learning_rate: 2e-05
train_batch_size: 64
eval_batch_size: 112
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.01
num_epochs: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Model Preparation Time	Mdl	Accumulated Loss	Correct Preds	Total Preds	Accuracy	Correct Gen Preds	Gen Accuracy	Correct Gen Preds 32	Correct Preds 32	Total Labels 32	Accuracy 32	Gen Accuracy 32	Correct Gen Preds 33	Correct Preds 33	Total Labels 33	Accuracy 33	Gen Accuracy 33	Correct Gen Preds 34	Correct Preds 34	Total Labels 34	Accuracy 34	Gen Accuracy 34	Correct Gen Preds 35	Correct Preds 35	Total Labels 35	Accuracy 35	Gen Accuracy 35	Correct Gen Preds 36	Correct Preds 36	Total Labels 36	Accuracy 36	Gen Accuracy 36
No log	0	0	1.6389	0.0061	706.9523	490.0220	66.0	299.0	0.2207	66.0	0.2207	62.0	62.0	64.0	0.9688	0.9688	0.0	0.0	73.0	0.0	0.0	4.0	4.0	78.0	0.0513	0.0513	0.0	0.0	83.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0
1.0321	1.0	15	1.4403	0.0061	621.3001	430.6524	115.0	299.0	0.3846	115.0	0.3846	41.0	41.0	64.0	0.6406	0.6406	42.0	42.0	73.0	0.5753	0.5753	16.0	16.0	78.0	0.2051	0.2051	16.0	16.0	83.0	0.1928	0.1928	0.0	0.0	1.0	0.0	0.0
0.8912	2.0	30	1.2259	0.0061	528.8111	366.5440	140.0	299.0	0.4682	140.0	0.4682	25.0	25.0	64.0	0.3906	0.3906	31.0	31.0	73.0	0.4247	0.4247	25.0	25.0	78.0	0.3205	0.3205	59.0	59.0	83.0	0.7108	0.7108	0.0	0.0	1.0	0.0	0.0
0.3355	3.0	45	1.4589	0.0061	629.3274	436.2165	147.0	299.0	0.4916	145.0	0.4849	27.0	27.0	64.0	0.4219	0.4219	43.0	43.0	73.0	0.5890	0.5890	32.0	32.0	78.0	0.4103	0.4103	43.0	45.0	83.0	0.5422	0.5181	0.0	0.0	1.0	0.0	0.0
0.1444	4.0	60	1.9825	0.0061	855.2000	592.7795	141.0	299.0	0.4716	139.0	0.4649	24.0	24.0	64.0	0.375	0.375	37.0	37.0	73.0	0.5068	0.5068	39.0	39.0	78.0	0.5	0.5	38.0	40.0	83.0	0.4819	0.4578	1.0	1.0	1.0	1.0	1.0
0.0438	5.0	75	3.1741	0.0061	1369.2046	949.0603	149.0	299.0	0.4983	145.0	0.4849	38.0	38.0	64.0	0.5938	0.5938	33.0	34.0	73.0	0.4658	0.4521	37.0	37.0	78.0	0.4744	0.4744	37.0	40.0	83.0	0.4819	0.4458	0.0	0.0	1.0	0.0	0.0
0.0613	6.0	90	5.2350	0.0061	2258.1845	1565.2542	142.0	299.0	0.4749	141.0	0.4716	15.0	15.0	64.0	0.2344	0.2344	43.0	43.0	73.0	0.5890	0.5890	47.0	47.0	78.0	0.6026	0.6026	36.0	37.0	83.0	0.4458	0.4337	0.0	0.0	1.0	0.0	0.0
0.0017	7.0	105	6.7149	0.0061	2896.5987	2007.7692	142.0	299.0	0.4749	141.0	0.4716	28.0	28.0	64.0	0.4375	0.4375	38.0	38.0	73.0	0.5205	0.5205	34.0	34.0	78.0	0.4359	0.4359	41.0	42.0	83.0	0.5060	0.4940	0.0	0.0	1.0	0.0	0.0
0.4981	8.0	120	6.7489	0.0061	2911.2262	2017.9082	128.0	299.0	0.4281	127.0	0.4247	19.0	19.0	64.0	0.2969	0.2969	42.0	42.0	73.0	0.5753	0.5753	47.0	47.0	78.0	0.6026	0.6026	19.0	20.0	83.0	0.2410	0.2289	0.0	0.0	1.0	0.0	0.0
0.0314	9.0	135	5.7793	0.0061	2492.9966	1728.0135	141.0	299.0	0.4716	141.0	0.4716	25.0	25.0	64.0	0.3906	0.3906	42.0	42.0	73.0	0.5753	0.5753	38.0	38.0	78.0	0.4872	0.4872	36.0	36.0	83.0	0.4337	0.4337	0.0	0.0	1.0	0.0	0.0
0.0031	10.0	150	4.2922	0.0061	1851.5266	1283.3804	139.0	299.0	0.4649	138.0	0.4615	26.0	27.0	64.0	0.4219	0.4062	37.0	37.0	73.0	0.5068	0.5068	38.0	38.0	78.0	0.4872	0.4872	37.0	37.0	83.0	0.4458	0.4458	0.0	0.0	1.0	0.0	0.0
0.0275	11.0	165	5.0434	0.0061	2175.5456	1507.9733	142.0	299.0	0.4749	142.0	0.4749	27.0	27.0	64.0	0.4219	0.4219	37.0	37.0	73.0	0.5068	0.5068	41.0	41.0	78.0	0.5256	0.5256	37.0	37.0	83.0	0.4458	0.4458	0.0	0.0	1.0	0.0	0.0
0.0001	12.0	180	5.1861	0.0061	2237.0880	1550.6312	144.0	299.0	0.4816	143.0	0.4783	28.0	28.0	64.0	0.4375	0.4375	37.0	37.0	73.0	0.5068	0.5068	39.0	39.0	78.0	0.5	0.5	39.0	40.0	83.0	0.4819	0.4699	0.0	0.0	1.0	0.0	0.0
0.089	13.0	195	4.9209	0.0061	2122.6968	1471.3413	142.0	299.0	0.4749	141.0	0.4716	32.0	32.0	64.0	0.5	0.5	34.0	34.0	73.0	0.4658	0.4658	37.0	37.0	78.0	0.4744	0.4744	38.0	39.0	83.0	0.4699	0.4578	0.0	0.0	1.0	0.0	0.0
0.0	14.0	210	5.1933	0.0061	2240.1909	1552.7820	141.0	299.0	0.4716	140.0	0.4682	31.0	31.0	64.0	0.4844	0.4844	35.0	35.0	73.0	0.4795	0.4795	37.0	37.0	78.0	0.4744	0.4744	37.0	38.0	83.0	0.4578	0.4458	0.0	0.0	1.0	0.0	0.0
0.0	15.0	225	5.3492	0.0061	2307.4562	1599.4067	143.0	299.0	0.4783	142.0	0.4749	30.0	30.0	64.0	0.4688	0.4688	35.0	35.0	73.0	0.4795	0.4795	39.0	39.0	78.0	0.5	0.5	38.0	39.0	83.0	0.4699	0.4578	0.0	0.0	1.0	0.0	0.0
0.0	16.0	240	5.4363	0.0061	2345.0356	1625.4548	143.0	299.0	0.4783	142.0	0.4749	30.0	30.0	64.0	0.4688	0.4688	36.0	36.0	73.0	0.4932	0.4932	38.0	38.0	78.0	0.4872	0.4872	38.0	39.0	83.0	0.4699	0.4578	0.0	0.0	1.0	0.0	0.0

Framework versions

Transformers 4.51.3
Pytorch 2.6.0+cu124
Datasets 3.5.0
Tokenizers 0.21.1

donoway/ARC-Challenge_Llama-3.2-1B-31bpnw48

ARC-Challenge_Llama-3.2-1B-31bpnw48

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

donoway/
ARC-Challenge_Llama-3.2-1B-31bpnw48