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	0.0063	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
1.4003	1.0	1	1.5354	0.0063	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
1.4003	2.0	2	2.5540	0.0063	2100.2576	1455.7876	152.0	570.0	0.2667	152.0	0.2667	0.0	0.0	158.0	0.0	0.0	152.0	152.0	152.0	1.0	1.0	0.0	0.0	142.0	0.0	0.0	0.0	0.0	118.0	0.0	0.0
1.9783	3.0	3	1.7445	0.0063	1434.5302	994.3405	164.0	570.0	0.2877	164.0	0.2877	151.0	151.0	158.0	0.9557	0.9557	13.0	13.0	152.0	0.0855	0.0855	0.0	0.0	142.0	0.0	0.0	0.0	0.0	118.0	0.0	0.0
0.8352	4.0	4	1.1644	0.0063	957.5167	663.7000	270.0	570.0	0.4737	270.0	0.4737	76.0	76.0	158.0	0.4810	0.4810	18.0	18.0	152.0	0.1184	0.1184	120.0	120.0	142.0	0.8451	0.8451	56.0	56.0	118.0	0.4746	0.4746
0.5867	5.0	5	1.2507	0.0063	1028.4822	712.8896	284.0	570.0	0.4982	275.0	0.4825	131.0	135.0	158.0	0.8544	0.8291	16.0	16.0	152.0	0.1053	0.1053	80.0	82.0	142.0	0.5775	0.5634	48.0	51.0	118.0	0.4322	0.4068
0.1943	6.0	6	1.3948	0.0063	1147.0146	795.0499	344.0	570.0	0.6035	241.0	0.4228	82.0	128.0	158.0	0.8101	0.5190	56.0	74.0	152.0	0.4868	0.3684	69.0	90.0	142.0	0.6338	0.4859	34.0	52.0	118.0	0.4407	0.2881
0.018	7.0	7	1.8824	0.0063	1548.0051	1072.9954	387.0	570.0	0.6789	365.0	0.6404	106.0	118.0	158.0	0.7468	0.6709	105.0	110.0	152.0	0.7237	0.6908	95.0	98.0	142.0	0.6901	0.6690	59.0	61.0	118.0	0.5169	0.5
0.0005	8.0	8	2.4944	0.0063	2051.2357	1421.8082	394.0	570.0	0.6912	382.0	0.6702	109.0	116.0	158.0	0.7342	0.6899	114.0	117.0	152.0	0.7697	0.75	99.0	101.0	142.0	0.7113	0.6972	60.0	60.0	118.0	0.5085	0.5085
0.0	9.0	9	2.9139	0.0063	2396.2351	1660.9436	393.0	570.0	0.6895	388.0	0.6807	115.0	118.0	158.0	0.7468	0.7278	117.0	117.0	152.0	0.7697	0.7697	94.0	96.0	142.0	0.6761	0.6620	62.0	62.0	118.0	0.5254	0.5254
0.0	10.0	10	3.2474	0.0063	2670.4270	1850.9989	389.0	570.0	0.6825	384.0	0.6737	118.0	120.0	158.0	0.7595	0.7468	117.0	117.0	152.0	0.7697	0.7697	87.0	90.0	142.0	0.6338	0.6127	62.0	62.0	118.0	0.5254	0.5254
0.0	11.0	11	3.5037	0.0063	2881.2221	1997.1110	390.0	570.0	0.6842	385.0	0.6754	121.0	122.0	158.0	0.7722	0.7658	117.0	117.0	152.0	0.7697	0.7697	85.0	89.0	142.0	0.6268	0.5986	62.0	62.0	118.0	0.5254	0.5254
0.0	12.0	12	3.6605	0.0063	3010.1267	2086.4608	390.0	570.0	0.6842	385.0	0.6754	123.0	124.0	158.0	0.7848	0.7785	117.0	117.0	152.0	0.7697	0.7697	83.0	87.0	142.0	0.6127	0.5845	62.0	62.0	118.0	0.5254	0.5254
0.0	13.0	13	3.8304	0.0063	3149.8940	2183.3402	391.0	570.0	0.6860	384.0	0.6737	125.0	126.0	158.0	0.7975	0.7911	116.0	116.0	152.0	0.7632	0.7632	82.0	87.0	142.0	0.6127	0.5775	61.0	62.0	118.0	0.5254	0.5169
0.0	14.0	14	3.8963	0.0063	3204.0477	2220.8766	391.0	570.0	0.6860	384.0	0.6737	124.0	127.0	158.0	0.8038	0.7848	115.0	115.0	152.0	0.7566	0.7566	85.0	87.0	142.0	0.6127	0.5986	60.0	62.0	118.0	0.5254	0.5085
0.0	15.0	15	3.9921	0.0063	3282.8743	2275.5150	390.0	570.0	0.6842	383.0	0.6719	125.0	127.0	158.0	0.8038	0.7911	115.0	115.0	152.0	0.7566	0.7566	83.0	87.0	142.0	0.6127	0.5845	60.0	61.0	118.0	0.5169	0.5085
0.0	16.0	16	4.0738	0.0063	3350.0615	2322.0857	388.0	570.0	0.6807	380.0	0.6667	124.0	127.0	158.0	0.8038	0.7848	116.0	116.0	152.0	0.7632	0.7632	82.0	85.0	142.0	0.5986	0.5775	58.0	60.0	118.0	0.5085	0.4915
0.0	17.0	17	4.0949	0.0063	3367.3849	2334.0934	386.0	570.0	0.6772	378.0	0.6632	124.0	127.0	158.0	0.8038	0.7848	115.0	115.0	152.0	0.7566	0.7566	81.0	84.0	142.0	0.5915	0.5704	58.0	60.0	118.0	0.5085	0.4915
0.0	18.0	18	4.2008	0.0063	3454.4411	2394.4361	385.0	570.0	0.6754	376.0	0.6596	125.0	128.0	158.0	0.8101	0.7911	115.0	115.0	152.0	0.7566	0.7566	78.0	82.0	142.0	0.5775	0.5493	58.0	60.0	118.0	0.5085	0.4915
0.0	19.0	19	4.2113	0.0063	3463.0756	2400.4211	386.0	570.0	0.6772	378.0	0.6632	125.0	128.0	158.0	0.8101	0.7911	114.0	114.0	152.0	0.75	0.75	81.0	84.0	142.0	0.5915	0.5704	58.0	60.0	118.0	0.5085	0.4915
0.0	20.0	20	4.2743	0.0063	3514.9065	2436.3475	382.0	570.0	0.6702	374.0	0.6561	124.0	127.0	158.0	0.8038	0.7848	115.0	115.0	152.0	0.7566	0.7566	77.0	81.0	142.0	0.5704	0.5423	58.0	59.0	118.0	0.5	0.4915
0.0	21.0	21	4.2790	0.0063	3518.7559	2439.0157	378.0	570.0	0.6632	372.0	0.6526	124.0	127.0	158.0	0.8038	0.7848	114.0	114.0	152.0	0.75	0.75	79.0	81.0	142.0	0.5704	0.5563	55.0	56.0	118.0	0.4746	0.4661
0.0	22.0	22	4.3109	0.0063	3544.9824	2457.1946	381.0	570.0	0.6684	373.0	0.6544	125.0	128.0	158.0	0.8101	0.7911	114.0	114.0	152.0	0.75	0.75	78.0	81.0	142.0	0.5704	0.5493	56.0	58.0	118.0	0.4915	0.4746
0.0	23.0	23	4.3390	0.0063	3568.1153	2473.2290	380.0	570.0	0.6667	373.0	0.6544	124.0	127.0	158.0	0.8038	0.7848	114.0	114.0	152.0	0.75	0.75	79.0	82.0	142.0	0.5775	0.5563	56.0	57.0	118.0	0.4831	0.4746
0.0	24.0	24	4.3289	0.0063	3559.8267	2467.4839	380.0	570.0	0.6667	372.0	0.6526	124.0	127.0	158.0	0.8038	0.7848	115.0	115.0	152.0	0.7566	0.7566	77.0	81.0	142.0	0.5704	0.5423	56.0	57.0	118.0	0.4831	0.4746
0.0	25.0	25	4.3637	0.0063	3588.4182	2487.3020	379.0	570.0	0.6649	371.0	0.6509	124.0	127.0	158.0	0.8038	0.7848	114.0	114.0	152.0	0.75	0.75	78.0	82.0	142.0	0.5775	0.5493	55.0	56.0	118.0	0.4746	0.4661
0.0	26.0	26	4.3781	0.0063	3600.2845	2495.5270	378.0	570.0	0.6632	371.0	0.6509	124.0	127.0	158.0	0.8038	0.7848	113.0	113.0	152.0	0.7434	0.7434	78.0	81.0	142.0	0.5704	0.5493	56.0	57.0	118.0	0.4831	0.4746
0.0	27.0	27	4.3894	0.0063	3609.5398	2501.9423	377.0	570.0	0.6614	369.0	0.6474	124.0	127.0	158.0	0.8038	0.7848	113.0	113.0	152.0	0.7434	0.7434	77.0	81.0	142.0	0.5704	0.5423	55.0	56.0	118.0	0.4746	0.4661
0.0	28.0	28	4.3832	0.0063	3604.4250	2498.3971	378.0	570.0	0.6632	372.0	0.6526	124.0	126.0	158.0	0.7975	0.7848	113.0	113.0	152.0	0.7434	0.7434	79.0	81.0	142.0	0.5704	0.5563	56.0	58.0	118.0	0.4915	0.4746
0.0	29.0	29	4.3985	0.0063	3617.0423	2507.1427	379.0	570.0	0.6649	370.0	0.6491	124.0	127.0	158.0	0.8038	0.7848	114.0	114.0	152.0	0.75	0.75	77.0	81.0	142.0	0.5704	0.5423	55.0	57.0	118.0	0.4831	0.4661
0.0	30.0	30	4.4130	0.0063	3628.9597	2515.4032	378.0	570.0	0.6632	369.0	0.6474	125.0	128.0	158.0	0.8101	0.7911	112.0	112.0	152.0	0.7368	0.7368	77.0	81.0	142.0	0.5704	0.5423	55.0	57.0	118.0	0.4831	0.4661
0.0	31.0	31	4.3846	0.0063	3605.6251	2499.2288	377.0	570.0	0.6614	370.0	0.6491	124.0	127.0	158.0	0.8038	0.7848	113.0	113.0	152.0	0.7434	0.7434	78.0	81.0	142.0	0.5704	0.5493	55.0	56.0	118.0	0.4746	0.4661
0.0	32.0	32	4.4328	0.0063	3645.2685	2526.7076	378.0	570.0	0.6632	371.0	0.6509	124.0	127.0	158.0	0.8038	0.7848	113.0	113.0	152.0	0.7434	0.7434	78.0	81.0	142.0	0.5704	0.5493	56.0	57.0	118.0	0.4831	0.4746
0.0	33.0	33	4.4063	0.0063	3623.4620	2511.5925	379.0	570.0	0.6649	371.0	0.6509	124.0	127.0	158.0	0.8038	0.7848	114.0	114.0	152.0	0.75	0.75	78.0	82.0	142.0	0.5775	0.5493	55.0	56.0	118.0	0.4746	0.4661
0.0	34.0	34	4.4189	0.0063	3633.7879	2518.7499	378.0	570.0	0.6632	371.0	0.6509	124.0	127.0	158.0	0.8038	0.7848	114.0	114.0	152.0	0.75	0.75	78.0	81.0	142.0	0.5704	0.5493	55.0	56.0	118.0	0.4746	0.4661
0.0	35.0	35	4.4016	0.0063	3619.6355	2508.9402	379.0	570.0	0.6649	371.0	0.6509	124.0	127.0	158.0	0.8038	0.7848	113.0	113.0	152.0	0.7434	0.7434	78.0	82.0	142.0	0.5775	0.5493	56.0	57.0	118.0	0.4831	0.4746
0.0	36.0	36	4.4070	0.0063	3624.0410	2511.9938	378.0	570.0	0.6632	370.0	0.6491	124.0	127.0	158.0	0.8038	0.7848	113.0	113.0	152.0	0.7434	0.7434	78.0	81.0	142.0	0.5704	0.5493	55.0	57.0	118.0	0.4831	0.4661
0.0	37.0	37	4.4347	0.0063	3646.8202	2527.7831	375.0	570.0	0.6579	368.0	0.6456	123.0	126.0	158.0	0.7975	0.7785	113.0	113.0	152.0	0.7434	0.7434	77.0	80.0	142.0	0.5634	0.5423	55.0	56.0	118.0	0.4746	0.4661
0.0	38.0	38	4.4391	0.0063	3650.3984	2530.2633	375.0	570.0	0.6579	368.0	0.6456	124.0	127.0	158.0	0.8038	0.7848	112.0	112.0	152.0	0.7368	0.7368	77.0	80.0	142.0	0.5634	0.5423	55.0	56.0	118.0	0.4746	0.4661

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-w2bxj3e2

ARC-Easy_Llama-3.2-1B-w2bxj3e2

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

donoway/
ARC-Easy_Llama-3.2-1B-w2bxj3e2