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
No log	0	0	1.5354	0.0058	1262.6022	875.1692	172.0	570.0	0.3018	170.0	0.2982	154.0	154.0	158.0	0.9747	0.9747	0.0	0.0	152.0	0.0	0.0	15.0	17.0	142.0	0.1197	0.1056	1.0	1.0	118.0	0.0085	0.0085
0.6971	1.0	22	0.8506	0.0058	699.4864	484.8470	396.0	570.0	0.6947	396.0	0.6947	101.0	101.0	158.0	0.6392	0.6392	118.0	118.0	152.0	0.7763	0.7763	99.0	99.0	142.0	0.6972	0.6972	78.0	78.0	118.0	0.6610	0.6610
0.4332	2.0	44	0.7871	0.0058	647.2576	448.6448	415.0	570.0	0.7281	414.0	0.7263	103.0	104.0	158.0	0.6582	0.6519	110.0	110.0	152.0	0.7237	0.7237	110.0	110.0	142.0	0.7746	0.7746	91.0	91.0	118.0	0.7712	0.7712
0.2401	3.0	66	0.9065	0.0058	745.4412	516.7005	418.0	570.0	0.7333	418.0	0.7333	111.0	111.0	158.0	0.7025	0.7025	107.0	107.0	152.0	0.7039	0.7039	116.0	116.0	142.0	0.8169	0.8169	84.0	84.0	118.0	0.7119	0.7119
0.0789	4.0	88	1.7925	0.0058	1474.0667	1021.7452	420.0	570.0	0.7368	419.0	0.7351	107.0	108.0	158.0	0.6835	0.6772	121.0	121.0	152.0	0.7961	0.7961	110.0	110.0	142.0	0.7746	0.7746	81.0	81.0	118.0	0.6864	0.6864
0.2485	5.0	110	2.2855	0.0058	1879.4359	1302.7257	410.0	570.0	0.7193	409.0	0.7175	95.0	96.0	158.0	0.6076	0.6013	113.0	113.0	152.0	0.7434	0.7434	112.0	112.0	142.0	0.7887	0.7887	89.0	89.0	118.0	0.7542	0.7542
0.0003	6.0	132	2.3405	0.0058	1924.6842	1334.0894	423.0	570.0	0.7421	423.0	0.7421	104.0	104.0	158.0	0.6582	0.6582	119.0	119.0	152.0	0.7829	0.7829	114.0	114.0	142.0	0.8028	0.8028	86.0	86.0	118.0	0.7288	0.7288
0.0005	7.0	154	1.9032	0.0058	1565.0971	1084.8427	445.0	570.0	0.7807	445.0	0.7807	119.0	119.0	158.0	0.7532	0.7532	124.0	124.0	152.0	0.8158	0.8158	112.0	112.0	142.0	0.7887	0.7887	90.0	90.0	118.0	0.7627	0.7627
0.0362	8.0	176	1.8448	0.0058	1517.0063	1051.5086	432.0	570.0	0.7579	432.0	0.7579	117.0	117.0	158.0	0.7405	0.7405	129.0	129.0	152.0	0.8487	0.8487	102.0	102.0	142.0	0.7183	0.7183	84.0	84.0	118.0	0.7119	0.7119
0.0029	9.0	198	2.0551	0.0058	1689.9909	1171.4124	421.0	570.0	0.7386	421.0	0.7386	122.0	122.0	158.0	0.7722	0.7722	107.0	107.0	152.0	0.7039	0.7039	116.0	116.0	142.0	0.8169	0.8169	76.0	76.0	118.0	0.6441	0.6441
0.0001	10.0	220	2.4058	0.0058	1978.3357	1371.2778	418.0	570.0	0.7333	418.0	0.7333	112.0	112.0	158.0	0.7089	0.7089	114.0	114.0	152.0	0.75	0.75	114.0	114.0	142.0	0.8028	0.8028	78.0	78.0	118.0	0.6610	0.6610
0.0	11.0	242	2.3804	0.0058	1957.4865	1356.8263	421.0	570.0	0.7386	421.0	0.7386	121.0	121.0	158.0	0.7658	0.7658	117.0	117.0	152.0	0.7697	0.7697	109.0	109.0	142.0	0.7676	0.7676	74.0	74.0	118.0	0.6271	0.6271
0.0	12.0	264	2.4653	0.0058	2027.3270	1405.2360	418.0	570.0	0.7333	418.0	0.7333	120.0	120.0	158.0	0.7595	0.7595	117.0	117.0	152.0	0.7697	0.7697	109.0	109.0	142.0	0.7676	0.7676	72.0	72.0	118.0	0.6102	0.6102
0.0	13.0	286	2.4930	0.0058	2050.0709	1421.0009	419.0	570.0	0.7351	419.0	0.7351	121.0	121.0	158.0	0.7658	0.7658	118.0	118.0	152.0	0.7763	0.7763	108.0	108.0	142.0	0.7606	0.7606	72.0	72.0	118.0	0.6102	0.6102
0.0	14.0	308	2.5053	0.0058	2060.2326	1428.0444	419.0	570.0	0.7351	419.0	0.7351	121.0	121.0	158.0	0.7658	0.7658	118.0	118.0	152.0	0.7763	0.7763	108.0	108.0	142.0	0.7606	0.7606	72.0	72.0	118.0	0.6102	0.6102
0.0	15.0	330	2.5079	0.0058	2062.2961	1429.4747	422.0	570.0	0.7404	422.0	0.7404	122.0	122.0	158.0	0.7722	0.7722	118.0	118.0	152.0	0.7763	0.7763	109.0	109.0	142.0	0.7676	0.7676	73.0	73.0	118.0	0.6186	0.6186
0.0	16.0	352	2.5187	0.0058	2071.1980	1435.6450	417.0	570.0	0.7316	417.0	0.7316	121.0	121.0	158.0	0.7658	0.7658	116.0	116.0	152.0	0.7632	0.7632	108.0	108.0	142.0	0.7606	0.7606	72.0	72.0	118.0	0.6102	0.6102
0.0	17.0	374	2.5386	0.0058	2087.5588	1446.9855	419.0	570.0	0.7351	419.0	0.7351	121.0	121.0	158.0	0.7658	0.7658	117.0	117.0	152.0	0.7697	0.7697	108.0	108.0	142.0	0.7606	0.7606	73.0	73.0	118.0	0.6186	0.6186
0.0	18.0	396	2.5528	0.0058	2099.2200	1455.0684	420.0	570.0	0.7368	420.0	0.7368	121.0	121.0	158.0	0.7658	0.7658	118.0	118.0	152.0	0.7763	0.7763	108.0	108.0	142.0	0.7606	0.7606	73.0	73.0	118.0	0.6186	0.6186
0.0	19.0	418	2.5550	0.0058	2101.0822	1456.3592	421.0	570.0	0.7386	421.0	0.7386	121.0	121.0	158.0	0.7658	0.7658	119.0	119.0	152.0	0.7829	0.7829	109.0	109.0	142.0	0.7676	0.7676	72.0	72.0	118.0	0.6102	0.6102
0.0	20.0	440	2.5618	0.0058	2106.6324	1460.2063	422.0	570.0	0.7404	422.0	0.7404	122.0	122.0	158.0	0.7722	0.7722	118.0	118.0	152.0	0.7763	0.7763	109.0	109.0	142.0	0.7676	0.7676	73.0	73.0	118.0	0.6186	0.6186
0.0	21.0	462	2.5781	0.0058	2120.0371	1469.4977	421.0	570.0	0.7386	421.0	0.7386	122.0	122.0	158.0	0.7722	0.7722	117.0	117.0	152.0	0.7697	0.7697	109.0	109.0	142.0	0.7676	0.7676	73.0	73.0	118.0	0.6186	0.6186
0.0	22.0	484	2.5808	0.0058	2122.3059	1471.0703	417.0	570.0	0.7316	417.0	0.7316	122.0	122.0	158.0	0.7722	0.7722	116.0	116.0	152.0	0.7632	0.7632	108.0	108.0	142.0	0.7606	0.7606	71.0	71.0	118.0	0.6017	0.6017
0.0	23.0	506	2.5933	0.0058	2132.6015	1478.2067	419.0	570.0	0.7351	419.0	0.7351	121.0	121.0	158.0	0.7658	0.7658	118.0	118.0	152.0	0.7763	0.7763	109.0	109.0	142.0	0.7676	0.7676	71.0	71.0	118.0	0.6017	0.6017

Framework versions

Transformers 4.51.3
Pytorch 2.6.0+cu124
Datasets 3.5.0
Tokenizers 0.21.1

donoway/ARC-Easy_Llama-3.2-1B-wgrqnyuw

ARC-Easy_Llama-3.2-1B-wgrqnyuw

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

donoway/
ARC-Easy_Llama-3.2-1B-wgrqnyuw