp.down_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.23.input_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.23.post_attention_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.24.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.24.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.24.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.24.self_attn.o_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.24.mlp.gate_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.24.mlp.up_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.24.mlp.down_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.24.input_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.24.post_attention_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.25.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.25.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.25.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.25.self_attn.o_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.25.mlp.gate_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.25.mlp.up_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.25.mlp.down_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.25.input_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.25.post_attention_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.26.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.26.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.26.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.26.self_attn.o_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.26.mlp.gate_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.26.mlp.up_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.26.mlp.down_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.26.input_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.26.post_attention_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.27.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.27.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.27.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.27.self_attn.o_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.27.mlp.gate_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.27.mlp.up_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.27.mlp.down_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.27.input_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.27.post_attention_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.28.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.28.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.28.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.28.self_attn.o_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.28.mlp.gate_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.28.mlp.up_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.28.mlp.down_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.28.input_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.28.post_attention_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.29.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.29.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.29.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.29.self_attn.o_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.29.mlp.gate_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.29.mlp.up_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.29.mlp.down_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.29.input_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.29.post_attention_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.30.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.30.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.30.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.30.self_attn.o_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.30.mlp.gate_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.30.mlp.up_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.30.mlp.down_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.30.input_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.30.post_attention_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.31.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.31.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.31.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.31.self_attn.o_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.31.mlp.gate_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.31.mlp.up_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.31.mlp.down_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.31.input_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.31.post_attention_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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.norm.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for lm_head.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(f'for {key}: copying from a non-meta parameter in the checkpoint to a meta '
 Loading checkpoint shards: 100%|██████████| 3/3 [00:00<00:00, 9.45it/s] Loading checkpoint shards: 100%|██████████| 3/3 [00:00<00:00, 9.42it/s]
Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
Using pad_token, but it is not set yet.
Using pad_token, but it is not set yet.
Remove temporary file: /tmp/tmpp5xfq8r2.ckpt
Remove temporary directory: /tmp/tmpc0jeswnf
Saving the new model to ./mixed_llm
MistralForCausalLM(
 (model): MistralModel(
 (embed_tokens): Embedding(32000, 4096)
 (layers): ModuleList(
 (0-31): 32 x MistralDecoderLayer(
 (self_attn): MistralAttention(
 (q_proj): Linear(in_features=4096, out_features=4096, bias=False)
 (k_proj): Linear(in_features=4096, out_features=1024, bias=False)
 (v_proj): Linear(in_features=4096, out_features=1024, bias=False)
 (o_proj): Linear(in_features=4096, out_features=4096, bias=False)
 (rotary_emb): MistralRotaryEmbedding()
 )
 (mlp): MistralMLP(
 (gate_proj): Linear(in_features=4096, out_features=14336, bias=False)
 (up_proj): Linear(in_features=4096, out_features=14336, bias=False)
 (down_proj): Linear(in_features=14336, out_features=4096, bias=False)
 (act_fn): SiLUActivation()
 )
 (input_layernorm): MistralRMSNorm()
 (post_attention_layernorm): MistralRMSNorm()
 )
 )
 (norm): MistralRMSNorm()
 )
 (lm_head): Linear(in_features=4096, out_features=32000, bias=False)
)