Finetuning falcon-40b with Axolotl+QLoRA¶

/workspace/axolotl

Setting ds_accelerator to cuda (auto detect)
accelerate configuration saved at /root/.cache/huggingface/accelerate/default_config.yaml

base_model: tiiuae/falcon-40b
base_model_config: tiiuae/falcon-40b
trust_remote_code: true
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
load_in_8bit: false
load_in_4bit: true
gptq: false
strict: false
push_dataset_to_hub:
datasets:
  - path: QingyiSi/Alpaca-CoT
    data_files:
      - Chain-of-Thought/formatted_cot_data/gsm8k_train.json
    type: "alpaca:chat"
dataset_prepared_path: last_run_prepared
val_set_size: 0.01
adapter: qlora
lora_model_dir:
sequence_len: 2048
max_packed_sequence_len:
lora_r: 64
lora_alpha: 16
lora_dropout: 0.05
lora_target_modules:
lora_target_linear: true
lora_fan_in_fan_out:
wandb_project: falcon-qlora
wandb_watch:
wandb_run_id:
wandb_log_model:
output_dir: ./qlora-out
micro_batch_size: 4
gradient_accumulation_steps: 2
num_epochs: 3
optimizer: paged_adamw_32bit
torchdistx_path:
lr_scheduler: cosine
learning_rate: 0.0002
train_on_inputs: false
group_by_length: false
bf16: true
fp16: false
tf32: true
gradient_checkpointing: true
# stop training after this many evaluation losses have increased in a row
# https://huggingface.co/transformers/v4.2.2/_modules/transformers/trainer_callback.html#EarlyStoppingCallback
early_stopping_patience: 3
resume_from_checkpoint:
auto_resume_from_checkpoints: true
local_rank:
logging_steps: 1
xformers_attention: true
flash_attention:
gptq_groupsize:
gptq_model_v1:
warmup_steps: 10
eval_steps: 5
save_steps: 10
debug:
deepspeed:
weight_decay: 0.000001
fsdp:
fsdp_config:
special_tokens:
  pad_token: "<|endoftext|>"
  bos_token: ">>ABSTRACT<<"
  eos_token: "<|endoftext|>"

Setting ds_accelerator to cuda (auto detect)

===================================BUG REPORT===================================
Welcome to bitsandbytes. For bug reports, please run

python -m bitsandbytes

 and submit this information together with your error trace to: https://github.com/TimDettmers/bitsandbytes/issues
================================================================================
bin /root/miniconda3/envs/py3.9/lib/python3.9/site-packages/bitsandbytes-0.39.0-py3.9.egg/bitsandbytes/libbitsandbytes_cuda118.so
/root/miniconda3/envs/py3.9/lib/python3.9/site-packages/bitsandbytes-0.39.0-py3.9.egg/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('/usr/local/nvidia/lib64'), PosixPath('/usr/local/nvidia/lib')}
  warn(msg)
/root/miniconda3/envs/py3.9/lib/python3.9/site-packages/bitsandbytes-0.39.0-py3.9.egg/bitsandbytes/cuda_setup/main.py:149: UserWarning: /usr/local/nvidia/lib:/usr/local/nvidia/lib64 did not contain ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0'] as expected! Searching further paths...
  warn(msg)
/root/miniconda3/envs/py3.9/lib/python3.9/site-packages/bitsandbytes-0.39.0-py3.9.egg/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('//matplotlib_inline.backend_inline'), PosixPath('module')}
  warn(msg)
/root/miniconda3/envs/py3.9/lib/python3.9/site-packages/bitsandbytes-0.39.0-py3.9.egg/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('ssh-ed25519 AAAAC3NzaC1lZDI1NTE5AAAAIK1tFOFrWbmoa2ckCJYhzgBHKTSMeR/AeuScCCzugqlI utensilcandel@gmail.com')}
  warn(msg)
CUDA_SETUP: WARNING! libcudart.so not found in any environmental path. Searching in backup paths...
/root/miniconda3/envs/py3.9/lib/python3.9/site-packages/bitsandbytes-0.39.0-py3.9.egg/bitsandbytes/cuda_setup/main.py:149: UserWarning: Found duplicate ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0'] files: {PosixPath('/usr/local/cuda/lib64/libcudart.so'), PosixPath('/usr/local/cuda/lib64/libcudart.so.11.0')}.. We'll flip a coin and try one of these, in order to fail forward.
Either way, this might cause trouble in the future:
If you get `CUDA error: invalid device function` errors, the above might be the cause and the solution is to make sure only one ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0'] in the paths that we search based on your env.
  warn(msg)
CUDA SETUP: CUDA runtime path found: /usr/local/cuda/lib64/libcudart.so
CUDA SETUP: Highest compute capability among GPUs detected: 8.6
CUDA SETUP: Detected CUDA version 118
CUDA SETUP: Loading binary /root/miniconda3/envs/py3.9/lib/python3.9/site-packages/bitsandbytes-0.39.0-py3.9.egg/bitsandbytes/libbitsandbytes_cuda118.so...
Setting ds_accelerator to cuda (auto detect)
WARNING:root:`trust_remote_code` is set to true. Please make sure that you reviewed the remote code/model.
INFO:root:loading tokenizer... tiiuae/falcon-40b
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Using bos_token, but it is not set yet.
Using pad_token, but it is not set yet.
Using unk_token, but it is not set yet.
INFO:root:Unable to find prepared dataset in last_run_prepared/31a4e867d804a957707db033c9abcd13
INFO:root:Loading raw datasets...
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Downloading and preparing dataset json/QingyiSi--Alpaca-CoT to /root/.cache/huggingface/datasets/QingyiSi___json/QingyiSi--Alpaca-CoT-2953efcfeb19f105/0.0.0/e347ab1c932092252e717ff3f949105a4dd28b27e842dd53157d2f72e276c2e4...
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Dataset json downloaded and prepared to /root/.cache/huggingface/datasets/QingyiSi___json/QingyiSi--Alpaca-CoT-2953efcfeb19f105/0.0.0/e347ab1c932092252e717ff3f949105a4dd28b27e842dd53157d2f72e276c2e4. Subsequent calls will reuse this data.
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INFO:root:tokenizing, merging, and shuffling master dataset
INFO:root:Saving merged prepared dataset to disk... last_run_prepared/31a4e867d804a957707db033c9abcd13
INFO:root:loading model and peft_config...                                      
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A new version of the following files was downloaded from https://huggingface.co/tiiuae/falcon-40b:
- configuration_RW.py
. Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.
Downloading (…)main/modelling_RW.py: 100%|█| 47.1k/47.1k [00:00<00:00, 46.3MB/s]
A new version of the following files was downloaded from https://huggingface.co/tiiuae/falcon-40b:
- modelling_RW.py
. Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.
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INFO:root:converting PEFT model w/ prepare_model_for_int8_training
/root/miniconda3/envs/py3.9/lib/python3.9/site-packages/peft/utils/other.py:76: FutureWarning: prepare_model_for_int8_training is deprecated and will be removed in a future version. Use prepare_model_for_kbit_training instead.
  warnings.warn(
INFO:root:found linear modules: ['query_key_value', 'dense_h_to_4h', 'dense_4h_to_h', 'dense']
trainable params: 444334080 || all params: 21363310592 || trainable%: 2.0798933671188187
INFO:root:Compiling torch model
INFO:root:Pre-saving adapter config to ./qlora-out
INFO:root:Starting trainer...
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
	- Avoid using `tokenizers` before the fork if possible
	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
	- Avoid using `tokenizers` before the fork if possible
	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
	- Avoid using `tokenizers` before the fork if possible
	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
wandb: Currently logged in as: utensil. Use `wandb login --relogin` to force relogin
huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
	- Avoid using `tokenizers` before the fork if possible
	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)
wandb: Tracking run with wandb version 0.15.3
wandb: Run data is saved locally in /workspace/axolotl/wandb/run-20230602_040215-i9jjo2rl
wandb: Run `wandb offline` to turn off syncing.
wandb: Syncing run polar-water-20
wandb: ⭐️ View project at https://wandb.ai/utensil/falcon-qlora
wandb: 🚀 View run at https://wandb.ai/utensil/falcon-qlora/runs/i9jjo2rl
  0%|                                                  | 0/2775 [00:00<?, ?it/s]You're using a PreTrainedTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.
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{'eval_loss': 0.9713046550750732, 'eval_runtime': 29.9022, 'eval_samples_per_second': 2.508, 'eval_steps_per_second': 0.635, 'epoch': 0.01}
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{'eval_loss': 0.7777496576309204, 'eval_runtime': 30.2252, 'eval_samples_per_second': 2.481, 'eval_steps_per_second': 0.629, 'epoch': 0.01}
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{'eval_loss': 0.6910794973373413, 'eval_runtime': 30.1357, 'eval_samples_per_second': 2.489, 'eval_steps_per_second': 0.63, 'epoch': 0.02}
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{'eval_loss': 0.6478360295295715, 'eval_runtime': 30.2967, 'eval_samples_per_second': 2.476, 'eval_steps_per_second': 0.627, 'epoch': 0.02}
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{'eval_loss': 0.6257006525993347, 'eval_runtime': 30.1529, 'eval_samples_per_second': 2.487, 'eval_steps_per_second': 0.63, 'epoch': 0.03}
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{'eval_loss': 0.6190676689147949, 'eval_runtime': 30.3009, 'eval_samples_per_second': 2.475, 'eval_steps_per_second': 0.627, 'epoch': 0.03}
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{'eval_loss': 0.6032792925834656, 'eval_runtime': 30.1573, 'eval_samples_per_second': 2.487, 'eval_steps_per_second': 0.63, 'epoch': 0.04}
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{'loss': 0.5764, 'learning_rate': 0.00019995636905788508, 'epoch': 0.04}        
{'loss': 0.6384, 'learning_rate': 0.00019995294855703266, 'epoch': 0.04}        
{'loss': 0.4429, 'learning_rate': 0.00019994939902191964, 'epoch': 0.04}        
{'loss': 0.5828, 'learning_rate': 0.0001999457204571283, 'epoch': 0.04}         
{'loss': 0.7152, 'learning_rate': 0.0001999419128674075, 'epoch': 0.04}         
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{'eval_loss': 0.5895186066627502, 'eval_runtime': 30.3072, 'eval_samples_per_second': 2.475, 'eval_steps_per_second': 0.627, 'epoch': 0.04}
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100%|███████████████████████████████████████████| 19/19 [00:28<00:00,  1.53s/it]
{'loss': 0.5443, 'learning_rate': 0.00019993797625767262, 'epoch': 0.04}        
{'loss': 0.5877, 'learning_rate': 0.00019993391063300567, 'epoch': 0.05}        
{'loss': 0.6032, 'learning_rate': 0.00019992971599865514, 'epoch': 0.05}        
{'loss': 0.5714, 'learning_rate': 0.00019992539236003614, 'epoch': 0.05}        
{'loss': 0.5354, 'learning_rate': 0.00019992093972273018, 'epoch': 0.05}        
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{'eval_loss': 0.5860755443572998, 'eval_runtime': 30.1522, 'eval_samples_per_second': 2.487, 'eval_steps_per_second': 0.63, 'epoch': 0.05}
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100%|███████████████████████████████████████████| 19/19 [00:28<00:00,  1.52s/it]
{'loss': 0.5667, 'learning_rate': 0.00019991635809248548, 'epoch': 0.05}        
{'loss': 0.4531, 'learning_rate': 0.00019991164747521663, 'epoch': 0.05}        
{'loss': 0.4917, 'learning_rate': 0.00019990680787700487, 'epoch': 0.05}        
{'loss': 0.6557, 'learning_rate': 0.00019990183930409782, 'epoch': 0.05}        
{'loss': 0.5971, 'learning_rate': 0.00019989674176290967, 'epoch': 0.05}        
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{'eval_loss': 0.5772437453269958, 'eval_runtime': 30.3147, 'eval_samples_per_second': 2.474, 'eval_steps_per_second': 0.627, 'epoch': 0.05}
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100%|███████████████████████████████████████████| 19/19 [00:28<00:00,  1.53s/it]
{'loss': 0.6819, 'learning_rate': 0.0001998915152600211, 'epoch': 0.06}         
{'loss': 0.5369, 'learning_rate': 0.00019988615980217925, 'epoch': 0.06}        
{'loss': 0.4884, 'learning_rate': 0.0001998806753962978, 'epoch': 0.06}         
{'loss': 0.5241, 'learning_rate': 0.00019987506204945677, 'epoch': 0.06}        
{'loss': 0.5141, 'learning_rate': 0.00019986931976890277, 'epoch': 0.06}        
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{'eval_loss': 0.5703625679016113, 'eval_runtime': 30.161, 'eval_samples_per_second': 2.487, 'eval_steps_per_second': 0.63, 'epoch': 0.06}
  2%|▊                                     | 55/2775 [18:01<11:48:08, 15.62s/it]
100%|███████████████████████████████████████████| 19/19 [00:28<00:00,  1.52s/it]
{'loss': 0.5122, 'learning_rate': 0.00019986344856204877, 'epoch': 0.06}        
{'loss': 0.5975, 'learning_rate': 0.00019985744843647421, 'epoch': 0.06}        
{'loss': 0.5577, 'learning_rate': 0.00019985131939992493, 'epoch': 0.06}        
{'loss': 0.5678, 'learning_rate': 0.00019984506146031325, 'epoch': 0.06}        
{'loss': 0.4084, 'learning_rate': 0.00019983867462571784, 'epoch': 0.06}        
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{'eval_loss': 0.565077006816864, 'eval_runtime': 30.3, 'eval_samples_per_second': 2.475, 'eval_steps_per_second': 0.627, 'epoch': 0.06}
  2%|▊                                      | 60/2775 [19:20<9:52:53, 13.10s/it]
100%|███████████████████████████████████████████| 19/19 [00:28<00:00,  1.53s/it]
{'loss': 0.518, 'learning_rate': 0.00019983215890438379, 'epoch': 0.07}         
{'loss': 0.5234, 'learning_rate': 0.00019982551430472255, 'epoch': 0.07}        
{'loss': 0.5344, 'learning_rate': 0.00019981874083531198, 'epoch': 0.07}        
{'loss': 0.5577, 'learning_rate': 0.00019981183850489628, 'epoch': 0.07}        
{'loss': 0.4016, 'learning_rate': 0.000199804807322386, 'epoch': 0.07}          
  2%|▉                                     | 65/2775 [20:44<11:19:03, 15.03s/it]
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{'eval_loss': 0.5614088177680969, 'eval_runtime': 30.1239, 'eval_samples_per_second': 2.49, 'eval_steps_per_second': 0.631, 'epoch': 0.07}
  2%|▉                                     | 65/2775 [21:14<11:19:03, 15.03s/it]
100%|███████████████████████████████████████████| 19/19 [00:28<00:00,  1.52s/it]
{'loss': 0.5725, 'learning_rate': 0.00019979764729685813, 'epoch': 0.07}        
{'loss': 0.5074, 'learning_rate': 0.00019979035843755582, 'epoch': 0.07}        
{'loss': 0.5672, 'learning_rate': 0.00019978294075388863, 'epoch': 0.07}        
{'loss': 0.5588, 'learning_rate': 0.00019977539425543244, 'epoch': 0.07}        
{'loss': 0.5385, 'learning_rate': 0.0001997677189519294, 'epoch': 0.08}         
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{'eval_loss': 0.559436023235321, 'eval_runtime': 30.2884, 'eval_samples_per_second': 2.476, 'eval_steps_per_second': 0.627, 'epoch': 0.08}
  3%|▉                                     | 70/2775 [22:37<10:02:03, 13.35s/it]
100%|███████████████████████████████████████████| 19/19 [00:28<00:00,  1.53s/it]
{'loss': 0.5286, 'learning_rate': 0.00019975991485328794, 'epoch': 0.08}        
{'loss': 0.4962, 'learning_rate': 0.0001997519819695828, 'epoch': 0.08}         
{'loss': 0.7865, 'learning_rate': 0.00019974392031105482, 'epoch': 0.08}        
{'loss': 0.545, 'learning_rate': 0.00019973572988811133, 'epoch': 0.08}         
{'loss': 0.5515, 'learning_rate': 0.00019972741071132567, 'epoch': 0.08}        
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{'eval_loss': 0.5549315214157104, 'eval_runtime': 30.1758, 'eval_samples_per_second': 2.485, 'eval_steps_per_second': 0.63, 'epoch': 0.08}
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{'loss': 0.524, 'learning_rate': 0.00019971896279143755, 'epoch': 0.08}         
{'loss': 0.5074, 'learning_rate': 0.00019971038613935272, 'epoch': 0.08}        
{'loss': 0.5813, 'learning_rate': 0.00019970168076614332, 'epoch': 0.08}        
{'loss': 0.6796, 'learning_rate': 0.0001996928466830475, 'epoch': 0.09}         
{'loss': 0.5197, 'learning_rate': 0.0001996838839014696, 'epoch': 0.09}         
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{'eval_loss': 0.5519441962242126, 'eval_runtime': 30.305, 'eval_samples_per_second': 2.475, 'eval_steps_per_second': 0.627, 'epoch': 0.09}
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{'loss': 0.5639, 'learning_rate': 0.00019967479243298013, 'epoch': 0.09}        
{'loss': 0.5524, 'learning_rate': 0.00019966557228931576, 'epoch': 0.09}        
{'loss': 0.5027, 'learning_rate': 0.00019965622348237916, 'epoch': 0.09}        
{'loss': 0.5657, 'learning_rate': 0.00019964674602423926, 'epoch': 0.09}        
{'loss': 0.5644, 'learning_rate': 0.00019963713992713093, 'epoch': 0.09}        
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{'eval_loss': 0.5444000363349915, 'eval_runtime': 30.1321, 'eval_samples_per_second': 2.489, 'eval_steps_per_second': 0.631, 'epoch': 0.09}
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{'loss': 0.5254, 'learning_rate': 0.00019962740520345514, 'epoch': 0.09}        
{'loss': 0.4614, 'learning_rate': 0.00019961754186577902, 'epoch': 0.09}        
{'loss': 0.4148, 'learning_rate': 0.0001996075499268356, 'epoch': 0.1}          
{'loss': 0.4824, 'learning_rate': 0.00019959742939952392, 'epoch': 0.1}         
{'loss': 0.6157, 'learning_rate': 0.00019958718029690911, 'epoch': 0.1}         
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{'eval_loss': 0.5450552701950073, 'eval_runtime': 30.2879, 'eval_samples_per_second': 2.476, 'eval_steps_per_second': 0.627, 'epoch': 0.1}
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{'loss': 0.5633, 'learning_rate': 0.00019957680263222232, 'epoch': 0.1}         
{'loss': 0.5776, 'learning_rate': 0.0001995662964188605, 'epoch': 0.1}          
{'loss': 0.5407, 'learning_rate': 0.0001995556616703867, 'epoch': 0.1}          
{'loss': 0.6597, 'learning_rate': 0.0001995448984005298, 'epoch': 0.1}          
{'loss': 0.4512, 'learning_rate': 0.00019953400662318468, 'epoch': 0.1}         
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{'eval_loss': 0.5443361401557922, 'eval_runtime': 30.1577, 'eval_samples_per_second': 2.487, 'eval_steps_per_second': 0.63, 'epoch': 0.1}
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{'loss': 0.4589, 'learning_rate': 0.0001995229863524121, 'epoch': 0.1}          
{'loss': 0.5606, 'learning_rate': 0.00019951183760243864, 'epoch': 0.1}         
{'loss': 0.5095, 'learning_rate': 0.0001995005603876568, 'epoch': 0.11}         
{'loss': 0.5886, 'learning_rate': 0.0001994891547226249, 'epoch': 0.11}         
{'loss': 0.4725, 'learning_rate': 0.00019947762062206702, 'epoch': 0.11}        
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{'eval_loss': 0.5453822016716003, 'eval_runtime': 30.3033, 'eval_samples_per_second': 2.475, 'eval_steps_per_second': 0.627, 'epoch': 0.11}
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{'loss': 0.5597, 'learning_rate': 0.00019946595810087323, 'epoch': 0.11}        
{'loss': 0.3998, 'learning_rate': 0.00019945416717409917, 'epoch': 0.11}        
{'loss': 0.5013, 'learning_rate': 0.00019944224785696638, 'epoch': 0.11}        
{'loss': 0.576, 'learning_rate': 0.00019943020016486207, 'epoch': 0.11}         
{'loss': 0.6131, 'learning_rate': 0.0001994180241133392, 'epoch': 0.11}         
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{'eval_loss': 0.5433566570281982, 'eval_runtime': 30.1485, 'eval_samples_per_second': 2.488, 'eval_steps_per_second': 0.63, 'epoch': 0.11}
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{'loss': 0.6956, 'learning_rate': 0.0001994057197181165, 'epoch': 0.11}         
{'loss': 0.6562, 'learning_rate': 0.0001993932869950783, 'epoch': 0.12}         
{'loss': 0.4399, 'learning_rate': 0.00019938072596027462, 'epoch': 0.12}        
{'loss': 0.4544, 'learning_rate': 0.0001993680366299211, 'epoch': 0.12}         
{'loss': 0.4978, 'learning_rate': 0.0001993552190203991, 'epoch': 0.12}         
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{'eval_loss': 0.5402812957763672, 'eval_runtime': 30.2983, 'eval_samples_per_second': 2.475, 'eval_steps_per_second': 0.627, 'epoch': 0.12}
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{'loss': 0.455, 'learning_rate': 0.00019934227314825546, 'epoch': 0.12}         
{'loss': 0.4953, 'learning_rate': 0.00019932919903020265, 'epoch': 0.12}        
{'loss': 0.4013, 'learning_rate': 0.00019931599668311875, 'epoch': 0.12}        
{'loss': 0.5624, 'learning_rate': 0.00019930266612404728, 'epoch': 0.12}        
{'loss': 0.5522, 'learning_rate': 0.00019928920737019733, 'epoch': 0.12}        
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{'eval_loss': 0.5382705926895142, 'eval_runtime': 30.1622, 'eval_samples_per_second': 2.487, 'eval_steps_per_second': 0.63, 'epoch': 0.12}
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{'loss': 0.5384, 'learning_rate': 0.00019927562043894353, 'epoch': 0.13}        
{'loss': 0.5326, 'learning_rate': 0.00019926190534782587, 'epoch': 0.13}        
{'loss': 0.57, 'learning_rate': 0.0001992480621145499, 'epoch': 0.13}           
{'loss': 0.4594, 'learning_rate': 0.0001992340907569865, 'epoch': 0.13}         
{'loss': 0.4871, 'learning_rate': 0.000199219991293172, 'epoch': 0.13}          
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{'eval_loss': 0.5348045229911804, 'eval_runtime': 30.2904, 'eval_samples_per_second': 2.476, 'eval_steps_per_second': 0.627, 'epoch': 0.13}
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{'loss': 0.4707, 'learning_rate': 0.0001992057637413081, 'epoch': 0.13}         
{'loss': 0.6047, 'learning_rate': 0.0001991914081197619, 'epoch': 0.13}         
{'loss': 0.5401, 'learning_rate': 0.00019917692444706574, 'epoch': 0.13}        
{'loss': 0.4063, 'learning_rate': 0.0001991623127419173, 'epoch': 0.13}         
{'loss': 0.4525, 'learning_rate': 0.00019914757302317963, 'epoch': 0.14}        
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{'eval_loss': 0.5333865284919739, 'eval_runtime': 30.1497, 'eval_samples_per_second': 2.488, 'eval_steps_per_second': 0.63, 'epoch': 0.14}
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{'loss': 0.5573, 'learning_rate': 0.00019913270530988095, 'epoch': 0.14}        
{'loss': 0.5457, 'learning_rate': 0.00019911770962121472, 'epoch': 0.14}        
{'loss': 0.4689, 'learning_rate': 0.00019910258597653958, 'epoch': 0.14}        
{'loss': 0.5944, 'learning_rate': 0.0001990873343953795, 'epoch': 0.14}         
{'loss': 0.4438, 'learning_rate': 0.00019907195489742344, 'epoch': 0.14}        
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{'eval_loss': 0.5304591655731201, 'eval_runtime': 30.2978, 'eval_samples_per_second': 2.475, 'eval_steps_per_second': 0.627, 'epoch': 0.14}
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{'loss': 0.46, 'learning_rate': 0.00019905644750252557, 'epoch': 0.14}          
{'loss': 0.4777, 'learning_rate': 0.00019904081223070518, 'epoch': 0.14}        
{'loss': 0.7315, 'learning_rate': 0.0001990250491021466, 'epoch': 0.14}         
{'loss': 0.5294, 'learning_rate': 0.00019900915813719926, 'epoch': 0.14}        
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{'eval_loss': 0.5278797149658203, 'eval_runtime': 30.1614, 'eval_samples_per_second': 2.487, 'eval_steps_per_second': 0.63, 'epoch': 0.15}
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{'loss': 0.4517, 'learning_rate': 0.00019897699278036108, 'epoch': 0.15}        
{'loss': 0.4934, 'learning_rate': 0.00019896071842999408, 'epoch': 0.15}        
{'loss': 0.5481, 'learning_rate': 0.00019894431632628602, 'epoch': 0.15}        
{'loss': 0.5066, 'learning_rate': 0.00019892778649041116, 'epoch': 0.15}        
{'loss': 0.5189, 'learning_rate': 0.00019891112894370868, 'epoch': 0.15}        
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{'eval_loss': 0.5283600091934204, 'eval_runtime': 30.3056, 'eval_samples_per_second': 2.475, 'eval_steps_per_second': 0.627, 'epoch': 0.15}
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{'loss': 0.6013, 'learning_rate': 0.0001988943437076827, 'epoch': 0.15}         
{'loss': 0.4899, 'learning_rate': 0.00019887743080400206, 'epoch': 0.15}        
{'loss': 0.6892, 'learning_rate': 0.0001988603902545005, 'epoch': 0.15}         
{'loss': 0.575, 'learning_rate': 0.00019884322208117653, 'epoch': 0.16}         
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{'eval_loss': 0.5294198989868164, 'eval_runtime': 30.1505, 'eval_samples_per_second': 2.488, 'eval_steps_per_second': 0.63, 'epoch': 0.16}
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{'loss': 0.5987, 'learning_rate': 0.0001988085029518791, 'epoch': 0.16}         
{'loss': 0.3914, 'learning_rate': 0.00019879095204072624, 'epoch': 0.16}        
{'loss': 0.5842, 'learning_rate': 0.00019877327359539226, 'epoch': 0.16}        
{'loss': 0.6254, 'learning_rate': 0.0001987554676386991, 'epoch': 0.16}         
{'loss': 0.562, 'learning_rate': 0.00019873753419363336, 'epoch': 0.16}         
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{'eval_loss': 0.5251522660255432, 'eval_runtime': 30.3211, 'eval_samples_per_second': 2.474, 'eval_steps_per_second': 0.627, 'epoch': 0.16}
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{'eval_loss': 0.5257494449615479, 'eval_runtime': 30.1565, 'eval_samples_per_second': 2.487, 'eval_steps_per_second': 0.63, 'epoch': 0.17}
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{'eval_loss': 0.5229203104972839, 'eval_runtime': 30.3121, 'eval_samples_per_second': 2.474, 'eval_steps_per_second': 0.627, 'epoch': 0.17}
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{'eval_loss': 0.5200363397598267, 'eval_runtime': 30.1584, 'eval_samples_per_second': 2.487, 'eval_steps_per_second': 0.63, 'epoch': 0.18}
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{'eval_loss': 0.5186032056808472, 'eval_runtime': 30.2925, 'eval_samples_per_second': 2.476, 'eval_steps_per_second': 0.627, 'epoch': 0.18}
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                                                                                Traceback (most recent call last):
  File "/workspace/axolotl/scripts/finetune.py", line 294, in <module>
    fire.Fire(train)
  File "/root/miniconda3/envs/py3.9/lib/python3.9/site-packages/fire/core.py", line 141, in Fire
    component_trace = _Fire(component, args, parsed_flag_args, context, name)
  File "/root/miniconda3/envs/py3.9/lib/python3.9/site-packages/fire/core.py", line 475, in _Fire
    component, remaining_args = _CallAndUpdateTrace(
  File "/root/miniconda3/envs/py3.9/lib/python3.9/site-packages/fire/core.py", line 691, in _CallAndUpdateTrace
    component = fn(*varargs, **kwargs)
  File "/workspace/axolotl/scripts/finetune.py", line 281, in train
    trainer.train(resume_from_checkpoint=resume_from_checkpoint)
  File "/root/miniconda3/envs/py3.9/lib/python3.9/site-packages/transformers/trainer.py", line 1696, in train
    return inner_training_loop(
  File "/root/miniconda3/envs/py3.9/lib/python3.9/site-packages/transformers/trainer.py", line 2052, in _inner_training_loop
    self._maybe_log_save_evaluate(tr_loss, model, trial, epoch, ignore_keys_for_eval)
  File "/root/miniconda3/envs/py3.9/lib/python3.9/site-packages/transformers/trainer.py", line 2360, in _maybe_log_save_evaluate
    self._save_checkpoint(model, trial, metrics=metrics)
  File "/root/miniconda3/envs/py3.9/lib/python3.9/site-packages/transformers/trainer.py", line 2417, in _save_checkpoint
    self.save_model(output_dir, _internal_call=True)
  File "/root/miniconda3/envs/py3.9/lib/python3.9/site-packages/transformers/trainer.py", line 2918, in save_model
    self._save(output_dir)
  File "/root/miniconda3/envs/py3.9/lib/python3.9/site-packages/transformers/trainer.py", line 2982, in _save
    torch.save(self.args, os.path.join(output_dir, TRAINING_ARGS_NAME))
  File "/root/miniconda3/envs/py3.9/lib/python3.9/site-packages/torch/serialization.py", line 440, in save
    with _open_zipfile_writer(f) as opened_zipfile:
  File "/root/miniconda3/envs/py3.9/lib/python3.9/site-packages/torch/serialization.py", line 315, in _open_zipfile_writer
    return container(name_or_buffer)
  File "/root/miniconda3/envs/py3.9/lib/python3.9/site-packages/torch/serialization.py", line 288, in __init__
    super().__init__(torch._C.PyTorchFileWriter(str(name)))
RuntimeError: Parent directory ./qlora-out/checkpoint-170 does not exist.
wandb: Waiting for W&B process to finish... (failed 1). Press Control-C to abort syncing.
wandb: 
wandb: Run history:
wandb:               eval/loss █▅▄▃▃▃▂▂▂▂▂▂▂▂▂▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
wandb:            eval/runtime ▁▆▅█▅█▅█▅█▅█▅▇▆█▅▇▅█▅█▅▇▅█▅█▅█▅█▅█
wandb: eval/samples_per_second █▂▄▁▄▁▄▁▄▁▄▁▄▁▃▁▄▁▄▁▄▁▄▁▄▁▄▁▄▁▄▁▄▁
wandb:   eval/steps_per_second █▃▄▁▄▁▄▁▄▁▄▁▅▁▄▁▅▁▄▁▄▁▄▁▄▁▄▁▄▁▄▁▄▁
wandb:             train/epoch ▁▁▁▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▄▄▅▅▅▅▆▆▆▆▆▆▇▇▇▇▇████
wandb:       train/global_step ▁▁▁▁▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇███
wandb:     train/learning_rate ▁▅██████████████████████████████████████
wandb:              train/loss █▇▇▄▅▄▃▅▃▅▃▂▃▃▂▃▃▃▃▃▁▃▂▂▃▁▃▃▃▃▁▃▃▃▃▃▄▁▃▁
wandb: 
wandb: Run summary:
wandb:               eval/loss 0.5186
wandb:            eval/runtime 30.2925
wandb: eval/samples_per_second 2.476
wandb:   eval/steps_per_second 0.627
wandb:             train/epoch 0.18
wandb:       train/global_step 170
wandb:     train/learning_rate 0.0002
wandb:              train/loss 0.4315
wandb: 
wandb: 🚀 View run polar-water-20 at: https://wandb.ai/utensil/falcon-qlora/runs/i9jjo2rl
wandb: Synced 6 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)
wandb: Find logs at: ./wandb/run-20230602_040215-i9jjo2rl/logs
Traceback (most recent call last):
  File "/root/miniconda3/envs/py3.9/bin/accelerate", line 8, in <module>
    sys.exit(main())
  File "/root/miniconda3/envs/py3.9/lib/python3.9/site-packages/accelerate/commands/accelerate_cli.py", line 45, in main
    args.func(args)
  File "/root/miniconda3/envs/py3.9/lib/python3.9/site-packages/accelerate/commands/launch.py", line 928, in launch_command
    simple_launcher(args)
  File "/root/miniconda3/envs/py3.9/lib/python3.9/site-packages/accelerate/commands/launch.py", line 588, in simple_launcher
    raise subprocess.CalledProcessError(returncode=process.returncode, cmd=cmd)
subprocess.CalledProcessError: Command '['/root/miniconda3/envs/py3.9/bin/python3', 'scripts/finetune.py', 'examples/falcon/qlora.yml']' returned non-zero exit status 1.

/

/workspace

Cloning into 'axolotl-update'...
remote: Enumerating objects: 2484, done.
remote: Counting objects: 100% (1234/1234), done.
remote: Compressing objects: 100% (356/356), done.
remote: Total 2484 (delta 907), reused 1082 (delta 812), pack-reused 1250
Receiving objects: 100% (2484/2484), 1.30 MiB | 3.02 MiB/s, done.
Resolving deltas: 100% (1539/1539), done.

/workspace/axolotl

On branch main
Your branch is up to date with 'origin/main'.

Changes not staged for commit:
  (use "git add <file>..." to update what will be committed)
  (use "git restore <file>..." to discard changes in working directory)
	modified:   README.md
	modified:   configs/cerebras_1_3B_alpaca.yml
	modified:   configs/galactica_1_3B.yml
	modified:   configs/gpt_neox_20b.yml
	modified:   configs/llama_13B_alpaca.yml
	modified:   configs/llama_65B_alpaca.yml
	modified:   configs/llama_7B_4bit.yml
	modified:   configs/llama_7B_alpaca.yml
	modified:   configs/llama_7B_jeopardy.yml
	modified:   configs/pythia_1_2B_alpaca.yml
	modified:   configs/quickstart.yml
	modified:   configs/sample.yml
	modified:   configs/stability_3b.yml
	modified:   configs/vicuna_13B_4bit_reflect.yml
	modified:   docker/Dockerfile-base
	modified:   examples/gptq-lora-7b/config.yml
	modified:   examples/mpt-7b/config.yml
	modified:   requirements.txt
	modified:   scripts/finetune.py
	modified:   scripts/runpod-entrypoint.sh
	modified:   src/axolotl/datasets.py
	modified:   src/axolotl/flash_attn.py
	modified:   src/axolotl/utils/data.py
	modified:   src/axolotl/utils/models.py
	modified:   src/axolotl/utils/validation.py
	modified:   tests/test_prompt_tokenizers.py
	modified:   tests/test_validation.py

Untracked files:
  (use "git add <file>..." to include in what will be committed)
	examples/falcon/lora.yml
	examples/falcon/qlora.yml
	examples/huggyllama/
	examples/openllama/
	image/axolotl-badge-web.png
	src/axolotl/monkeypatch/
	tests/fixtures/alpaca/
	tests/test_packed_dataset.py

no changes added to commit (use "git add" and/or "git commit -a")

Obtaining file:///workspace/axolotl
  Preparing metadata (setup.py) ... done
Collecting transformers@ git+https://github.com/huggingface/transformers.git (from axolotl==0.1)
  Cloning https://github.com/huggingface/transformers.git to /tmp/pip-install-w8_gs79m/transformers_d7bb90e3e98d4c5c855b5488e6abe26d
  Running command git clone --filter=blob:none --quiet https://github.com/huggingface/transformers.git /tmp/pip-install-w8_gs79m/transformers_d7bb90e3e98d4c5c855b5488e6abe26d
  Resolved https://github.com/huggingface/transformers.git to commit f49a3453caa6fe606bb31c571423f72264152fce
  Installing build dependencies ... done
  Getting requirements to build wheel ... done
  Preparing metadata (pyproject.toml) ... done
Requirement already satisfied: bitsandbytes>=0.39.0 in /root/miniconda3/envs/py3.9/lib/python3.9/site-packages/bitsandbytes-0.39.0-py3.9.egg (from axolotl==0.1) (0.39.0)
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Requirement already satisfied: mypy-extensions>=0.3.0 in /root/miniconda3/envs/py3.9/lib/python3.9/site-packages (from typing-inspect->pyre-extensions==0.0.29->xformers->axolotl==0.1) (1.0.0)
Installing collected packages: axolotl
  Attempting uninstall: axolotl
    Found existing installation: axolotl 0.1
    Uninstalling axolotl-0.1:
      Successfully uninstalled axolotl-0.1
  Running setup.py develop for axolotl
Successfully installed axolotl-0.1
WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv