model 3
Browse files- README.md +25 -180
- adapter_model.bin +1 -1
- {checkpoint-200 β checkpoint-1000}/optimizer.pt +1 -1
- {checkpoint-400 β checkpoint-1000}/pytorch_model.bin +1 -1
- {checkpoint-400 β checkpoint-1000}/rng_state_0.pth +1 -1
- {checkpoint-400 β checkpoint-1000}/rng_state_1.pth +1 -1
- {checkpoint-200 β checkpoint-1000}/scaler.pt +1 -1
- {checkpoint-200 β checkpoint-1000}/scheduler.pt +1 -1
- checkpoint-1000/trainer_state.json +656 -0
- {checkpoint-400 β checkpoint-1000}/training_args.bin +1 -1
- {checkpoint-400 β checkpoint-1200}/optimizer.pt +1 -1
- {checkpoint-200 β checkpoint-1200}/pytorch_model.bin +1 -1
- {checkpoint-200 β checkpoint-1200}/rng_state_0.pth +1 -1
- {checkpoint-200 β checkpoint-1200}/rng_state_1.pth +1 -1
- {checkpoint-400 β checkpoint-1200}/scaler.pt +1 -1
- {checkpoint-400 β checkpoint-1200}/scheduler.pt +1 -1
- checkpoint-1200/trainer_state.json +784 -0
- {checkpoint-200 β checkpoint-1200}/training_args.bin +1 -1
- checkpoint-1400/optimizer.pt +3 -0
- checkpoint-1400/pytorch_model.bin +3 -0
- checkpoint-1400/rng_state_0.pth +3 -0
- checkpoint-1400/rng_state_1.pth +3 -0
- checkpoint-1400/scaler.pt +3 -0
- checkpoint-1400/scheduler.pt +3 -0
- checkpoint-1400/trainer_state.json +912 -0
- checkpoint-1400/training_args.bin +3 -0
- checkpoint-200/trainer_state.json +0 -144
- checkpoint-400/trainer_state.json +0 -272
README.md
CHANGED
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@@ -6,186 +6,31 @@ Note 1. Output directory was initially lora-alpaca and then contents were moved
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## Log
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(sqltest) chrisdono@deep-learning-duo-t4-3:~/alpaca-lora$ WORLD_SIZE=2 CUDA_VISIBLE_DEVICES=0,1 torchrun --nproc_per_node=2 --master_port=1234 finetune.py --base_model 'decapoda-research/llam
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a-7b-hf' --data_path 'spider' --output_dir './lora-alpaca' --num_epochs 3 --batch_size 32 --micro_batch_size 16 --learning_rate '1e-4'
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WARNING:torch.distributed.run:
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*****************************************
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Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your appli
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cation as needed.
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*****************************************
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===================================BUG REPORT===================================
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Welcome to bitsandbytes. For bug reports, please submit your error trace to: https://github.com/TimDettmers/bitsandbytes/issues
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================================================================================
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===================================BUG REPORT===================================
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Welcome to bitsandbytes. For bug reports, please submit your error trace to: https://github.com/TimDettmers/bitsandbytes/issues
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================================================================================
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/opt/conda/envs/sqltest/lib/python3.10/site-packages/bitsandbytes/cuda_setup/main.py:136: UserWarning: /opt/conda/envs/sqltest did not contain libcudart.so as expected! Searching further path
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s...
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warn(msg)
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/opt/conda/envs/sqltest/lib/python3.10/site-packages/bitsandbytes/cuda_setup/main.py:136: UserWarning: /opt/conda/envs/sqltest did not contain libcudart.so as expected! Searching further path
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s...
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warn(msg)
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CUDA SETUP: CUDA runtime path found: /usr/local/cuda/lib64/libcudart.so
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CUDA SETUP: Highest compute capability among GPUs detected: 7.5
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CUDA SETUP: Detected CUDA version 113
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CUDA SETUP: Loading binary /opt/conda/envs/sqltest/lib/python3.10/site-packages/bitsandbytes/libbitsandbytes_cuda113.so...
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CUDA SETUP: CUDA runtime path found: /usr/local/cuda/lib64/libcudart.so
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CUDA SETUP: Highest compute capability among GPUs detected: 7.5
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CUDA SETUP: Detected CUDA version 113
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CUDA SETUP: Loading binary /opt/conda/envs/sqltest/lib/python3.10/site-packages/bitsandbytes/libbitsandbytes_cuda113.so...
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Training Alpaca-LoRA model with params:
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base_model: decapoda-research/llama-7b-hf
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data_path: spider
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output_dir: ./lora-alpaca
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batch_size: 32
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micro_batch_size: 16
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num_epochs: 3
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learning_rate: 0.0001
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cutoff_len: 256
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val_set_size: 2000
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lora_r: 8
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lora_alpha: 16
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lora_dropout: 0.05
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lora_target_modules: ['q_proj', 'v_proj']
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train_on_inputs: True
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add_eos_token: False
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group_by_length: False
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wandb_project:
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wandb_run_name:
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wandb_watch:
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wandb_log_model:
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resume_from_checkpoint: False
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prompt template: alpaca
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Loading checkpoint shards: 100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 33/33 [01:19<00:00, 2.42s/it]
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The tokenizer class you load from this checkpoint is not the same type as the class this function is called from. It may result in unexpected tokenization.
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The tokenizer class you load from this checkpoint is 'LLaMATokenizer'.
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The class this function is called from is 'LlamaTokenizer'.
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The tokenizer class you load from this checkpoint is not the same type as the class this function is called from. It may result in unexpected tokenization.
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The tokenizer class you load from this checkpoint is 'LLaMATokenizer'.
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The class this function is called from is 'LlamaTokenizer'.
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Found cached dataset spider (/home/chrisdono/.cache/huggingface/datasets/spider/spider/1.0.0/4e5143d825a3895451569c8b9b55432b91a4bc2d04d390376c950837f4680daa)
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0%| | 0/2 [00:00<?, ?it/s]
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Found cached dataset spider (/home/chrisdono/.cache/huggingface/datasets/spider/spider/1.0.0/4e5143d825a3895451569c8b9b55432b91a4bc2d04d390376c950837f4680daa)
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100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββοΏ½οΏ½βββββββββββββββββββββββββββββββββββββββββββββββ| 2/2 [00:00<00:00, 113.71it/s]
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100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2/2 [00:00<00:00, 56.18it/s]
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Found cached dataset csv (/home/chrisdono/.cache/huggingface/datasets/csv/default-68889607ac077205/0.0.0/6954658bab30a358235fa864b05cf819af0e179325c740e4bc853bcc7ec513e1)
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100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2/2 [00:00<00:00, 214.92it/s]
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trainable params: 4194304 || all params: 6742609920 || trainable%: 0.06220594176090199
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Loading cached split indices for dataset at /home/chrisdono/.cache/huggingface/datasets/csv/default-68889607ac077205/0.0.0/6954658bab30a358235fa864b05cf819af0e179325c740e4bc853bcc7ec513e1/cac
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he-b310cf91933dea79.arrow and /home/chrisdono/.cache/huggingface/datasets/csv/default-68889607ac077205/0.0.0/6954658bab30a358235fa864b05cf819af0e179325c740e4bc853bcc7ec513e1/cache-9632dc43aab
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73df2.arrow
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Found cached dataset csv (/home/chrisdono/.cache/huggingface/datasets/csv/default-68889607ac077205/0.0.0/6954658bab30a358235fa864b05cf819af0e179325c740e4bc853bcc7ec513e1)
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100%|βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 2/2 [00:00<00:00, 787.74it/s]
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trainable params: 4194304 || all params: 6742609920 || trainable%: 0.06220594176090199
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Loading cached split indices for dataset at /home/chrisdono/.cache/huggingface/datasets/csv/default-68889607ac077205/0.0.0/6954658bab30a358235fa864b05cf819af0e179325c740e4bc853bcc7ec513e1/cac
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he-b310cf91933dea79.arrow and /home/chrisdono/.cache/huggingface/datasets/csv/default-68889607ac077205/0.0.0/6954658bab30a358235fa864b05cf819af0e179325c740e4bc853bcc7ec513e1/cache-9632dc43aab
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73df2.arrow
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TRAIN DATA
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{'Unnamed: 0': 2621, 'db_id': 'inn_1', 'query': 'SELECT decor , avg(basePrice) , min(basePrice) FROM Rooms GROUP BY decor;', 'question': 'What is the average minimum and price of the rooms f
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or each different decor.', 'query_toks': "['SELECT' 'decor' ',' 'avg' '(' 'basePrice' ')' ',' 'min' '(' 'basePrice'\n ')' 'FROM' 'Rooms' 'GROUP' 'BY' 'decor' ';']", 'query_toks_no_value': "['
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| 90 |
-
select' 'decor' ',' 'avg' '(' 'baseprice' ')' ',' 'min' '(' 'baseprice'\n ')' 'from' 'rooms' 'group' 'by' 'decor']", 'question_toks': "['What' 'is' 'the' 'average' 'minimum' 'and' 'price' 'of
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| 91 |
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' 'the' 'rooms'\n 'for' 'each' 'different' 'decor' '.']", 'db_context': "['room id', 'room name', 'beds', 'bed type', 'max occupancy', 'base price', 'decor', 'code', 'room', 'check in', 'chec
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| 92 |
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k out', 'rate', 'last name', 'first name', 'adults', 'kids']", 'input_ids': [0, 13866, 338, 385, 15278, 393, 16612, 263, 3414, 29892, 3300, 2859, 411, 385, 1881, 393, 8128, 4340, 3030, 29889,
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| 93 |
-
14350, 263, 2933, 393, 7128, 2486, 1614, 2167, 278, 2009, 29889, 13, 13, 2277, 29937, 2799, 4080, 29901, 13, 5618, 338, 278, 6588, 9212, 322, 8666, 310, 278, 19600, 363, 1269, 1422, 10200, 2
|
| 94 |
-
9889, 13, 13, 2277, 29937, 10567, 29901, 13, 1839, 8345, 1178, 742, 525, 8345, 1024, 742, 525, 2580, 29879, 742, 525, 2580, 1134, 742, 525, 3317, 6919, 6906, 742, 525, 3188, 8666, 742, 525, 1
|
| 95 |
-
9557, 742, 525, 401, 742, 525, 8345, 742, 525, 3198, 297, 742, 525, 3198, 714, 742, 525, 10492, 742, 525, 4230, 1024, 742, 525, 4102, 1024, 742, 525, 328, 499, 29879, 742, 525, 29895, 4841, 2
|
| 96 |
-
033, 13, 13, 2277, 29937, 13291, 29901, 13, 6404, 10200, 1919, 1029, 29887, 29898, 3188, 13026, 29897, 1919, 29871, 1375, 29898, 3188, 13026, 29897, 3895, 1528, 4835, 15345, 6770, 10200, 2993
|
| 97 |
-
6, 0], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
|
| 98 |
-
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
|
| 99 |
-
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 'labels': [0, 13866, 338, 385, 15278, 393, 16612, 263, 3414, 29892, 3300, 2859, 411,
|
| 100 |
-
385, 1881, 393, 8128, 4340, 3030, 29889, 14350, 263, 2933, 393, 7128, 2486, 1614, 2167, 278, 2009, 29889, 13, 13, 2277, 29937, 2799, 4080, 29901, 13, 5618, 338, 278, 6588, 9212, 322, 8666, 3
|
| 101 |
-
10, 278, 19600, 363, 1269, 1422, 10200, 29889, 13, 13, 2277, 29937, 10567, 29901, 13, 1839, 8345, 1178, 742, 525, 8345, 1024, 742, 525, 2580, 29879, 742, 525, 2580, 1134, 742, 525, 3317, 6919
|
| 102 |
-
, 6906, 742, 525, 3188, 8666, 742, 525, 19557, 742, 525, 401, 742, 525, 8345, 742, 525, 3198, 297, 742, 525, 3198, 714, 742, 525, 10492, 742, 525, 4230, 1024, 742, 525, 4102, 1024, 742, 525,
|
| 103 |
-
328, 499, 29879, 742, 525, 29895, 4841, 2033, 13, 13, 2277, 29937, 13291, 29901, 13, 6404, 10200, 1919, 1029, 29887, 29898, 3188, 13026, 29897, 1919, 29871, 1375, 29898, 3188, 13026, 29897, 3
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| 104 |
-
895, 1528, 4835, 15345, 6770, 10200, 29936, 0]}
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| 105 |
-
TRAIN DATA
|
| 106 |
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{'Unnamed: 0': 4767, 'db_id': 'department_store', 'query': 'SELECT product_id FROM Order_Items GROUP BY product_id HAVING count(*) > 3 UNION SELECT product_id FROM Product_Suppliers GROUP B
|
| 107 |
-
Y product_id HAVING sum(total_amount_purchased) > 80000', 'question': 'Return the ids of all products that were ordered more than three times or supplied more than 80000.', 'query_toks': "[
|
| 108 |
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'SELECT' 'product_id' 'FROM' 'Order_Items' 'GROUP' 'BY' 'product_id'\n 'HAVING' 'count' '(' '*' ')' '>' '3' 'UNION' 'SELECT' 'product_id' 'FROM'\n 'Product_Suppliers' 'GROUP' 'BY' 'product_id
|
| 109 |
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' 'HAVING' 'sum' '('\n 'total_amount_purchased' ')' '>' '80000']", 'query_toks_no_value': "['select' 'product_id' 'from' 'order_items' 'group' 'by' 'product_id'\n 'having' 'count' '(' '*' ')'
|
| 110 |
-
'>' 'value' 'union' 'select' 'product_id'\n 'from' 'product_suppliers' 'group' 'by' 'product_id' 'having' 'sum' '('\n 'total_amount_purchased' ')' '>' 'value']", 'question_toks': "['Return'
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| 111 |
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'the' 'ids' 'of' 'all' 'products' 'that' 'were' 'ordered' 'more'\n 'than' 'three' 'times' 'or' 'supplied' 'more' 'than' '80000' '.']", 'db_context': "['address id', 'address details', 'staff
|
| 112 |
-
id', 'staff gender', 'staff name', 'supplier id', 'supplier name', 'supplier phone', 'department store chain id', 'department store chain name', 'customer id', 'payment method code', 'custome
|
| 113 |
-
r code', 'customer name', 'customer address', 'customer phone', 'customer email', 'product id', 'product type code', 'product name', 'product price', 'supplier id', 'address id', 'date from',
|
| 114 |
-
'date to', 'customer id', 'address id', 'date from', 'date to', 'order id', 'customer id', 'order status code', 'order date', 'department store id', 'department store chain id', 'store name'
|
| 115 |
-
, 'store address', 'store phone', 'store email', 'department id', 'department store id', 'department name', 'order item id', 'order id', 'product id', 'product id', 'supplier id', 'date suppl
|
| 116 |
-
ied from', 'date supplied to', 'total amount purchased', 'total value purchased', 'staff id', 'department id', 'date assigned from', 'job title code', 'date assigned to']", 'input_ids': [0, 1
|
| 117 |
-
3866, 338, 385, 15278, 393, 16612, 263, 3414, 29892, 3300, 2859, 411, 385, 1881, 393, 8128, 4340, 3030, 29889, 14350, 263, 2933, 393, 7128, 2486, 1614, 2167, 278, 2009, 29889, 13, 13, 2277, 2
|
| 118 |
-
9937, 2799, 4080, 29901, 13, 11609, 278, 18999, 310, 599, 9316, 393, 892, 10372, 901, 1135, 2211, 3064, 470, 19056, 901, 1135, 29871, 29947, 29900, 29900, 29900, 29900, 29889, 13, 13, 2277, 2
|
| 119 |
-
9937, 10567, 29901, 13, 1839, 7328, 1178, 742, 525, 7328, 4902, 742, 525, 303, 3470, 1178, 742, 525, 303, 3470, 23346, 742, 525, 303, 3470, 1024, 742, 525, 19303, 4926, 1178, 742, 525, 19303,
|
| 120 |
-
4926, 1024, 742, 525, 19303, 4926, 9008, 742, 525, 311, 8076, 3787, 9704, 1178, 742, 525, 311, 8076, 3787, 9704, 1024, 742, 525, 15539, 1178, 742, 525, 27825, 1158, 775, 742, 525, 15539, 775
|
| 121 |
-
, 742, 525, 15539, 1024, 742, 525, 15539, 3211, 742, 525, 15539, 9008, 742, 525, 15539, 4876, 742, 525, 4704, 1178, 742, 525, 4704, 1134, 775, 742, 525, 4704, 1024, 742, 525, 4704, 8666, 742,
|
| 122 |
-
525, 19303, 4926, 1178, 742, 525, 7328, 1178, 742, 525, 1256, 515, 742, 525, 1256, 304, 742, 525, 15539, 1178, 742, 525, 7328, 1178, 742, 525, 1256, 515, 742, 525, 1256, 304, 742, 525, 2098,
|
| 123 |
-
1178, 742, 525, 15539, 1178, 742, 525, 2098, 4660, 775, 742, 525, 2098, 2635, 742, 525, 311, 8076, 3787, 1178, 742, 525, 311, 8076, 3787, 9704, 1178, 742, 525, 8899, 1024, 742, 525, 8899, 32
|
| 124 |
-
11, 742, 525, 8899, 9008, 742, 525, 8899, 4876, 742, 525, 311, 8076, 1178, 742, 525, 311, 8076, 3787], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
|
| 125 |
-
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
|
| 126 |
-
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1
|
| 127 |
-
, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
|
| 128 |
-
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 'labels': [0, 13866, 338, 385, 15278, 393, 16612, 263, 3414, 298
|
| 129 |
-
92, 3300, 2859, 411, 385, 1881, 393, 8128, 4340, 3030, 29889, 14350, 263, 2933, 393, 7128, 2486, 1614, 2167, 278, 2009, 29889, 13, 13, 2277, 29937, 2799, 4080, 29901, 13, 11609, 278, 18999, 3
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-
10, 599, 9316, 393, 892, 10372, 901, 1135, 2211, 3064, 470, 19056, 901, 1135, 29871, 29947, 29900, 29900, 29900, 29900, 29889, 13, 13, 2277, 29937, 10567, 29901, 13, 1839, 7328, 1178, 742, 52
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2, 525, 1256, 515, 742, 525, 1256, 304, 742, 525, 15539, 1178, 742, 525, 7328, 1178, 742, 525, 1256, 515, 742, 525, 1256, 304, 742, 525, 2098, 1178, 742, 525, 15539, 1178, 742, 525, 2098, 466
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0, 775, 742, 525, 2098, 2635, 742, 525, 311, 8076, 3787, 1178, 742, 525, 311, 8076, 3787, 9704, 1178, 742, 525, 8899, 1024, 742, 525, 8899, 3211, 742, 525, 8899, 9008, 742, 525, 8899, 4876, 7
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| 136 |
-
42, 525, 311, 8076, 1178, 742, 525, 311, 8076, 3787]}
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| 137 |
-
{'loss': 2.2228, 'learning_rate': 8.000000000000001e-06, 'epoch': 0.06}
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| 138 |
-
{'loss': 2.185, 'learning_rate': 1.8e-05, 'epoch': 0.13}
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| 139 |
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{'loss': 2.1452, 'learning_rate': 2.8000000000000003e-05, 'epoch': 0.19}
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| 140 |
-
{'loss': 2.0232, 'learning_rate': 3.8e-05, 'epoch': 0.25}
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| 141 |
-
{'loss': 1.884, 'learning_rate': 4.8e-05, 'epoch': 0.32}
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-
{'loss': 1.62, 'learning_rate': 5.6000000000000006e-05, 'epoch': 0.38}
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| 143 |
-
{'loss': 1.3664, 'learning_rate': 6.6e-05, 'epoch': 0.45}
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| 144 |
-
{'loss': 1.2159, 'learning_rate': 7.6e-05, 'epoch': 0.51}
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| 145 |
-
{'loss': 1.1656, 'learning_rate': 8.6e-05, 'epoch': 0.57}
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| 146 |
-
{'loss': 1.0664, 'learning_rate': 9.6e-05, 'epoch': 0.64}
|
| 147 |
-
{'loss': 1.0253, 'learning_rate': 9.838274932614556e-05, 'epoch': 0.7}
|
| 148 |
-
{'loss': 0.9716, 'learning_rate': 9.568733153638815e-05, 'epoch': 0.76}
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| 149 |
-
{'loss': 0.9162, 'learning_rate': 9.299191374663073e-05, 'epoch': 0.83}
|
| 150 |
-
{'loss': 0.8849, 'learning_rate': 9.029649595687331e-05, 'epoch': 0.89}
|
| 151 |
-
{'loss': 0.8648, 'learning_rate': 8.76010781671159e-05, 'epoch': 0.96}
|
| 152 |
-
{'loss': 0.8077, 'learning_rate': 8.49056603773585e-05, 'epoch': 1.02}
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| 153 |
-
{'loss': 0.7443, 'learning_rate': 8.221024258760108e-05, 'epoch': 1.08}
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| 154 |
-
{'loss': 0.7253, 'learning_rate': 7.951482479784367e-05, 'epoch': 1.15}
|
| 155 |
-
{'loss': 0.6845, 'learning_rate': 7.681940700808625e-05, 'epoch': 1.21}
|
| 156 |
-
{'loss': 0.6956, 'learning_rate': 7.412398921832885e-05, 'epoch': 1.27}
|
| 157 |
-
{'eval_loss': 0.6555210947990417, 'eval_runtime': 179.8763, 'eval_samples_per_second': 11.119, 'eval_steps_per_second': 0.695, 'epoch': 1.27}
|
| 158 |
-
{'loss': 0.6293, 'learning_rate': 7.142857142857143e-05, 'epoch': 1.34}
|
| 159 |
-
{'loss': 0.5948, 'learning_rate': 6.873315363881401e-05, 'epoch': 1.4}
|
| 160 |
-
{'loss': 0.5306, 'learning_rate': 6.60377358490566e-05, 'epoch': 1.46}
|
| 161 |
-
{'loss': 0.5607, 'learning_rate': 6.33423180592992e-05, 'epoch': 1.53}
|
| 162 |
-
{'loss': 0.5095, 'learning_rate': 6.0646900269541785e-05, 'epoch': 1.59}
|
| 163 |
-
{'loss': 0.4947, 'learning_rate': 5.795148247978437e-05, 'epoch': 1.66}
|
| 164 |
-
{'loss': 0.4856, 'learning_rate': 5.525606469002696e-05, 'epoch': 1.72}
|
| 165 |
-
{'loss': 0.4878, 'learning_rate': 5.2560646900269536e-05, 'epoch': 1.78}
|
| 166 |
-
{'loss': 0.4496, 'learning_rate': 4.986522911051213e-05, 'epoch': 1.85}
|
| 167 |
-
{'loss': 0.4544, 'learning_rate': 4.716981132075472e-05, 'epoch': 1.91}
|
| 168 |
-
{'loss': 0.4542, 'learning_rate': 4.447439353099731e-05, 'epoch': 1.97}
|
| 169 |
-
{'loss': 0.4556, 'learning_rate': 4.1778975741239893e-05, 'epoch': 2.04}
|
| 170 |
-
{'loss': 0.4014, 'learning_rate': 3.908355795148248e-05, 'epoch': 2.1}
|
| 171 |
-
{'loss': 0.3893, 'learning_rate': 3.638814016172507e-05, 'epoch': 2.17}
|
| 172 |
-
{'loss': 0.4197, 'learning_rate': 3.369272237196766e-05, 'epoch': 2.23}
|
| 173 |
-
{'loss': 0.3942, 'learning_rate': 3.0997304582210244e-05, 'epoch': 2.29}
|
| 174 |
-
{'loss': 0.3967, 'learning_rate': 2.830188679245283e-05, 'epoch': 2.36}
|
| 175 |
-
{'loss': 0.3848, 'learning_rate': 2.5606469002695423e-05, 'epoch': 2.42}
|
| 176 |
-
{'loss': 0.3834, 'learning_rate': 2.2911051212938006e-05, 'epoch': 2.48}
|
| 177 |
-
{'loss': 0.3647, 'learning_rate': 2.0215633423180595e-05, 'epoch': 2.55}
|
| 178 |
-
{'eval_loss': 0.3913075923919678, 'eval_runtime': 179.5793, 'eval_samples_per_second': 11.137, 'eval_steps_per_second': 0.696, 'epoch': 2.55}
|
| 179 |
-
{'loss': 0.3703, 'learning_rate': 1.752021563342318e-05, 'epoch': 2.61}
|
| 180 |
-
{'loss': 0.3776, 'learning_rate': 1.4824797843665769e-05, 'epoch': 2.68}
|
| 181 |
-
{'loss': 0.3509, 'learning_rate': 1.2129380053908356e-05, 'epoch': 2.74}
|
| 182 |
-
{'loss': 0.3622, 'learning_rate': 9.433962264150944e-06, 'epoch': 2.8}
|
| 183 |
-
{'loss': 0.351, 'learning_rate': 6.738544474393531e-06, 'epoch': 2.87}
|
| 184 |
-
{'loss': 0.351, 'learning_rate': 6.738544474393531e-06, 'epoch': 2.87}
|
| 185 |
-
{'loss': 0.3497, 'learning_rate': 4.0431266846361185e-06, 'epoch': 2.93}
|
| 186 |
-
{'loss': 0.369, 'learning_rate': 1.3477088948787064e-06, 'epoch': 2.99}
|
| 187 |
-
100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 471/471 [1:25:47<00:00, 8.04s/it]
|
| 188 |
-
{'train_runtime': 5148.4044, 'train_samples_per_second': 2.914, 'train_steps_per_second': 0.091, 'train_loss': 0.7860396517057074, 'epoch': 3.0}
|
| 189 |
-
100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 471/471 [1:25:47<00:00, 10.93s/it]
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| 191 |
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| 6 |
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| 7 |
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| 8 |
## Log
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| 9 |
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| 10 |
+
(sqltest) chrisdono@deep-learning-duo-t4-3:~/alpaca-lora$ WORLD_SIZE=2 CUDA_VISIBLE_DEVICES=0,1 torchrun --nproc_per_node=2 --master_port=1234 finetune.py --base_model 'decapoda-research/llama-7b-hf' --data_path 'spider' --output_dir './lora-alpaca' --num_epochs 10 --batch_size 32 --micro_batch_size 16 --learning_rate '9e-5' --add_eos_token
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| 11 |
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| 12 |
+
|
| 13 |
+
Adding last loss values not included in trainer json file from last checkpoint.
|
| 14 |
|
| 15 |
+
{'loss': 0.241, 'learning_rate': 1.0040816326530613e-05, 'epoch': 8.98}
|
| 16 |
+
{'loss': 0.2343, 'learning_rate': 9.42857142857143e-06, 'epoch': 9.04}
|
| 17 |
+
{'loss': 0.2376, 'learning_rate': 8.816326530612245e-06, 'epoch': 9.11}
|
| 18 |
+
{'loss': 0.2355, 'learning_rate': 8.204081632653062e-06, 'epoch': 9.17}
|
| 19 |
+
{'loss': 0.229, 'learning_rate': 7.591836734693877e-06, 'epoch': 9.24}
|
| 20 |
+
{'loss': 0.2325, 'learning_rate': 6.979591836734694e-06, 'epoch': 9.3}
|
| 21 |
+
{'loss': 0.24, 'learning_rate': 6.367346938775511e-06, 'epoch': 9.36}
|
| 22 |
+
{'loss': 0.2438, 'learning_rate': 5.755102040816327e-06, 'epoch': 9.43}
|
| 23 |
+
{'loss': 0.2391, 'learning_rate': 5.142857142857143e-06, 'epoch': 9.49}
|
| 24 |
+
{'loss': 0.2351, 'learning_rate': 4.530612244897959e-06, 'epoch': 9.55}
|
| 25 |
+
{'loss': 0.2289, 'learning_rate': 3.9183673469387755e-06, 'epoch': 9.62}
|
| 26 |
+
{'loss': 0.2294, 'learning_rate': 3.3061224489795924e-06, 'epoch': 9.68}
|
| 27 |
+
{'loss': 0.2344, 'learning_rate': 2.693877551020408e-06, 'epoch': 9.75}
|
| 28 |
+
{'loss': 0.2358, 'learning_rate': 2.0816326530612247e-06, 'epoch': 9.81}
|
| 29 |
+
{'loss': 0.2365, 'learning_rate': 1.469387755102041e-06, 'epoch': 9.87}
|
| 30 |
+
{'loss': 0.2309, 'learning_rate': 8.571428571428572e-07, 'epoch': 9.94}
|
| 31 |
+
{'loss': 0.2438, 'learning_rate': 2.4489795918367347e-07, 'epoch': 10.0}
|
| 32 |
+
100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1570
|
| 33 |
+
{'train_runtime': 17144.6766, 'train_samples_per_second': 2.916, 'train_steps_per_second': 0.092, 'train_loss': 0.41175747267000234, 'epoch': 10.0}
|
| 34 |
+
100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1570
|
| 35 |
+
100%|ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ| 1570
|
| 36 |
+
/1570 [4:45:44<00:00, 10.92s/it]
|
adapter_model.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
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| 3 |
size 16822989
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|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
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| 2 |
+
oid sha256:1e81348636200bed953a7d22b8739df02326f7214568cb23543c97d607744996
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| 3 |
size 16822989
|
{checkpoint-200 β checkpoint-1000}/optimizer.pt
RENAMED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
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| 2 |
-
oid sha256:
|
| 3 |
size 33661637
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:ada108aeb75370253e0951853ba7679a2e83ece2a0fb62fa917cfc8b801a6e05
|
| 3 |
size 33661637
|
{checkpoint-400 β checkpoint-1000}/pytorch_model.bin
RENAMED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 16822989
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
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| 2 |
+
oid sha256:7a446829eac1cfe55026072d9943915b7e04e92ec658726682e785ee4cdd2913
|
| 3 |
size 16822989
|
{checkpoint-400 β checkpoint-1000}/rng_state_0.pth
RENAMED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 14583
|
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|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:c577fb5890f86350227d881b094a3f4caad4d67e203b55f7ff03a77b90ab0cef
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| 3 |
size 14583
|
{checkpoint-400 β checkpoint-1000}/rng_state_1.pth
RENAMED
|
@@ -1,3 +1,3 @@
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| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
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| 3 |
size 14583
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|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:4714127c9b51c424cf4b38037eeee13e70666b411e45a04809165e1ffbd0d4b0
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| 3 |
size 14583
|
{checkpoint-200 β checkpoint-1000}/scaler.pt
RENAMED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
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| 2 |
-
oid sha256:
|
| 3 |
size 557
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|
| 1 |
version https://git-lfs.github.com/spec/v1
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| 2 |
+
oid sha256:52e4fb9b07eb9216477d8718b3245f228285c2168e19863ec6206be844186973
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| 3 |
size 557
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{checkpoint-200 β checkpoint-1000}/scheduler.pt
RENAMED
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@@ -1,3 +1,3 @@
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| 1 |
version https://git-lfs.github.com/spec/v1
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| 2 |
-
oid sha256:
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| 3 |
size 627
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version https://git-lfs.github.com/spec/v1
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oid sha256:67f4442b507c07c88aff26c2e96c490aeb9a0158825e369330c7aae60a905187
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size 627
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checkpoint-1000/trainer_state.json
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