{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# [Finetuning falcon-40b with Axolotl+QLoRA](https://wandb.ai/utensil/falcon-qlora/reports/Finetuning-falcon-7b-with-Axolotl-QLoRA--Vmlldzo0NTIzNTMw)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
""
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "zJvyUmZdktu0",
"outputId": "25275dd8-4787-4b77-f7bf-4f2bbe66f0b8"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/workspace/axolotl\n"
]
}
],
"source": [
"%cd /workspace/axolotl"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "mC48y25Lkqa5",
"outputId": "2757a3c8-3790-4fd3-be39-03be8f533b35"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Setting ds_accelerator to cuda (auto detect)\n",
"accelerate configuration saved at /root/.cache/huggingface/accelerate/default_config.yaml\n"
]
}
],
"source": [
"!accelerate config --config_file configs/accelerate/default_config.yaml default"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## #1 40b 4*2"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"base_model: tiiuae/falcon-40b\n",
"base_model_config: tiiuae/falcon-40b\n",
"trust_remote_code: true\n",
"model_type: AutoModelForCausalLM\n",
"tokenizer_type: AutoTokenizer\n",
"load_in_8bit: false\n",
"load_in_4bit: true\n",
"gptq: false\n",
"strict: false\n",
"push_dataset_to_hub:\n",
"datasets:\n",
" - path: QingyiSi/Alpaca-CoT\n",
" data_files:\n",
" - Chain-of-Thought/formatted_cot_data/gsm8k_train.json\n",
" type: \"alpaca:chat\"\n",
"dataset_prepared_path: last_run_prepared\n",
"val_set_size: 0.01\n",
"adapter: qlora\n",
"lora_model_dir:\n",
"sequence_len: 2048\n",
"max_packed_sequence_len:\n",
"lora_r: 64\n",
"lora_alpha: 16\n",
"lora_dropout: 0.05\n",
"lora_target_modules:\n",
"lora_target_linear: true\n",
"lora_fan_in_fan_out:\n",
"wandb_project: falcon-qlora\n",
"wandb_watch:\n",
"wandb_run_id:\n",
"wandb_log_model:\n",
"output_dir: ./qlora-out\n",
"micro_batch_size: 4\n",
"gradient_accumulation_steps: 2\n",
"num_epochs: 3\n",
"optimizer: paged_adamw_32bit\n",
"torchdistx_path:\n",
"lr_scheduler: cosine\n",
"learning_rate: 0.0002\n",
"train_on_inputs: false\n",
"group_by_length: false\n",
"bf16: true\n",
"fp16: false\n",
"tf32: true\n",
"gradient_checkpointing: true\n",
"# stop training after this many evaluation losses have increased in a row\n",
"# https://huggingface.co/transformers/v4.2.2/_modules/transformers/trainer_callback.html#EarlyStoppingCallback\n",
"early_stopping_patience: 3\n",
"resume_from_checkpoint:\n",
"auto_resume_from_checkpoints: true\n",
"local_rank:\n",
"logging_steps: 1\n",
"xformers_attention: true\n",
"flash_attention:\n",
"gptq_groupsize:\n",
"gptq_model_v1:\n",
"warmup_steps: 10\n",
"eval_steps: 5\n",
"save_steps: 10\n",
"debug:\n",
"deepspeed:\n",
"weight_decay: 0.000001\n",
"fsdp:\n",
"fsdp_config:\n",
"special_tokens:\n",
" pad_token: \"<|endoftext|>\"\n",
" bos_token: \">>ABSTRACT<<\"\n",
" eos_token: \"<|endoftext|>\""
]
}
],
"source": [
"!cat examples/falcon/qlora.yml"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"id": "jICMPJuomFsx",
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Setting ds_accelerator to cuda (auto detect)\n",
"\n",
"===================================BUG REPORT===================================\n",
"Welcome to bitsandbytes. For bug reports, please run\n",
"\n",
"python -m bitsandbytes\n",
"\n",
" and submit this information together with your error trace to: https://github.com/TimDettmers/bitsandbytes/issues\n",
"================================================================================\n",
"bin /root/miniconda3/envs/py3.9/lib/python3.9/site-packages/bitsandbytes-0.39.0-py3.9.egg/bitsandbytes/libbitsandbytes_cuda118.so\n",
"/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')}\n",
" warn(msg)\n",
"/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...\n",
" warn(msg)\n",
"/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')}\n",
" warn(msg)\n",
"/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')}\n",
" warn(msg)\n",
"CUDA_SETUP: WARNING! libcudart.so not found in any environmental path. Searching in backup paths...\n",
"/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.\n",
"Either way, this might cause trouble in the future:\n",
"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.\n",
" warn(msg)\n",
"CUDA SETUP: CUDA runtime path found: /usr/local/cuda/lib64/libcudart.so\n",
"CUDA SETUP: Highest compute capability among GPUs detected: 8.6\n",
"CUDA SETUP: Detected CUDA version 118\n",
"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...\n",
"Setting ds_accelerator to cuda (auto detect)\n",
"WARNING:root:`trust_remote_code` is set to true. Please make sure that you reviewed the remote code/model.\n",
"INFO:root:loading tokenizer... tiiuae/falcon-40b\n",
"Downloading (…)okenizer_config.json: 100%|█████| 175/175 [00:00<00:00, 23.9kB/s]\n",
"Downloading (…)/main/tokenizer.json: 100%|█| 2.73M/2.73M [00:00<00:00, 66.6MB/s]\n",
"Downloading (…)cial_tokens_map.json: 100%|██████| 281/281 [00:00<00:00, 382kB/s]\n",
"Using bos_token, but it is not set yet.\n",
"Using pad_token, but it is not set yet.\n",
"Using unk_token, but it is not set yet.\n",
"INFO:root:Unable to find prepared dataset in last_run_prepared/31a4e867d804a957707db033c9abcd13\n",
"INFO:root:Loading raw datasets...\n",
"Downloading readme: 100%|██████████████████| 8.26k/8.26k [00:00<00:00, 16.3MB/s]\n",
"Downloading and preparing dataset json/QingyiSi--Alpaca-CoT to /root/.cache/huggingface/datasets/QingyiSi___json/QingyiSi--Alpaca-CoT-2953efcfeb19f105/0.0.0/e347ab1c932092252e717ff3f949105a4dd28b27e842dd53157d2f72e276c2e4...\n",
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"Downloading data: 100%|████████████████████| 4.45M/4.45M [00:00<00:00, 29.0MB/s]\u001b[A\n",
"Downloading data files: 100%|█████████████████████| 1/1 [00:00<00:00, 2.39it/s]\n",
"Extracting data files: 100%|████████████████████| 1/1 [00:00<00:00, 2246.55it/s]\n",
"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.\n",
"100%|████████████████████████████████████████████| 1/1 [00:00<00:00, 805.20it/s]\n",
"INFO:root:tokenizing, merging, and shuffling master dataset\n",
"INFO:root:Saving merged prepared dataset to disk... last_run_prepared/31a4e867d804a957707db033c9abcd13\n",
"INFO:root:loading model and peft_config... \n",
"Downloading (…)lve/main/config.json: 100%|██████| 941/941 [00:00<00:00, 899kB/s]\n",
"Downloading (…)/configuration_RW.py: 100%|█| 2.51k/2.51k [00:00<00:00, 3.30MB/s]\n",
"A new version of the following files was downloaded from https://huggingface.co/tiiuae/falcon-40b:\n",
"- configuration_RW.py\n",
". 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.\n",
"Downloading (…)main/modelling_RW.py: 100%|█| 47.1k/47.1k [00:00<00:00, 46.3MB/s]\n",
"A new version of the following files was downloaded from https://huggingface.co/tiiuae/falcon-40b:\n",
"- modelling_RW.py\n",
". 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.\n",
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"INFO:root:converting PEFT model w/ prepare_model_for_int8_training\n",
"/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.\n",
" warnings.warn(\n",
"INFO:root:found linear modules: ['query_key_value', 'dense_h_to_4h', 'dense_4h_to_h', 'dense']\n",
"trainable params: 444334080 || all params: 21363310592 || trainable%: 2.0798933671188187\n",
"INFO:root:Compiling torch model\n",
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"To disable this warning, you can either:\n",
"\t- Avoid using `tokenizers` before the fork if possible\n",
"\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n",
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"\t- Avoid using `tokenizers` before the fork if possible\n",
"\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n",
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"\t- Avoid using `tokenizers` before the fork if possible\n",
"\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n",
"\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mutensil\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n",
"huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
"To disable this warning, you can either:\n",
"\t- Avoid using `tokenizers` before the fork if possible\n",
"\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n",
"\u001b[34m\u001b[1mwandb\u001b[0m: Tracking run with wandb version 0.15.3\n",
"\u001b[34m\u001b[1mwandb\u001b[0m: Run data is saved locally in \u001b[35m\u001b[1m/workspace/axolotl/wandb/run-20230602_040215-i9jjo2rl\u001b[0m\n",
"\u001b[34m\u001b[1mwandb\u001b[0m: Run \u001b[1m`wandb offline`\u001b[0m to turn off syncing.\n",
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"\u001b[34m\u001b[1mwandb\u001b[0m: ⭐️ View project at \u001b[34m\u001b[4mhttps://wandb.ai/utensil/falcon-qlora\u001b[0m\n",
"\u001b[34m\u001b[1mwandb\u001b[0m: 🚀 View run at \u001b[34m\u001b[4mhttps://wandb.ai/utensil/falcon-qlora/runs/i9jjo2rl\u001b[0m\n",
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" 58%|████████████████████████▉ | 11/19 [00:15<00:11, 1.48s/it]\u001b[A\n",
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" 95%|████████████████████████████████████████▋ | 18/19 [00:27<00:01, 1.56s/it]\u001b[A\n",
" \u001b[A\n",
"\u001b[A{'eval_loss': 0.6910794973373413, 'eval_runtime': 30.1357, 'eval_samples_per_second': 2.489, 'eval_steps_per_second': 0.63, 'epoch': 0.02}\n",
" 1%|▏ | 15/2775 [04:44<11:51:49, 15.47s/it]\n",
"100%|███████████████████████████████████████████| 19/19 [00:28<00:00, 1.52s/it]\u001b[A\n",
"{'loss': 0.6445, 'learning_rate': 0.00019999767629871982, 'epoch': 0.02} \u001b[A\n",
"{'loss': 0.7054, 'learning_rate': 0.00019999683718879195, 'epoch': 0.02} \n",
"{'loss': 0.6997, 'learning_rate': 0.00019999586898794544, 'epoch': 0.02} \n",
"{'loss': 0.6207, 'learning_rate': 0.00019999477169743024, 'epoch': 0.02} \n",
"{'loss': 0.4718, 'learning_rate': 0.00019999354531866285, 'epoch': 0.02} \n",
" 1%|▎ | 20/2775 [05:38<10:41:51, 13.98s/it]\n",
" 0%| | 0/19 [00:00, ?it/s]\u001b[A\n",
" 11%|████▋ | 2/19 [00:01<00:15, 1.06it/s]\u001b[A\n",
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" 84%|████████████████████████████████████▏ | 16/19 [00:24<00:05, 1.68s/it]\u001b[A\n",
" 89%|██████████████████████████████████████▍ | 17/19 [00:25<00:03, 1.61s/it]\u001b[A\n",
" 95%|████████████████████████████████████████▋ | 18/19 [00:27<00:01, 1.57s/it]\u001b[A\n",
" \u001b[A\n",
"\u001b[A{'eval_loss': 0.6478360295295715, 'eval_runtime': 30.2967, 'eval_samples_per_second': 2.476, 'eval_steps_per_second': 0.627, 'epoch': 0.02}\n",
" 1%|▎ | 20/2775 [06:09<10:41:51, 13.98s/it]\n",
"100%|███████████████████████████████████████████| 19/19 [00:28<00:00, 1.53s/it]\u001b[A\n",
"{'loss': 0.5887, 'learning_rate': 0.00019999218985322653, 'epoch': 0.02} \u001b[A\n",
"{'loss': 0.6598, 'learning_rate': 0.00019999070530287103, 'epoch': 0.02} \n",
"{'loss': 0.6178, 'learning_rate': 0.0001999890916695129, 'epoch': 0.02} \n",
"{'loss': 0.5742, 'learning_rate': 0.00019998734895523525, 'epoch': 0.03} \n",
"{'loss': 0.7448, 'learning_rate': 0.0001999854771622878, 'epoch': 0.03} \n",
" 1%|▎ | 25/2775 [07:33<11:54:13, 15.58s/it]\n",
" 0%| | 0/19 [00:00, ?it/s]\u001b[A\n",
" 11%|████▋ | 2/19 [00:01<00:15, 1.07it/s]\u001b[A\n",
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" \u001b[A\n",
"\u001b[A{'eval_loss': 0.6257006525993347, 'eval_runtime': 30.1529, 'eval_samples_per_second': 2.487, 'eval_steps_per_second': 0.63, 'epoch': 0.03}\n",
" 1%|▎ | 25/2775 [08:03<11:54:13, 15.58s/it]\n",
"100%|███████████████████████████████████████████| 19/19 [00:28<00:00, 1.52s/it]\u001b[A\n",
"{'loss': 0.5813, 'learning_rate': 0.000199983476293087, 'epoch': 0.03} \u001b[A\n",
"{'loss': 0.6013, 'learning_rate': 0.0001999813463502158, 'epoch': 0.03} \n",
"{'loss': 0.5715, 'learning_rate': 0.00019997908733642388, 'epoch': 0.03} \n",
"{'loss': 0.5183, 'learning_rate': 0.00019997669925462755, 'epoch': 0.03} \n",
"{'loss': 0.5113, 'learning_rate': 0.00019997418210790965, 'epoch': 0.03} \n",
" 1%|▍ | 30/2775 [08:56<10:14:57, 13.44s/it]\n",
" 0%| | 0/19 [00:00, ?it/s]\u001b[A\n",
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" \u001b[A\n",
"\u001b[A{'eval_loss': 0.6190676689147949, 'eval_runtime': 30.3009, 'eval_samples_per_second': 2.475, 'eval_steps_per_second': 0.627, 'epoch': 0.03}\n",
" 1%|▍ | 30/2775 [09:27<10:14:57, 13.44s/it]\n",
"100%|███████████████████████████████████████████| 19/19 [00:28<00:00, 1.53s/it]\u001b[A\n",
"{'loss': 0.6729, 'learning_rate': 0.00019997153589951973, 'epoch': 0.03} \u001b[A\n",
"{'loss': 0.7161, 'learning_rate': 0.0001999687606328739, 'epoch': 0.03} \n",
"{'loss': 0.567, 'learning_rate': 0.00019996585631155492, 'epoch': 0.04} \n",
"{'loss': 0.5651, 'learning_rate': 0.00019996282293931203, 'epoch': 0.04} \n",
"{'loss': 0.5905, 'learning_rate': 0.00019995966052006127, 'epoch': 0.04} \n",
" 1%|▍ | 35/2775 [10:52<11:54:08, 15.64s/it]\n",
" 0%| | 0/19 [00:00, ?it/s]\u001b[A\n",
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" \u001b[A\n",
"\u001b[A{'eval_loss': 0.6032792925834656, 'eval_runtime': 30.1573, 'eval_samples_per_second': 2.487, 'eval_steps_per_second': 0.63, 'epoch': 0.04}\n",
" 1%|▍ | 35/2775 [11:22<11:54:08, 15.64s/it]\n",
"100%|███████████████████████████████████████████| 19/19 [00:28<00:00, 1.52s/it]\u001b[A\n",
"{'loss': 0.5764, 'learning_rate': 0.00019995636905788508, 'epoch': 0.04} \u001b[A\n",
"{'loss': 0.6384, 'learning_rate': 0.00019995294855703266, 'epoch': 0.04} \n",
"{'loss': 0.4429, 'learning_rate': 0.00019994939902191964, 'epoch': 0.04} \n",
"{'loss': 0.5828, 'learning_rate': 0.0001999457204571283, 'epoch': 0.04} \n",
"{'loss': 0.7152, 'learning_rate': 0.0001999419128674075, 'epoch': 0.04} \n",
" 1%|▌ | 40/2775 [12:14<10:12:21, 13.43s/it]\n",
" 0%| | 0/19 [00:00, ?it/s]\u001b[A\n",
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" \u001b[A\n",
"\u001b[A{'eval_loss': 0.5895186066627502, 'eval_runtime': 30.3072, 'eval_samples_per_second': 2.475, 'eval_steps_per_second': 0.627, 'epoch': 0.04}\n",
" 1%|▌ | 40/2775 [12:44<10:12:21, 13.43s/it]\n",
"100%|███████████████████████████████████████████| 19/19 [00:28<00:00, 1.53s/it]\u001b[A\n",
"{'loss': 0.5443, 'learning_rate': 0.00019993797625767262, 'epoch': 0.04} \u001b[A\n",
"{'loss': 0.5877, 'learning_rate': 0.00019993391063300567, 'epoch': 0.05} \n",
"{'loss': 0.6032, 'learning_rate': 0.00019992971599865514, 'epoch': 0.05} \n",
"{'loss': 0.5714, 'learning_rate': 0.00019992539236003614, 'epoch': 0.05} \n",
"{'loss': 0.5354, 'learning_rate': 0.00019992093972273018, 'epoch': 0.05} \n",
" 2%|▌ | 45/2775 [14:11<11:44:34, 15.49s/it]\n",
" 0%| | 0/19 [00:00, ?it/s]\u001b[A\n",
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" \u001b[A\n",
"\u001b[A{'eval_loss': 0.5860755443572998, 'eval_runtime': 30.1522, 'eval_samples_per_second': 2.487, 'eval_steps_per_second': 0.63, 'epoch': 0.05}\n",
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"100%|███████████████████████████████████████████| 19/19 [00:28<00:00, 1.52s/it]\u001b[A\n",
"{'loss': 0.5667, 'learning_rate': 0.00019991635809248548, 'epoch': 0.05} \u001b[A\n",
"{'loss': 0.4531, 'learning_rate': 0.00019991164747521663, 'epoch': 0.05} \n",
"{'loss': 0.4917, 'learning_rate': 0.00019990680787700487, 'epoch': 0.05} \n",
"{'loss': 0.6557, 'learning_rate': 0.00019990183930409782, 'epoch': 0.05} \n",
"{'loss': 0.5971, 'learning_rate': 0.00019989674176290967, 'epoch': 0.05} \n",
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" \u001b[A\n",
"\u001b[A{'eval_loss': 0.5772437453269958, 'eval_runtime': 30.3147, 'eval_samples_per_second': 2.474, 'eval_steps_per_second': 0.627, 'epoch': 0.05}\n",
" 2%|▋ | 50/2775 [16:04<10:24:47, 13.76s/it]\n",
"100%|███████████████████████████████████████████| 19/19 [00:28<00:00, 1.53s/it]\u001b[A\n",
"{'loss': 0.6819, 'learning_rate': 0.0001998915152600211, 'epoch': 0.06} \u001b[A\n",
"{'loss': 0.5369, 'learning_rate': 0.00019988615980217925, 'epoch': 0.06} \n",
"{'loss': 0.4884, 'learning_rate': 0.0001998806753962978, 'epoch': 0.06} \n",
"{'loss': 0.5241, 'learning_rate': 0.00019987506204945677, 'epoch': 0.06} \n",
"{'loss': 0.5141, 'learning_rate': 0.00019986931976890277, 'epoch': 0.06} \n",
" 2%|▊ | 55/2775 [17:30<11:48:08, 15.62s/it]\n",
" 0%| | 0/19 [00:00, ?it/s]\u001b[A\n",
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" \u001b[A\n",
"\u001b[A{'eval_loss': 0.5703625679016113, 'eval_runtime': 30.161, 'eval_samples_per_second': 2.487, 'eval_steps_per_second': 0.63, 'epoch': 0.06}\n",
" 2%|▊ | 55/2775 [18:01<11:48:08, 15.62s/it]\n",
"100%|███████████████████████████████████████████| 19/19 [00:28<00:00, 1.52s/it]\u001b[A\n",
"{'loss': 0.5122, 'learning_rate': 0.00019986344856204877, 'epoch': 0.06} \u001b[A\n",
"{'loss': 0.5975, 'learning_rate': 0.00019985744843647421, 'epoch': 0.06} \n",
"{'loss': 0.5577, 'learning_rate': 0.00019985131939992493, 'epoch': 0.06} \n",
"{'loss': 0.5678, 'learning_rate': 0.00019984506146031325, 'epoch': 0.06} \n",
"{'loss': 0.4084, 'learning_rate': 0.00019983867462571784, 'epoch': 0.06} \n",
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" 0%| | 0/19 [00:00, ?it/s]\u001b[A\n",
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" \u001b[A\n",
"\u001b[A{'eval_loss': 0.565077006816864, 'eval_runtime': 30.3, 'eval_samples_per_second': 2.475, 'eval_steps_per_second': 0.627, 'epoch': 0.06}\n",
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"100%|███████████████████████████████████████████| 19/19 [00:28<00:00, 1.53s/it]\u001b[A\n",
"{'loss': 0.518, 'learning_rate': 0.00019983215890438379, 'epoch': 0.07} \u001b[A\n",
"{'loss': 0.5234, 'learning_rate': 0.00019982551430472255, 'epoch': 0.07} \n",
"{'loss': 0.5344, 'learning_rate': 0.00019981874083531198, 'epoch': 0.07} \n",
"{'loss': 0.5577, 'learning_rate': 0.00019981183850489628, 'epoch': 0.07} \n",
"{'loss': 0.4016, 'learning_rate': 0.000199804807322386, 'epoch': 0.07} \n",
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" 0%| | 0/19 [00:00, ?it/s]\u001b[A\n",
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" \u001b[A\n",
"\u001b[A{'eval_loss': 0.5614088177680969, 'eval_runtime': 30.1239, 'eval_samples_per_second': 2.49, 'eval_steps_per_second': 0.631, 'epoch': 0.07}\n",
" 2%|▉ | 65/2775 [21:14<11:19:03, 15.03s/it]\n",
"100%|███████████████████████████████████████████| 19/19 [00:28<00:00, 1.52s/it]\u001b[A\n",
"{'loss': 0.5725, 'learning_rate': 0.00019979764729685813, 'epoch': 0.07} \u001b[A\n",
"{'loss': 0.5074, 'learning_rate': 0.00019979035843755582, 'epoch': 0.07} \n",
"{'loss': 0.5672, 'learning_rate': 0.00019978294075388863, 'epoch': 0.07} \n",
"{'loss': 0.5588, 'learning_rate': 0.00019977539425543244, 'epoch': 0.07} \n",
"{'loss': 0.5385, 'learning_rate': 0.0001997677189519294, 'epoch': 0.08} \n",
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" 0%| | 0/19 [00:00, ?it/s]\u001b[A\n",
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" \u001b[A\n",
"\u001b[A{'eval_loss': 0.559436023235321, 'eval_runtime': 30.2884, 'eval_samples_per_second': 2.476, 'eval_steps_per_second': 0.627, 'epoch': 0.08}\n",
" 3%|▉ | 70/2775 [22:37<10:02:03, 13.35s/it]\n",
"100%|███████████████████████████████████████████| 19/19 [00:28<00:00, 1.53s/it]\u001b[A\n",
"{'loss': 0.5286, 'learning_rate': 0.00019975991485328794, 'epoch': 0.08} \u001b[A\n",
"{'loss': 0.4962, 'learning_rate': 0.0001997519819695828, 'epoch': 0.08} \n",
"{'loss': 0.7865, 'learning_rate': 0.00019974392031105482, 'epoch': 0.08} \n",
"{'loss': 0.545, 'learning_rate': 0.00019973572988811133, 'epoch': 0.08} \n",
"{'loss': 0.5515, 'learning_rate': 0.00019972741071132567, 'epoch': 0.08} \n",
" 3%|█ | 75/2775 [23:57<11:21:50, 15.15s/it]\n",
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" \u001b[A\n",
"\u001b[A{'eval_loss': 0.5549315214157104, 'eval_runtime': 30.1758, 'eval_samples_per_second': 2.485, 'eval_steps_per_second': 0.63, 'epoch': 0.08}\n",
" 3%|█ | 75/2775 [24:27<11:21:50, 15.15s/it]\n",
"100%|███████████████████████████████████████████| 19/19 [00:28<00:00, 1.53s/it]\u001b[A\n",
"{'loss': 0.524, 'learning_rate': 0.00019971896279143755, 'epoch': 0.08} \u001b[A\n",
"{'loss': 0.5074, 'learning_rate': 0.00019971038613935272, 'epoch': 0.08} \n",
"{'loss': 0.5813, 'learning_rate': 0.00019970168076614332, 'epoch': 0.08} \n",
"{'loss': 0.6796, 'learning_rate': 0.0001996928466830475, 'epoch': 0.09} \n",
"{'loss': 0.5197, 'learning_rate': 0.0001996838839014696, 'epoch': 0.09} \n",
" 3%|█ | 80/2775 [25:21<10:13:28, 13.66s/it]\n",
" 0%| | 0/19 [00:00, ?it/s]\u001b[A\n",
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" \u001b[A\n",
"\u001b[A{'eval_loss': 0.5519441962242126, 'eval_runtime': 30.305, 'eval_samples_per_second': 2.475, 'eval_steps_per_second': 0.627, 'epoch': 0.09}\n",
" 3%|█ | 80/2775 [25:51<10:13:28, 13.66s/it]\n",
"100%|███████████████████████████████████████████| 19/19 [00:28<00:00, 1.53s/it]\u001b[A\n",
"{'loss': 0.5639, 'learning_rate': 0.00019967479243298013, 'epoch': 0.09} \u001b[A\n",
"{'loss': 0.5524, 'learning_rate': 0.00019966557228931576, 'epoch': 0.09} \n",
"{'loss': 0.5027, 'learning_rate': 0.00019965622348237916, 'epoch': 0.09} \n",
"{'loss': 0.5657, 'learning_rate': 0.00019964674602423926, 'epoch': 0.09} \n",
"{'loss': 0.5644, 'learning_rate': 0.00019963713992713093, 'epoch': 0.09} \n",
" 3%|█▏ | 85/2775 [27:17<11:38:44, 15.59s/it]\n",
" 0%| | 0/19 [00:00, ?it/s]\u001b[A\n",
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" \u001b[A\n",
"\u001b[A{'eval_loss': 0.5444000363349915, 'eval_runtime': 30.1321, 'eval_samples_per_second': 2.489, 'eval_steps_per_second': 0.631, 'epoch': 0.09}\n",
" 3%|█▏ | 85/2775 [27:47<11:38:44, 15.59s/it]\n",
"100%|███████████████████████████████████████████| 19/19 [00:28<00:00, 1.52s/it]\u001b[A\n",
"{'loss': 0.5254, 'learning_rate': 0.00019962740520345514, 'epoch': 0.09} \u001b[A\n",
"{'loss': 0.4614, 'learning_rate': 0.00019961754186577902, 'epoch': 0.09} \n",
"{'loss': 0.4148, 'learning_rate': 0.0001996075499268356, 'epoch': 0.1} \n",
"{'loss': 0.4824, 'learning_rate': 0.00019959742939952392, 'epoch': 0.1} \n",
"{'loss': 0.6157, 'learning_rate': 0.00019958718029690911, 'epoch': 0.1} \n",
" 3%|█▏ | 90/2775 [28:40<10:13:20, 13.71s/it]\n",
" 0%| | 0/19 [00:00, ?it/s]\u001b[A\n",
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" \u001b[A\n",
"\u001b[A{'eval_loss': 0.5450552701950073, 'eval_runtime': 30.2879, 'eval_samples_per_second': 2.476, 'eval_steps_per_second': 0.627, 'epoch': 0.1}\n",
" 3%|█▏ | 90/2775 [29:10<10:13:20, 13.71s/it]\n",
"100%|███████████████████████████████████████████| 19/19 [00:28<00:00, 1.53s/it]\u001b[A\n",
"{'loss': 0.5633, 'learning_rate': 0.00019957680263222232, 'epoch': 0.1} \u001b[A\n",
"{'loss': 0.5776, 'learning_rate': 0.0001995662964188605, 'epoch': 0.1} \n",
"{'loss': 0.5407, 'learning_rate': 0.0001995556616703867, 'epoch': 0.1} \n",
"{'loss': 0.6597, 'learning_rate': 0.0001995448984005298, 'epoch': 0.1} \n",
"{'loss': 0.4512, 'learning_rate': 0.00019953400662318468, 'epoch': 0.1} \n",
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" \u001b[A\n",
"\u001b[A{'eval_loss': 0.5443361401557922, 'eval_runtime': 30.1577, 'eval_samples_per_second': 2.487, 'eval_steps_per_second': 0.63, 'epoch': 0.1}\n",
" 3%|█▎ | 95/2775 [31:08<11:46:05, 15.81s/it]\n",
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"{'loss': 0.4589, 'learning_rate': 0.0001995229863524121, 'epoch': 0.1} \u001b[A\n",
"{'loss': 0.5606, 'learning_rate': 0.00019951183760243864, 'epoch': 0.1} \n",
"{'loss': 0.5095, 'learning_rate': 0.0001995005603876568, 'epoch': 0.11} \n",
"{'loss': 0.5886, 'learning_rate': 0.0001994891547226249, 'epoch': 0.11} \n",
"{'loss': 0.4725, 'learning_rate': 0.00019947762062206702, 'epoch': 0.11} \n",
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" \u001b[A\n",
"\u001b[A{'eval_loss': 0.5453822016716003, 'eval_runtime': 30.3033, 'eval_samples_per_second': 2.475, 'eval_steps_per_second': 0.627, 'epoch': 0.11}\n",
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"{'loss': 0.5597, 'learning_rate': 0.00019946595810087323, 'epoch': 0.11} \u001b[A\n",
"{'loss': 0.3998, 'learning_rate': 0.00019945416717409917, 'epoch': 0.11} \n",
"{'loss': 0.5013, 'learning_rate': 0.00019944224785696638, 'epoch': 0.11} \n",
"{'loss': 0.576, 'learning_rate': 0.00019943020016486207, 'epoch': 0.11} \n",
"{'loss': 0.6131, 'learning_rate': 0.0001994180241133392, 'epoch': 0.11} \n",
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" \u001b[A\n",
"\u001b[A{'eval_loss': 0.5433566570281982, 'eval_runtime': 30.1485, 'eval_samples_per_second': 2.488, 'eval_steps_per_second': 0.63, 'epoch': 0.11}\n",
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"{'loss': 0.6956, 'learning_rate': 0.0001994057197181165, 'epoch': 0.11} \u001b[A\n",
"{'loss': 0.6562, 'learning_rate': 0.0001993932869950783, 'epoch': 0.12} \n",
"{'loss': 0.4399, 'learning_rate': 0.00019938072596027462, 'epoch': 0.12} \n",
"{'loss': 0.4544, 'learning_rate': 0.0001993680366299211, 'epoch': 0.12} \n",
"{'loss': 0.4978, 'learning_rate': 0.0001993552190203991, 'epoch': 0.12} \n",
" 4%|█▌ | 110/2775 [35:18<9:49:28, 13.27s/it]\n",
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" \u001b[A\n",
"\u001b[A{'eval_loss': 0.5402812957763672, 'eval_runtime': 30.2983, 'eval_samples_per_second': 2.475, 'eval_steps_per_second': 0.627, 'epoch': 0.12}\n",
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"{'loss': 0.455, 'learning_rate': 0.00019934227314825546, 'epoch': 0.12} \u001b[A\n",
"{'loss': 0.4953, 'learning_rate': 0.00019932919903020265, 'epoch': 0.12} \n",
"{'loss': 0.4013, 'learning_rate': 0.00019931599668311875, 'epoch': 0.12} \n",
"{'loss': 0.5624, 'learning_rate': 0.00019930266612404728, 'epoch': 0.12} \n",
"{'loss': 0.5522, 'learning_rate': 0.00019928920737019733, 'epoch': 0.12} \n",
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" \u001b[A\n",
"\u001b[A{'eval_loss': 0.5382705926895142, 'eval_runtime': 30.1622, 'eval_samples_per_second': 2.487, 'eval_steps_per_second': 0.63, 'epoch': 0.12}\n",
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"100%|███████████████████████████████████████████| 19/19 [00:28<00:00, 1.53s/it]\u001b[A\n",
"{'loss': 0.5384, 'learning_rate': 0.00019927562043894353, 'epoch': 0.13} \u001b[A\n",
"{'loss': 0.5326, 'learning_rate': 0.00019926190534782587, 'epoch': 0.13} \n",
"{'loss': 0.57, 'learning_rate': 0.0001992480621145499, 'epoch': 0.13} \n",
"{'loss': 0.4594, 'learning_rate': 0.0001992340907569865, 'epoch': 0.13} \n",
"{'loss': 0.4871, 'learning_rate': 0.000199219991293172, 'epoch': 0.13} \n",
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" \u001b[A\n",
"\u001b[A{'eval_loss': 0.5348045229911804, 'eval_runtime': 30.2904, 'eval_samples_per_second': 2.476, 'eval_steps_per_second': 0.627, 'epoch': 0.13}\n",
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"{'loss': 0.4707, 'learning_rate': 0.0001992057637413081, 'epoch': 0.13} \u001b[A\n",
"{'loss': 0.6047, 'learning_rate': 0.0001991914081197619, 'epoch': 0.13} \n",
"{'loss': 0.5401, 'learning_rate': 0.00019917692444706574, 'epoch': 0.13} \n",
"{'loss': 0.4063, 'learning_rate': 0.0001991623127419173, 'epoch': 0.13} \n",
"{'loss': 0.4525, 'learning_rate': 0.00019914757302317963, 'epoch': 0.14} \n",
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" \u001b[A\n",
"\u001b[A{'eval_loss': 0.5333865284919739, 'eval_runtime': 30.1497, 'eval_samples_per_second': 2.488, 'eval_steps_per_second': 0.63, 'epoch': 0.14}\n",
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"{'loss': 0.5573, 'learning_rate': 0.00019913270530988095, 'epoch': 0.14} \u001b[A\n",
"{'loss': 0.5457, 'learning_rate': 0.00019911770962121472, 'epoch': 0.14} \n",
"{'loss': 0.4689, 'learning_rate': 0.00019910258597653958, 'epoch': 0.14} \n",
"{'loss': 0.5944, 'learning_rate': 0.0001990873343953795, 'epoch': 0.14} \n",
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" \u001b[A\n",
"\u001b[A{'eval_loss': 0.5304591655731201, 'eval_runtime': 30.2978, 'eval_samples_per_second': 2.475, 'eval_steps_per_second': 0.627, 'epoch': 0.14}\n",
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"{'loss': 0.46, 'learning_rate': 0.00019905644750252557, 'epoch': 0.14} \u001b[A\n",
"{'loss': 0.4777, 'learning_rate': 0.00019904081223070518, 'epoch': 0.14} \n",
"{'loss': 0.7315, 'learning_rate': 0.0001990250491021466, 'epoch': 0.14} \n",
"{'loss': 0.5294, 'learning_rate': 0.00019900915813719926, 'epoch': 0.14} \n",
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" \u001b[A\n",
"\u001b[A{'eval_loss': 0.5278797149658203, 'eval_runtime': 30.1614, 'eval_samples_per_second': 2.487, 'eval_steps_per_second': 0.63, 'epoch': 0.15}\n",
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"{'loss': 0.4517, 'learning_rate': 0.00019897699278036108, 'epoch': 0.15} \u001b[A\n",
"{'loss': 0.4934, 'learning_rate': 0.00019896071842999408, 'epoch': 0.15} \n",
"{'loss': 0.5481, 'learning_rate': 0.00019894431632628602, 'epoch': 0.15} \n",
"{'loss': 0.5066, 'learning_rate': 0.00019892778649041116, 'epoch': 0.15} \n",
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" \u001b[A\n",
"\u001b[A{'eval_loss': 0.5283600091934204, 'eval_runtime': 30.3056, 'eval_samples_per_second': 2.475, 'eval_steps_per_second': 0.627, 'epoch': 0.15}\n",
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"{'loss': 0.6013, 'learning_rate': 0.0001988943437076827, 'epoch': 0.15} \u001b[A\n",
"{'loss': 0.4899, 'learning_rate': 0.00019887743080400206, 'epoch': 0.15} \n",
"{'loss': 0.6892, 'learning_rate': 0.0001988603902545005, 'epoch': 0.15} \n",
"{'loss': 0.575, 'learning_rate': 0.00019884322208117653, 'epoch': 0.16} \n",
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" \u001b[A\n",
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" \u001b[ATraceback (most recent call last):\n",
" File \"/workspace/axolotl/scripts/finetune.py\", line 294, in \n",
" fire.Fire(train)\n",
" File \"/root/miniconda3/envs/py3.9/lib/python3.9/site-packages/fire/core.py\", line 141, in Fire\n",
" component_trace = _Fire(component, args, parsed_flag_args, context, name)\n",
" File \"/root/miniconda3/envs/py3.9/lib/python3.9/site-packages/fire/core.py\", line 475, in _Fire\n",
" component, remaining_args = _CallAndUpdateTrace(\n",
" File \"/root/miniconda3/envs/py3.9/lib/python3.9/site-packages/fire/core.py\", line 691, in _CallAndUpdateTrace\n",
" component = fn(*varargs, **kwargs)\n",
" File \"/workspace/axolotl/scripts/finetune.py\", line 281, in train\n",
" trainer.train(resume_from_checkpoint=resume_from_checkpoint)\n",
" File \"/root/miniconda3/envs/py3.9/lib/python3.9/site-packages/transformers/trainer.py\", line 1696, in train\n",
" return inner_training_loop(\n",
" File \"/root/miniconda3/envs/py3.9/lib/python3.9/site-packages/transformers/trainer.py\", line 2052, in _inner_training_loop\n",
" self._maybe_log_save_evaluate(tr_loss, model, trial, epoch, ignore_keys_for_eval)\n",
" File \"/root/miniconda3/envs/py3.9/lib/python3.9/site-packages/transformers/trainer.py\", line 2360, in _maybe_log_save_evaluate\n",
" self._save_checkpoint(model, trial, metrics=metrics)\n",
" File \"/root/miniconda3/envs/py3.9/lib/python3.9/site-packages/transformers/trainer.py\", line 2417, in _save_checkpoint\n",
" self.save_model(output_dir, _internal_call=True)\n",
" File \"/root/miniconda3/envs/py3.9/lib/python3.9/site-packages/transformers/trainer.py\", line 2918, in save_model\n",
" self._save(output_dir)\n",
" File \"/root/miniconda3/envs/py3.9/lib/python3.9/site-packages/transformers/trainer.py\", line 2982, in _save\n",
" torch.save(self.args, os.path.join(output_dir, TRAINING_ARGS_NAME))\n",
" File \"/root/miniconda3/envs/py3.9/lib/python3.9/site-packages/torch/serialization.py\", line 440, in save\n",
" with _open_zipfile_writer(f) as opened_zipfile:\n",
" File \"/root/miniconda3/envs/py3.9/lib/python3.9/site-packages/torch/serialization.py\", line 315, in _open_zipfile_writer\n",
" return container(name_or_buffer)\n",
" File \"/root/miniconda3/envs/py3.9/lib/python3.9/site-packages/torch/serialization.py\", line 288, in __init__\n",
" super().__init__(torch._C.PyTorchFileWriter(str(name)))\n",
"RuntimeError: Parent directory ./qlora-out/checkpoint-170 does not exist.\n",
"\u001b[34m\u001b[1mwandb\u001b[0m: Waiting for W&B process to finish... \u001b[31m(failed 1).\u001b[0m Press Control-C to abort syncing.\n",
"\u001b[34m\u001b[1mwandb\u001b[0m: \n",
"\u001b[34m\u001b[1mwandb\u001b[0m: Run history:\n",
"\u001b[34m\u001b[1mwandb\u001b[0m: eval/loss █▅▄▃▃▃▂▂▂▂▂▂▂▂▂▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁\n",
"\u001b[34m\u001b[1mwandb\u001b[0m: eval/runtime ▁▆▅█▅█▅█▅█▅█▅▇▆█▅▇▅█▅█▅▇▅█▅█▅█▅█▅█\n",
"\u001b[34m\u001b[1mwandb\u001b[0m: eval/samples_per_second █▂▄▁▄▁▄▁▄▁▄▁▄▁▃▁▄▁▄▁▄▁▄▁▄▁▄▁▄▁▄▁▄▁\n",
"\u001b[34m\u001b[1mwandb\u001b[0m: eval/steps_per_second █▃▄▁▄▁▄▁▄▁▄▁▅▁▄▁▅▁▄▁▄▁▄▁▄▁▄▁▄▁▄▁▄▁\n",
"\u001b[34m\u001b[1mwandb\u001b[0m: train/epoch ▁▁▁▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▄▄▅▅▅▅▆▆▆▆▆▆▇▇▇▇▇████\n",
"\u001b[34m\u001b[1mwandb\u001b[0m: train/global_step ▁▁▁▁▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇███\n",
"\u001b[34m\u001b[1mwandb\u001b[0m: train/learning_rate ▁▅██████████████████████████████████████\n",
"\u001b[34m\u001b[1mwandb\u001b[0m: train/loss █▇▇▄▅▄▃▅▃▅▃▂▃▃▂▃▃▃▃▃▁▃▂▂▃▁▃▃▃▃▁▃▃▃▃▃▄▁▃▁\n",
"\u001b[34m\u001b[1mwandb\u001b[0m: \n",
"\u001b[34m\u001b[1mwandb\u001b[0m: Run summary:\n",
"\u001b[34m\u001b[1mwandb\u001b[0m: eval/loss 0.5186\n",
"\u001b[34m\u001b[1mwandb\u001b[0m: eval/runtime 30.2925\n",
"\u001b[34m\u001b[1mwandb\u001b[0m: eval/samples_per_second 2.476\n",
"\u001b[34m\u001b[1mwandb\u001b[0m: eval/steps_per_second 0.627\n",
"\u001b[34m\u001b[1mwandb\u001b[0m: train/epoch 0.18\n",
"\u001b[34m\u001b[1mwandb\u001b[0m: train/global_step 170\n",
"\u001b[34m\u001b[1mwandb\u001b[0m: train/learning_rate 0.0002\n",
"\u001b[34m\u001b[1mwandb\u001b[0m: train/loss 0.4315\n",
"\u001b[34m\u001b[1mwandb\u001b[0m: \n",
"\u001b[34m\u001b[1mwandb\u001b[0m: 🚀 View run \u001b[33mpolar-water-20\u001b[0m at: \u001b[34m\u001b[4mhttps://wandb.ai/utensil/falcon-qlora/runs/i9jjo2rl\u001b[0m\n",
"\u001b[34m\u001b[1mwandb\u001b[0m: Synced 6 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)\n",
"\u001b[34m\u001b[1mwandb\u001b[0m: Find logs at: \u001b[35m\u001b[1m./wandb/run-20230602_040215-i9jjo2rl/logs\u001b[0m\n",
"Traceback (most recent call last):\n",
" File \"/root/miniconda3/envs/py3.9/bin/accelerate\", line 8, in \n",
" sys.exit(main())\n",
" File \"/root/miniconda3/envs/py3.9/lib/python3.9/site-packages/accelerate/commands/accelerate_cli.py\", line 45, in main\n",
" args.func(args)\n",
" File \"/root/miniconda3/envs/py3.9/lib/python3.9/site-packages/accelerate/commands/launch.py\", line 928, in launch_command\n",
" simple_launcher(args)\n",
" File \"/root/miniconda3/envs/py3.9/lib/python3.9/site-packages/accelerate/commands/launch.py\", line 588, in simple_launcher\n",
" raise subprocess.CalledProcessError(returncode=process.returncode, cmd=cmd)\n",
"subprocess.CalledProcessError: Command '['/root/miniconda3/envs/py3.9/bin/python3', 'scripts/finetune.py', 'examples/falcon/qlora.yml']' returned non-zero exit status 1.\n"
]
}
],
"source": [
"!accelerate launch scripts/finetune.py examples/falcon/qlora.yml"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## #2 \n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"!cat examples/falcon/qlora.yml"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "jICMPJuomFsx",
"scrolled": true
},
"outputs": [],
"source": [
"!accelerate launch scripts/finetune.py examples/falcon/qlora.yml"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## #3"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"!cat examples/falcon/qlora.yml"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"!accelerate launch scripts/finetune.py examples/falcon/qlora.yml"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## #4"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"!cat examples/falcon/qlora.yml"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"!accelerate launch scripts/finetune.py examples/falcon/qlora.yml"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Below are ad hoc cells handling issues during training"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"### Force release VRAM"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"# First interupt the kernel, wait a few seconds then run this to kill finetune to release VRAM\n",
"!ps aux|grep python|grep finetune|awk '{print $2}'|xargs kill"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"### Clean the finetuned model and all checkpoints"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"# Only run this to start over\n",
"!rm -rf ./qlora-out"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"### Zip the prepared dataset"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!apt install zip\n",
"!zip -r last_run_prepared.zip -xi last_run_prepared"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"### Monitoring GPU"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Run this in a seperate terminal\n",
"!nvitop -m full"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"### Fix DISK FULL"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/\n"
]
}
],
"source": [
"%cd /"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!du -d 2 -h|grep G"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!du -d 2 -h /root/.local"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"!rm -rf /root/.local/share/Trash/"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!rm -rf /root/.local/share/wandb/"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!rm -rf /root/.cache/wandb/"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"### Check who is using GPU"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"!apt install lsof"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"!lsof /dev/nvidia*"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"### Upload checkpoints to HF"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!cp -r /workspace/axolotl/qlora-out/checkpoint-120 /workspace/llm-playground/storage/falcon-qlora/"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!python /workspace/llm-playground/helper/storage.py -u"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"### Update axolotl"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/workspace\n"
]
}
],
"source": [
"%cd /workspace/"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Cloning into 'axolotl-update'...\n",
"remote: Enumerating objects: 2484, done.\u001b[K\n",
"remote: Counting objects: 100% (1234/1234), done.\u001b[K\n",
"remote: Compressing objects: 100% (356/356), done.\u001b[K\n",
"remote: Total 2484 (delta 907), reused 1082 (delta 812), pack-reused 1250\u001b[K\n",
"Receiving objects: 100% (2484/2484), 1.30 MiB | 3.02 MiB/s, done.\n",
"Resolving deltas: 100% (1539/1539), done.\n"
]
}
],
"source": [
"!git clone https://github.com/OpenAccess-AI-Collective/axolotl axolotl-update"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"!cp -r axolotl-update/* axolotl"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/workspace/axolotl\n"
]
}
],
"source": [
"%cd /workspace/axolotl"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"On branch main\n",
"Your branch is up to date with 'origin/main'.\n",
"\n",
"Changes not staged for commit:\n",
" (use \"git add ...\" to update what will be committed)\n",
" (use \"git restore ...\" to discard changes in working directory)\n",
"\t\u001b[31mmodified: README.md\u001b[m\n",
"\t\u001b[31mmodified: configs/cerebras_1_3B_alpaca.yml\u001b[m\n",
"\t\u001b[31mmodified: configs/galactica_1_3B.yml\u001b[m\n",
"\t\u001b[31mmodified: configs/gpt_neox_20b.yml\u001b[m\n",
"\t\u001b[31mmodified: configs/llama_13B_alpaca.yml\u001b[m\n",
"\t\u001b[31mmodified: configs/llama_65B_alpaca.yml\u001b[m\n",
"\t\u001b[31mmodified: configs/llama_7B_4bit.yml\u001b[m\n",
"\t\u001b[31mmodified: configs/llama_7B_alpaca.yml\u001b[m\n",
"\t\u001b[31mmodified: configs/llama_7B_jeopardy.yml\u001b[m\n",
"\t\u001b[31mmodified: configs/pythia_1_2B_alpaca.yml\u001b[m\n",
"\t\u001b[31mmodified: configs/quickstart.yml\u001b[m\n",
"\t\u001b[31mmodified: configs/sample.yml\u001b[m\n",
"\t\u001b[31mmodified: configs/stability_3b.yml\u001b[m\n",
"\t\u001b[31mmodified: configs/vicuna_13B_4bit_reflect.yml\u001b[m\n",
"\t\u001b[31mmodified: docker/Dockerfile-base\u001b[m\n",
"\t\u001b[31mmodified: examples/gptq-lora-7b/config.yml\u001b[m\n",
"\t\u001b[31mmodified: examples/mpt-7b/config.yml\u001b[m\n",
"\t\u001b[31mmodified: requirements.txt\u001b[m\n",
"\t\u001b[31mmodified: scripts/finetune.py\u001b[m\n",
"\t\u001b[31mmodified: scripts/runpod-entrypoint.sh\u001b[m\n",
"\t\u001b[31mmodified: src/axolotl/datasets.py\u001b[m\n",
"\t\u001b[31mmodified: src/axolotl/flash_attn.py\u001b[m\n",
"\t\u001b[31mmodified: src/axolotl/utils/data.py\u001b[m\n",
"\t\u001b[31mmodified: src/axolotl/utils/models.py\u001b[m\n",
"\t\u001b[31mmodified: src/axolotl/utils/validation.py\u001b[m\n",
"\t\u001b[31mmodified: tests/test_prompt_tokenizers.py\u001b[m\n",
"\t\u001b[31mmodified: tests/test_validation.py\u001b[m\n",
"\n",
"Untracked files:\n",
" (use \"git add ...\" to include in what will be committed)\n",
"\t\u001b[31mexamples/falcon/lora.yml\u001b[m\n",
"\t\u001b[31mexamples/falcon/qlora.yml\u001b[m\n",
"\t\u001b[31mexamples/huggyllama/\u001b[m\n",
"\t\u001b[31mexamples/openllama/\u001b[m\n",
"\t\u001b[31mimage/axolotl-badge-web.png\u001b[m\n",
"\t\u001b[31msrc/axolotl/monkeypatch/\u001b[m\n",
"\t\u001b[31mtests/fixtures/alpaca/\u001b[m\n",
"\t\u001b[31mtests/test_packed_dataset.py\u001b[m\n",
"\n",
"no changes added to commit (use \"git add\" and/or \"git commit -a\")\n"
]
}
],
"source": [
"!git status"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Obtaining file:///workspace/axolotl\n",
" Preparing metadata (setup.py) ... \u001b[?25ldone\n",
"\u001b[?25hCollecting transformers@ git+https://github.com/huggingface/transformers.git (from axolotl==0.1)\n",
" Cloning https://github.com/huggingface/transformers.git to /tmp/pip-install-w8_gs79m/transformers_d7bb90e3e98d4c5c855b5488e6abe26d\n",
" Running command git clone --filter=blob:none --quiet https://github.com/huggingface/transformers.git /tmp/pip-install-w8_gs79m/transformers_d7bb90e3e98d4c5c855b5488e6abe26d\n",
" Resolved https://github.com/huggingface/transformers.git to commit f49a3453caa6fe606bb31c571423f72264152fce\n",
" Installing build dependencies ... \u001b[?25ldone\n",
"\u001b[?25h Getting requirements to build wheel ... \u001b[?25ldone\n",
"\u001b[?25h Preparing metadata (pyproject.toml) ... \u001b[?25ldone\n",
"\u001b[?25hRequirement 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)\n",
"Requirement already satisfied: accelerate in /root/miniconda3/envs/py3.9/lib/python3.9/site-packages (from axolotl==0.1) (0.20.0.dev0)\n",
"Requirement already satisfied: addict in /root/miniconda3/envs/py3.9/lib/python3.9/site-packages (from axolotl==0.1) (2.4.0)\n",
"Requirement already satisfied: fire in /root/miniconda3/envs/py3.9/lib/python3.9/site-packages (from axolotl==0.1) (0.5.0)\n",
"Requirement already satisfied: PyYAML==6.0 in /root/miniconda3/envs/py3.9/lib/python3.9/site-packages (from axolotl==0.1) (6.0)\n",
"Requirement already satisfied: datasets in /root/miniconda3/envs/py3.9/lib/python3.9/site-packages (from axolotl==0.1) (2.12.0)\n",
"Requirement already satisfied: sentencepiece in /root/miniconda3/envs/py3.9/lib/python3.9/site-packages (from axolotl==0.1) (0.1.99)\n",
"Requirement already satisfied: wandb in /root/miniconda3/envs/py3.9/lib/python3.9/site-packages (from axolotl==0.1) (0.15.3)\n",
"Requirement already satisfied: einops in /root/miniconda3/envs/py3.9/lib/python3.9/site-packages (from axolotl==0.1) (0.6.1)\n",
"Requirement already satisfied: xformers in /root/miniconda3/envs/py3.9/lib/python3.9/site-packages (from axolotl==0.1) (0.0.20)\n",
"Requirement already satisfied: bert-score==0.3.13 in /root/miniconda3/envs/py3.9/lib/python3.9/site-packages (from axolotl==0.1) (0.3.13)\n",
"Requirement already satisfied: evaluate==0.4.0 in /root/miniconda3/envs/py3.9/lib/python3.9/site-packages (from axolotl==0.1) (0.4.0)\n",
"Requirement already satisfied: rouge-score==0.1.2 in /root/miniconda3/envs/py3.9/lib/python3.9/site-packages (from axolotl==0.1) (0.1.2)\n",
"Requirement already satisfied: scipy in /root/miniconda3/envs/py3.9/lib/python3.9/site-packages (from axolotl==0.1) (1.10.1)\n",
"Requirement already satisfied: scikit-learn==1.2.2 in /root/miniconda3/envs/py3.9/lib/python3.9/site-packages (from axolotl==0.1) (1.2.2)\n",
"Requirement already satisfied: torch>=1.0.0 in /root/miniconda3/envs/py3.9/lib/python3.9/site-packages (from bert-score==0.3.13->axolotl==0.1) (2.0.1)\n",
"Requirement already satisfied: pandas>=1.0.1 in /root/miniconda3/envs/py3.9/lib/python3.9/site-packages (from bert-score==0.3.13->axolotl==0.1) (2.0.2)\n",
"Requirement already satisfied: numpy in /root/miniconda3/envs/py3.9/lib/python3.9/site-packages (from bert-score==0.3.13->axolotl==0.1) (1.24.3)\n",
"Requirement already satisfied: requests in /root/miniconda3/envs/py3.9/lib/python3.9/site-packages (from bert-score==0.3.13->axolotl==0.1) (2.31.0)\n",
"Requirement already satisfied: tqdm>=4.31.1 in /root/miniconda3/envs/py3.9/lib/python3.9/site-packages (from bert-score==0.3.13->axolotl==0.1) (4.65.0)\n",
"Requirement already satisfied: matplotlib in /root/miniconda3/envs/py3.9/lib/python3.9/site-packages (from bert-score==0.3.13->axolotl==0.1) (3.7.1)\n",
"Requirement already satisfied: packaging>=20.9 in /root/miniconda3/envs/py3.9/lib/python3.9/site-packages (from bert-score==0.3.13->axolotl==0.1) (23.1)\n",
"Requirement already satisfied: dill in /root/miniconda3/envs/py3.9/lib/python3.9/site-packages (from evaluate==0.4.0->axolotl==0.1) (0.3.6)\n",
"Requirement already satisfied: xxhash in /root/miniconda3/envs/py3.9/lib/python3.9/site-packages (from evaluate==0.4.0->axolotl==0.1) (3.2.0)\n",
"Requirement already satisfied: multiprocess in /root/miniconda3/envs/py3.9/lib/python3.9/site-packages (from evaluate==0.4.0->axolotl==0.1) (0.70.14)\n",
"Requirement already satisfied: fsspec[http]>=2021.05.0 in /root/miniconda3/envs/py3.9/lib/python3.9/site-packages (from evaluate==0.4.0->axolotl==0.1) (2023.5.0)\n",
"Requirement already satisfied: huggingface-hub>=0.7.0 in /root/miniconda3/envs/py3.9/lib/python3.9/site-packages (from evaluate==0.4.0->axolotl==0.1) (0.14.1)\n",
"Requirement already satisfied: responses<0.19 in /root/miniconda3/envs/py3.9/lib/python3.9/site-packages (from evaluate==0.4.0->axolotl==0.1) (0.18.0)\n",
"Requirement already satisfied: absl-py in /root/miniconda3/envs/py3.9/lib/python3.9/site-packages (from rouge-score==0.1.2->axolotl==0.1) (1.4.0)\n",
"Requirement already satisfied: nltk in /root/miniconda3/envs/py3.9/lib/python3.9/site-packages (from rouge-score==0.1.2->axolotl==0.1) (3.8.1)\n",
"Requirement already satisfied: six>=1.14.0 in /root/miniconda3/envs/py3.9/lib/python3.9/site-packages (from rouge-score==0.1.2->axolotl==0.1) (1.16.0)\n",
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"Requirement already satisfied: nvidia-curand-cu11==10.2.10.91 in /root/miniconda3/envs/py3.9/lib/python3.9/site-packages (from torch>=1.0.0->bert-score==0.3.13->axolotl==0.1) (10.2.10.91)\n",
"Requirement already satisfied: nvidia-cusolver-cu11==11.4.0.1 in /root/miniconda3/envs/py3.9/lib/python3.9/site-packages (from torch>=1.0.0->bert-score==0.3.13->axolotl==0.1) (11.4.0.1)\n",
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"Requirement already satisfied: nvidia-nccl-cu11==2.14.3 in /root/miniconda3/envs/py3.9/lib/python3.9/site-packages (from torch>=1.0.0->bert-score==0.3.13->axolotl==0.1) (2.14.3)\n",
"Requirement already satisfied: nvidia-nvtx-cu11==11.7.91 in /root/miniconda3/envs/py3.9/lib/python3.9/site-packages (from torch>=1.0.0->bert-score==0.3.13->axolotl==0.1) (11.7.91)\n",
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"Requirement already satisfied: importlib-resources>=3.2.0 in /root/miniconda3/envs/py3.9/lib/python3.9/site-packages (from matplotlib->bert-score==0.3.13->axolotl==0.1) (5.12.0)\n",
"Requirement already satisfied: smmap<6,>=3.0.1 in /root/miniconda3/envs/py3.9/lib/python3.9/site-packages (from gitdb<5,>=4.0.1->GitPython!=3.1.29,>=1.0.0->wandb->axolotl==0.1) (5.0.0)\n",
"Requirement already satisfied: zipp>=3.1.0 in /root/miniconda3/envs/py3.9/lib/python3.9/site-packages (from importlib-resources>=3.2.0->matplotlib->bert-score==0.3.13->axolotl==0.1) (3.15.0)\n",
"Requirement already satisfied: MarkupSafe>=2.0 in /root/miniconda3/envs/py3.9/lib/python3.9/site-packages (from jinja2->torch>=1.0.0->bert-score==0.3.13->axolotl==0.1) (2.1.2)\n",
"Requirement already satisfied: mpmath>=0.19 in /root/miniconda3/envs/py3.9/lib/python3.9/site-packages (from sympy->torch>=1.0.0->bert-score==0.3.13->axolotl==0.1) (1.3.0)\n",
"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)\n",
"Installing collected packages: axolotl\n",
" Attempting uninstall: axolotl\n",
" Found existing installation: axolotl 0.1\n",
" Uninstalling axolotl-0.1:\n",
" Successfully uninstalled axolotl-0.1\n",
" Running setup.py develop for axolotl\n",
"Successfully installed axolotl-0.1\n",
"\u001b[33mWARNING: 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\u001b[0m\u001b[33m\n",
"\u001b[0m"
]
}
],
"source": [
"!pip install -e ."
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"### A new bash without tmux etc."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!bash --norc --noprofile"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Clean up all checkpoints but last one"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"cd /workspace/axolotl/qlora-out/ && ls -lhta |grep checkpoint- | awk 'NR > 1 {print $9}' | xargs rm -rf"
]
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"authorship_tag": "ABX9TyP0jHt4WNuLaC5ecmX0YtWl",
"gpuType": "T4",
"include_colab_link": true,
"provenance": [],
"toc_visible": true
},
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.16"
}
},
"nbformat": 4,
"nbformat_minor": 4
}