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yalhessi/
lemexp-task1-v2-template_small_nodefs-Llama-3.2-1B-8lr-24epochs-nonspecial-eos-token

Name: lemexp-task1-v2-template_small_nodefs-Llama-3.2-1B-8lr-24epochs-nonspecial-eos-token
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lemexp-task1-v2-template_small_nodefs-Llama-3.2-1B-8lr-24epochs-nonspecial-eos-token

This model is a fine-tuned version of meta-llama/Llama-3.2-1B on an unknown dataset. It achieves the following results on the evaluation set:

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The following hyperparameters were used during training:

learning_rate: 0.0008
train_batch_size: 1
eval_batch_size: 2
seed: 42
distributed_type: multi-GPU
num_devices: 8
gradient_accumulation_steps: 2
total_train_batch_size: 16
total_eval_batch_size: 16
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 24
mixed_precision_training: Native AMP

Training Loss	Epoch	Step	Validation Loss
0.4177	0.4002	1440	0.4011
0.3596	0.8003	2880	0.3598
0.3297	1.2004	4320	0.3253
0.3159	1.6005	5760	0.3246
0.3067	2.0006	7200	0.3112
0.2948	2.4007	8640	0.3049
0.2864	2.8009	10080	0.2953
0.2753	3.2009	11520	0.2888
0.2713	3.6011	12960	0.2779
0.2682	4.0011	14400	0.2768
0.262	4.4013	15840	0.2739
0.2555	4.8014	17280	0.2742
0.2499	5.2015	18720	0.2648
0.2484	5.6016	20160	0.2723
0.2425	6.0017	21600	0.2579
0.2395	6.4018	23040	0.2447
0.2363	6.8020	24480	0.2571
0.226	7.2020	25920	0.2494
0.2289	7.6022	27360	0.2449
0.2294	8.0022	28800	0.2507
0.2153	8.4024	30240	0.2330
0.2159	8.8026	31680	0.2298
0.21	9.2026	33120	0.2275
0.2115	9.6028	34560	0.2332
0.2026	10.0028	36000	0.2274
0.1982	10.4029	37440	0.2214
0.2012	10.8031	38880	0.2145
0.1917	11.2031	40320	0.2179
0.1919	11.6033	41760	0.2135
0.1929	12.0033	43200	0.2088
0.18	12.4035	44640	0.2131
0.1841	12.8037	46080	0.2005
0.1729	13.2037	47520	0.2046
0.174	13.6039	48960	0.2056
0.1745	14.0039	50400	0.1975
0.1668	14.4041	51840	0.2001
0.1687	14.8042	53280	0.1923
0.1562	15.2043	54720	0.1963
0.1583	15.6044	56160	0.1853
0.1593	16.0044	57600	0.1869
0.1494	16.4046	59040	0.1833
0.1511	16.8048	60480	0.1816
0.1401	17.2048	61920	0.1810
0.1403	17.6050	63360	0.1832
0.142	18.0050	64800	0.1797
0.1293	18.4052	66240	0.1759
0.1346	18.8053	67680	0.1734
0.1223	19.2054	69120	0.1782
0.1218	19.6055	70560	0.1702
0.12	20.0056	72000	0.1749
0.1127	20.4057	73440	0.1701
0.1137	20.8059	74880	0.1672
0.1065	21.2059	76320	0.1728
0.1032	21.6061	77760	0.1663
0.1019	22.0061	79200	0.1690
0.0929	22.4063	80640	0.1686
0.0948	22.8064	82080	0.1651
0.0858	23.2065	83520	0.1667
0.0874	23.6066	84960	0.1670