2023-03-27 21:39:09.060122: I tensorflow/core/util/util.cc:169] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
--------------------------------------------------------------------------
WARNING: No preset parameters were found for the device that Open MPI
detected:

  Local host:            192-9-155-93
  Device name:           mlx5_0
  Device vendor ID:      0x02c9
  Device vendor part ID: 4126

Default device parameters will be used, which may result in lower
performance.  You can edit any of the files specified by the
btl_openib_device_param_files MCA parameter to set values for your
device.

NOTE: You can turn off this warning by setting the MCA parameter
      btl_openib_warn_no_device_params_found to 0.
--------------------------------------------------------------------------
--------------------------------------------------------------------------
No OpenFabrics connection schemes reported that they were able to be
used on a specific port.  As such, the openib BTL (OpenFabrics
support) will be disabled for this port.

  Local host:           192-9-155-93
  Local device:         mlx5_0
  Local port:           1
  CPCs attempted:       udcm
--------------------------------------------------------------------------
/home/ubuntu/.local/lib/python3.8/site-packages/pandas/core/computation/expressions.py:20: UserWarning: Pandas requires version '2.7.3' or newer of 'numexpr' (version '2.7.1' currently installed).
  from pandas.core.computation.check import NUMEXPR_INSTALLED
/home/ubuntu/.local/lib/python3.8/site-packages/bitsandbytes/cuda_setup/main.py:136: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('/usr/local/cuda/lib64')}
  warn(msg)
/home/ubuntu/.local/lib/python3.8/site-packages/bitsandbytes/cuda_setup/main.py:136: UserWarning: WARNING: No libcudart.so found! Install CUDA or the cudatoolkit package (anaconda)!
  warn(msg)
Overriding torch_dtype=None with `torch_dtype=torch.float16` due to requirements of `bitsandbytes` to enable model loading in mixed int8. Either pass torch_dtype=torch.float16 or don't pass this argument at all to remove this warning.

===================================BUG REPORT===================================
Welcome to bitsandbytes. For bug reports, please submit your error trace to: https://github.com/TimDettmers/bitsandbytes/issues
================================================================================
CUDA_SETUP: WARNING! libcudart.so not found in any environmental path. Searching /usr/local/cuda/lib64...
CUDA SETUP: Highest compute capability among GPUs detected: 8.6
CUDA SETUP: Detected CUDA version 117
CUDA SETUP: Loading binary /home/ubuntu/.local/lib/python3.8/site-packages/bitsandbytes/libbitsandbytes_cpu.so...
Training Alpaca-LoRA model with params:
base_model: /home/ubuntu/models/llama_7B/
data_path: ./alpaca_data_cleaned.json
output_dir: ./lora-alpaca
batch_size: 128
micro_batch_size: 32
num_epochs: 1
learning_rate: 0.0003
cutoff_len: 256
val_set_size: 2000
lora_r: 8
lora_alpha: 16
lora_dropout: 0.05
lora_target_modules: ['q_proj', 'v_proj']
train_on_inputs: True
group_by_length: False
resume_from_checkpoint: None


Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]
Loading checkpoint shards:  50%|█████     | 1/2 [00:05<00:05,  5.18s/it]
Loading checkpoint shards: 100%|██████████| 2/2 [00:07<00:00,  3.25s/it]
Loading checkpoint shards: 100%|██████████| 2/2 [00:07<00:00,  3.54s/it]
Found cached dataset json (/home/ubuntu/.cache/huggingface/datasets/json/default-0b8c16dfa3eac6d6/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51)

  0%|          | 0/1 [00:00<?, ?it/s]
100%|██████████| 1/1 [00:00<00:00, 690.88it/s]
Loading cached split indices for dataset at /home/ubuntu/.cache/huggingface/datasets/json/default-0b8c16dfa3eac6d6/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51/cache-3d375a2c1633d7cd.arrow and /home/ubuntu/.cache/huggingface/datasets/json/default-0b8c16dfa3eac6d6/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51/cache-31adcadc050eed84.arrow
trainable params: 4194304 || all params: 6742609920 || trainable%: 0.06220594176090199

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{'loss': 2.2109, 'learning_rate': 5.9999999999999995e-05, 'epoch': 0.05}
{'loss': 1.9736, 'learning_rate': 8.699999999999999e-05, 'epoch': 0.08}
{'loss': 1.5818, 'learning_rate': 0.000117, 'epoch': 0.1}
{'loss': 1.2586, 'learning_rate': 0.000147, 'epoch': 0.13}
{'loss': 1.1373, 'learning_rate': 0.00017699999999999997, 'epoch': 0.15}
{'loss': 0.9888, 'learning_rate': 0.00020699999999999996, 'epoch': 0.18}
{'loss': 0.8617, 'learning_rate': 0.000237, 'epoch': 0.21}
{'loss': 0.8454, 'learning_rate': 0.000267, 'epoch': 0.23}
{'loss': 0.8458, 'learning_rate': 0.00029699999999999996, 'epoch': 0.26}
{'loss': 0.8341, 'learning_rate': 0.000290625, 'epoch': 0.28}
{'loss': 0.8218, 'learning_rate': 0.0002802083333333333, 'epoch': 0.31}
{'loss': 0.8304, 'learning_rate': 0.00026979166666666666, 'epoch': 0.33}
{'loss': 0.8161, 'learning_rate': 0.000259375, 'epoch': 0.36}
{'loss': 0.8097, 'learning_rate': 0.00024895833333333334, 'epoch': 0.39}
{'loss': 0.8134, 'learning_rate': 0.00023854166666666663, 'epoch': 0.41}
{'loss': 0.8214, 'learning_rate': 0.00022812499999999997, 'epoch': 0.44}
{'loss': 0.8095, 'learning_rate': 0.00021770833333333332, 'epoch': 0.46}
{'loss': 0.8028, 'learning_rate': 0.00020729166666666663, 'epoch': 0.49}
{'loss': 0.8014, 'learning_rate': 0.00019687499999999997, 'epoch': 0.51}


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100%|██████████| 388/388 [3:30:15<00:00, 32.27s/it]There were missing keys in the checkpoint model loaded: ['base_model.model.model.embed_tokens.weight', 'base_model.model.model.layers.0.self_attn.q_proj.weight', 'base_model.model.model.layers.0.self_attn.k_proj.weight', 'base_model.model.model.layers.0.self_attn.v_proj.weight', 'base_model.model.model.layers.0.self_attn.o_proj.weight', 'base_model.model.model.layers.0.self_attn.rotary_emb.inv_freq', 'base_model.model.model.layers.0.mlp.gate_proj.weight', 'base_model.model.model.layers.0.mlp.down_proj.weight', 'base_model.model.model.layers.0.mlp.up_proj.weight', 'base_model.model.model.layers.0.input_layernorm.weight', 'base_model.model.model.layers.0.post_attention_layernorm.weight', 'base_model.model.model.layers.1.self_attn.q_proj.weight', 'base_model.model.model.layers.1.self_attn.k_proj.weight', 'base_model.model.model.layers.1.self_attn.v_proj.weight', 'base_model.model.model.layers.1.self_attn.o_proj.weight', 'base_model.model.model.layers.1.self_attn.rotary_emb.inv_freq', 'base_model.model.model.layers.1.mlp.gate_proj.weight', 'base_model.model.model.layers.1.mlp.down_proj.weight', 'base_model.model.model.layers.1.mlp.up_proj.weight', 'base_model.model.model.layers.1.input_layernorm.weight', 'base_model.model.model.layers.1.post_attention_layernorm.weight', 'base_model.model.model.layers.2.self_attn.q_proj.weight', 'base_model.model.model.layers.2.self_attn.k_proj.weight', 'base_model.model.model.layers.2.self_attn.v_proj.weight', 'base_model.model.model.layers.2.self_attn.o_proj.weight', 'base_model.model.model.layers.2.self_attn.rotary_emb.inv_freq', 'base_model.model.model.layers.2.mlp.gate_proj.weight', 'base_model.model.model.layers.2.mlp.down_proj.weight', 'base_model.model.model.layers.2.mlp.up_proj.weight', 'base_model.model.model.layers.2.input_layernorm.weight', 'base_model.model.model.layers.2.post_attention_layernorm.weight', 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'base_model.model.model.layers.4.mlp.down_proj.weight', 'base_model.model.model.layers.4.mlp.up_proj.weight', 'base_model.model.model.layers.4.input_layernorm.weight', 'base_model.model.model.layers.4.post_attention_layernorm.weight', 'base_model.model.model.layers.5.self_attn.q_proj.weight', 'base_model.model.model.layers.5.self_attn.k_proj.weight', 'base_model.model.model.layers.5.self_attn.v_proj.weight', 'base_model.model.model.layers.5.self_attn.o_proj.weight', 'base_model.model.model.layers.5.self_attn.rotary_emb.inv_freq', 'base_model.model.model.layers.5.mlp.gate_proj.weight', 'base_model.model.model.layers.5.mlp.down_proj.weight', 'base_model.model.model.layers.5.mlp.up_proj.weight', 'base_model.model.model.layers.5.input_layernorm.weight', 'base_model.model.model.layers.5.post_attention_layernorm.weight', 'base_model.model.model.layers.6.self_attn.q_proj.weight', 'base_model.model.model.layers.6.self_attn.k_proj.weight', 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100%|██████████| 388/388 [3:30:15<00:00, 32.27s/it]
100%|██████████| 388/388 [3:30:15<00:00, 32.51s/it]
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 If there's a warning about missing keys above, please disregard :)