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	0.0045	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
1.435	1.0	1	1.5354	0.0045	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
1.435	2.0	2	2.1175	0.0045	1741.3294	1206.9975	184.0	570.0	0.3228	184.0	0.3228	0.0	0.0	158.0	0.0	0.0	151.0	151.0	152.0	0.9934	0.9934	33.0	33.0	142.0	0.2324	0.2324	0.0	0.0	118.0	0.0	0.0
2.0061	3.0	3	1.2944	0.0045	1064.4094	737.7924	208.0	570.0	0.3649	208.0	0.3649	55.0	55.0	158.0	0.3481	0.3481	145.0	145.0	152.0	0.9539	0.9539	8.0	8.0	142.0	0.0563	0.0563	0.0	0.0	118.0	0.0	0.0
0.9135	4.0	4	2.0963	0.0045	1723.8711	1194.8964	203.0	570.0	0.3561	203.0	0.3561	154.0	154.0	158.0	0.9747	0.9747	3.0	3.0	152.0	0.0197	0.0197	35.0	35.0	142.0	0.2465	0.2465	11.0	11.0	118.0	0.0932	0.0932
0.5128	5.0	5	1.6210	0.0045	1333.0135	923.9746	319.0	570.0	0.5596	319.0	0.5596	141.0	141.0	158.0	0.8924	0.8924	60.0	60.0	152.0	0.3947	0.3947	80.0	80.0	142.0	0.5634	0.5634	38.0	38.0	118.0	0.3220	0.3220
0.1014	6.0	6	1.6826	0.0045	1383.6259	959.0564	368.0	570.0	0.6456	367.0	0.6439	106.0	107.0	158.0	0.6772	0.6709	113.0	113.0	152.0	0.7434	0.7434	93.0	93.0	142.0	0.6549	0.6549	55.0	55.0	118.0	0.4661	0.4661
0.0091	7.0	7	2.6261	0.0045	2159.4969	1496.8492	372.0	570.0	0.6526	372.0	0.6526	134.0	134.0	158.0	0.8481	0.8481	95.0	95.0	152.0	0.625	0.625	88.0	88.0	142.0	0.6197	0.6197	55.0	55.0	118.0	0.4661	0.4661
0.0	8.0	8	3.7800	0.0045	3108.3964	2154.5762	358.0	570.0	0.6281	358.0	0.6281	141.0	141.0	158.0	0.8924	0.8924	82.0	82.0	152.0	0.5395	0.5395	80.0	80.0	142.0	0.5634	0.5634	55.0	55.0	118.0	0.4661	0.4661
0.0	9.0	9	4.7384	0.0045	3896.5734	2700.8989	354.0	570.0	0.6211	354.0	0.6211	146.0	146.0	158.0	0.9241	0.9241	79.0	79.0	152.0	0.5197	0.5197	76.0	76.0	142.0	0.5352	0.5352	53.0	53.0	118.0	0.4492	0.4492
0.0	10.0	10	5.4116	0.0045	4450.1384	3084.6009	348.0	570.0	0.6105	348.0	0.6105	145.0	145.0	158.0	0.9177	0.9177	75.0	75.0	152.0	0.4934	0.4934	75.0	75.0	142.0	0.5282	0.5282	53.0	53.0	118.0	0.4492	0.4492
0.0	11.0	11	5.9142	0.0045	4863.4871	3371.1124	344.0	570.0	0.6035	344.0	0.6035	147.0	147.0	158.0	0.9304	0.9304	72.0	72.0	152.0	0.4737	0.4737	73.0	73.0	142.0	0.5141	0.5141	52.0	52.0	118.0	0.4407	0.4407
0.0	12.0	12	6.3347	0.0045	5209.2659	3610.7880	336.0	570.0	0.5895	336.0	0.5895	149.0	149.0	158.0	0.9430	0.9430	68.0	68.0	152.0	0.4474	0.4474	70.0	70.0	142.0	0.4930	0.4930	49.0	49.0	118.0	0.4153	0.4153
0.0	13.0	13	6.5582	0.0045	5393.0187	3738.1557	334.0	570.0	0.5860	334.0	0.5860	148.0	148.0	158.0	0.9367	0.9367	68.0	68.0	152.0	0.4474	0.4474	70.0	70.0	142.0	0.4930	0.4930	48.0	48.0	118.0	0.4068	0.4068
0.0	14.0	14	6.7692	0.0045	5566.5687	3858.4514	329.0	570.0	0.5772	329.0	0.5772	149.0	149.0	158.0	0.9430	0.9430	65.0	65.0	152.0	0.4276	0.4276	67.0	67.0	142.0	0.4718	0.4718	48.0	48.0	118.0	0.4068	0.4068
0.0	15.0	15	6.9534	0.0045	5718.0129	3963.4245	323.0	570.0	0.5667	323.0	0.5667	148.0	148.0	158.0	0.9367	0.9367	63.0	63.0	152.0	0.4145	0.4145	65.0	65.0	142.0	0.4577	0.4577	47.0	47.0	118.0	0.3983	0.3983
0.0	16.0	16	7.1324	0.0045	5865.2522	4065.4830	324.0	570.0	0.5684	324.0	0.5684	149.0	149.0	158.0	0.9430	0.9430	63.0	63.0	152.0	0.4145	0.4145	66.0	66.0	142.0	0.4648	0.4648	46.0	46.0	118.0	0.3898	0.3898
0.0	17.0	17	7.2239	0.0045	5940.4461	4117.6035	323.0	570.0	0.5667	323.0	0.5667	150.0	150.0	158.0	0.9494	0.9494	63.0	63.0	152.0	0.4145	0.4145	65.0	65.0	142.0	0.4577	0.4577	45.0	45.0	118.0	0.3814	0.3814
0.0	18.0	18	7.3212	0.0045	6020.4799	4173.0787	322.0	570.0	0.5649	322.0	0.5649	150.0	150.0	158.0	0.9494	0.9494	63.0	63.0	152.0	0.4145	0.4145	64.0	64.0	142.0	0.4507	0.4507	45.0	45.0	118.0	0.3814	0.3814
0.0	19.0	19	7.3806	0.0045	6069.3113	4206.9260	323.0	570.0	0.5667	323.0	0.5667	150.0	150.0	158.0	0.9494	0.9494	63.0	63.0	152.0	0.4145	0.4145	65.0	65.0	142.0	0.4577	0.4577	45.0	45.0	118.0	0.3814	0.3814
0.0	20.0	20	7.4461	0.0045	6123.1916	4244.2730	319.0	570.0	0.5596	319.0	0.5596	149.0	149.0	158.0	0.9430	0.9430	61.0	61.0	152.0	0.4013	0.4013	64.0	64.0	142.0	0.4507	0.4507	45.0	45.0	118.0	0.3814	0.3814
0.0	21.0	21	7.4628	0.0045	6136.9611	4253.8173	320.0	570.0	0.5614	320.0	0.5614	150.0	150.0	158.0	0.9494	0.9494	61.0	61.0	152.0	0.4013	0.4013	64.0	64.0	142.0	0.4507	0.4507	45.0	45.0	118.0	0.3814	0.3814
0.0	22.0	22	7.5301	0.0045	6192.2567	4292.1453	319.0	570.0	0.5596	319.0	0.5596	150.0	150.0	158.0	0.9494	0.9494	61.0	61.0	152.0	0.4013	0.4013	64.0	64.0	142.0	0.4507	0.4507	44.0	44.0	118.0	0.3729	0.3729
0.0	23.0	23	7.5338	0.0045	6195.2843	4294.2438	319.0	570.0	0.5596	319.0	0.5596	149.0	149.0	158.0	0.9430	0.9430	61.0	61.0	152.0	0.4013	0.4013	64.0	64.0	142.0	0.4507	0.4507	45.0	45.0	118.0	0.3814	0.3814
0.0	24.0	24	7.5580	0.0045	6215.2572	4308.0880	320.0	570.0	0.5614	320.0	0.5614	150.0	150.0	158.0	0.9494	0.9494	61.0	61.0	152.0	0.4013	0.4013	64.0	64.0	142.0	0.4507	0.4507	45.0	45.0	118.0	0.3814	0.3814
0.0	25.0	25	7.5289	0.0045	6191.2736	4291.4638	319.0	570.0	0.5596	319.0	0.5596	149.0	149.0	158.0	0.9430	0.9430	61.0	61.0	152.0	0.4013	0.4013	64.0	64.0	142.0	0.4507	0.4507	45.0	45.0	118.0	0.3814	0.3814
0.0	26.0	26	7.6133	0.0045	6260.6716	4339.5669	319.0	570.0	0.5596	319.0	0.5596	150.0	150.0	158.0	0.9494	0.9494	61.0	61.0	152.0	0.4013	0.4013	63.0	63.0	142.0	0.4437	0.4437	45.0	45.0	118.0	0.3814	0.3814
0.0	27.0	27	7.6015	0.0045	6250.9622	4332.8368	318.0	570.0	0.5579	318.0	0.5579	149.0	149.0	158.0	0.9430	0.9430	61.0	61.0	152.0	0.4013	0.4013	63.0	63.0	142.0	0.4437	0.4437	45.0	45.0	118.0	0.3814	0.3814
0.0	28.0	28	7.5912	0.0045	6242.5494	4327.0055	318.0	570.0	0.5579	318.0	0.5579	149.0	149.0	158.0	0.9430	0.9430	61.0	61.0	152.0	0.4013	0.4013	63.0	63.0	142.0	0.4437	0.4437	45.0	45.0	118.0	0.3814	0.3814
0.0	29.0	29	7.6034	0.0045	6252.5795	4333.9578	318.0	570.0	0.5579	318.0	0.5579	149.0	149.0	158.0	0.9430	0.9430	61.0	61.0	152.0	0.4013	0.4013	63.0	63.0	142.0	0.4437	0.4437	45.0	45.0	118.0	0.3814	0.3814
0.0	30.0	30	7.6187	0.0045	6265.1093	4342.6429	316.0	570.0	0.5544	316.0	0.5544	149.0	149.0	158.0	0.9430	0.9430	60.0	60.0	152.0	0.3947	0.3947	63.0	63.0	142.0	0.4437	0.4437	44.0	44.0	118.0	0.3729	0.3729
0.0	31.0	31	7.6249	0.0045	6270.2339	4346.1949	318.0	570.0	0.5579	318.0	0.5579	149.0	149.0	158.0	0.9430	0.9430	61.0	61.0	152.0	0.4013	0.4013	63.0	63.0	142.0	0.4437	0.4437	45.0	45.0	118.0	0.3814	0.3814
0.0	32.0	32	7.5821	0.0045	6235.0026	4321.7745	318.0	570.0	0.5579	318.0	0.5579	149.0	149.0	158.0	0.9430	0.9430	61.0	61.0	152.0	0.4013	0.4013	63.0	63.0	142.0	0.4437	0.4437	45.0	45.0	118.0	0.3814	0.3814
0.0	33.0	33	7.6294	0.0045	6273.9284	4348.7558	318.0	570.0	0.5579	318.0	0.5579	149.0	149.0	158.0	0.9430	0.9430	61.0	61.0	152.0	0.4013	0.4013	63.0	63.0	142.0	0.4437	0.4437	45.0	45.0	118.0	0.3814	0.3814
0.0	34.0	34	7.6133	0.0045	6260.6774	4339.5709	319.0	570.0	0.5596	319.0	0.5596	150.0	150.0	158.0	0.9494	0.9494	59.0	59.0	152.0	0.3882	0.3882	65.0	65.0	142.0	0.4577	0.4577	45.0	45.0	118.0	0.3814	0.3814
0.0	35.0	35	7.6097	0.0045	6257.7566	4337.5464	319.0	570.0	0.5596	319.0	0.5596	150.0	150.0	158.0	0.9494	0.9494	61.0	61.0	152.0	0.4013	0.4013	64.0	64.0	142.0	0.4507	0.4507	44.0	44.0	118.0	0.3729	0.3729
0.0	36.0	36	7.6376	0.0045	6280.6414	4353.4089	316.0	570.0	0.5544	316.0	0.5544	149.0	149.0	158.0	0.9430	0.9430	60.0	60.0	152.0	0.3947	0.3947	63.0	63.0	142.0	0.4437	0.4437	44.0	44.0	118.0	0.3729	0.3729
0.0	37.0	37	7.6398	0.0045	6282.5078	4354.7025	318.0	570.0	0.5579	318.0	0.5579	149.0	149.0	158.0	0.9430	0.9430	61.0	61.0	152.0	0.4013	0.4013	63.0	63.0	142.0	0.4437	0.4437	45.0	45.0	118.0	0.3814	0.3814

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-jlf8qw0w

ARC-Easy_Llama-3.2-1B-jlf8qw0w

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

donoway/
ARC-Easy_Llama-3.2-1B-jlf8qw0w