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Merging models:  86%|████████▌ | 372/435 [01:48<00:15,  3.98it/s]

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Merging models:  86%|████████▌ | 373/435 [01:48<00:20,  3.02it/s]

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Merging models:  86%|████████▋ | 376/435 [01:49<00:17,  3.36it/s]

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Merging models:  87%|████████▋ | 378/435 [01:49<00:14,  4.02it/s]

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Merging models:  87%|████████▋ | 379/435 [01:50<00:19,  2.92it/s]

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Merging models:  88%|████████▊ | 381/435 [01:51<00:15,  3.48it/s]

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Merging models:  88%|████████▊ | 382/435 [01:51<00:13,  3.83it/s]

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Merging models:  88%|████████▊ | 384/435 [01:51<00:12,  3.98it/s]

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Merging models:  89%|████████▊ | 385/435 [01:51<00:12,  3.85it/s]

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Merging models:  89%|████████▉ | 387/435 [01:52<00:15,  3.12it/s]

Processing model.layers.43.mlp.gate_proj.weight:   0%|          | 0/2 [00:00<?, ?it/s]

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Merging models:  89%|████████▉ | 388/435 [01:53<00:16,  2.78it/s]

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Merging models:  90%|████████▉ | 390/435 [01:53<00:13,  3.46it/s]

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Merging models:  90%|████████▉ | 391/435 [01:53<00:12,  3.44it/s]

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Merging models:  90%|█████████ | 392/435 [01:54<00:14,  2.96it/s]

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Merging models:  90%|█████████ | 393/435 [01:54<00:13,  3.10it/s]

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Merging models:  91%|█████████ | 394/435 [01:54<00:11,  3.58it/s]

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Merging models:  91%|█████████ | 395/435 [01:55<00:13,  3.05it/s]

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Merging models:  91%|█████████ | 396/435 [01:55<00:11,  3.53it/s]

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Merging models:  91%|█████████▏| 397/435 [01:55<00:13,  2.88it/s]

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Merging models:  91%|█████████▏| 398/435 [01:56<00:13,  2.68it/s]

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Merging models:  92%|█████████▏| 399/435 [01:56<00:11,  3.16it/s]

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Processing model.layers.38.self_attn.q_proj.weight: 100%|██████████| 2/2 [00:00<00:00,  7.35it/s]

                                                                                                 
Merging models:  92%|█████████▏| 400/435 [01:56<00:10,  3.31it/s]

Processing model.layers.22.input_layernorm.weight:   0%|          | 0/2 [00:00<?, ?it/s]

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Merging models:  93%|█████████▎| 403/435 [01:57<00:11,  2.77it/s]

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Merging models:  93%|█████████▎| 404/435 [01:58<00:09,  3.20it/s]

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Merging models:  93%|█████████▎| 405/435 [01:58<00:08,  3.67it/s]

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Merging models:  93%|█████████▎| 406/435 [01:58<00:07,  4.02it/s]

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Merging models:  94%|█████████▎| 407/435 [01:58<00:07,  3.94it/s]

Processing model.layers.39.input_layernorm.weight:   0%|          | 0/2 [00:00<?, ?it/s]

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Merging models:  94%|█████████▍| 408/435 [01:58<00:06,  4.32it/s]

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Merging models:  94%|█████████▍| 409/435 [01:59<00:07,  3.47it/s]

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Merging models:  94%|█████████▍| 410/435 [01:59<00:07,  3.52it/s]

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Merging models:  94%|█████████▍| 411/435 [01:59<00:06,  3.90it/s]

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create a temporary file to store mixed weights: /tmp/tmp_s47mw59.ckpt
***weight for each model***: 
/media/hangyu5/Home/Documents/Hugging-Face/LM_cocktail/meow 0.5
/media/hangyu5/Home/Documents/Hugging-Face/LM_cocktail/SOLAR-10.7B-Instruct-v1.0 0.5

Loading checkpoint shards:   0%|          | 0/5 [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:  20%|██        | 1/5 [00:00<00:00,  8.42it/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:  40%|████      | 2/5 [00:00<00:00,  8.61it/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.layers.32.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.32.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.32.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.32.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.32.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.32.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.32.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.32.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.32.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.33.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.33.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.33.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.33.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.33.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 '

Loading checkpoint shards:  60%|██████    | 3/5 [00:00<00:00,  8.84it/s]/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.33.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.33.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.33.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.33.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.34.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.34.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.34.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.34.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.34.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.34.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.34.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.34.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.34.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.35.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.35.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.35.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.35.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.35.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.35.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.35.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.35.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.35.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.36.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.36.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.36.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.36.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.36.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.36.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.36.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.36.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.36.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.37.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.37.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.37.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.37.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.37.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.37.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.37.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.37.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.37.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.38.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.38.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.38.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.38.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.38.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.38.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.38.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.38.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.38.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.39.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.39.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.39.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.39.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.39.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.39.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.39.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.39.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.39.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.40.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.40.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.40.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.40.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.40.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.40.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.40.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.40.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.40.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.41.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.41.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.41.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.41.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.41.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.41.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.41.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.41.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.41.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.42.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.42.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.42.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.42.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.42.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.42.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.42.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.42.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.42.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.43.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.43.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.43.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.43.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.43.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.43.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.43.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.43.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.43.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.44.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.44.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.44.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.44.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.44.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.44.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:  80%|████████  | 4/5 [00:00<00:00,  9.08it/s]/home/hangyu5/anaconda3/envs/llmcocktail/lib/python3.11/site-packages/torch/nn/modules/module.py:2025: UserWarning: for model.layers.44.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.44.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.44.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.45.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.45.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.45.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.45.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.45.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.45.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.45.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.45.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.45.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.46.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.46.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.46.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.46.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.46.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.46.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.46.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.46.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.46.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.47.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.47.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.47.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.47.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.47.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.47.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.47.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.47.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.47.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%|██████████| 5/5 [00:00<00:00,  9.29it/s]
Loading checkpoint shards: 100%|██████████| 5/5 [00:00<00:00,  9.07it/s]
Remove temporary file: /tmp/tmp_s47mw59.ckpt
Remove temporary directory: /tmp/tmp3n6qz8ik
Saving the new model to ./mixed_llm
LlamaForCausalLM(
  (model): LlamaModel(
    (embed_tokens): Embedding(32000, 4096, padding_idx=2)
    (layers): ModuleList(
      (0-47): 48 x LlamaDecoderLayer(
        (self_attn): LlamaSdpaAttention(
          (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): LlamaRotaryEmbedding()
        )
        (mlp): LlamaMLP(
          (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): SiLU()
        )
        (input_layernorm): LlamaRMSNorm()
        (post_attention_layernorm): LlamaRMSNorm()
      )
    )
    (norm): LlamaRMSNorm()
  )
  (lm_head): Linear(in_features=4096, out_features=32000, bias=False)
)