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	0.006	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
1.3552	1.0	1	1.5354	0.006	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
1.3552	2.0	2	2.4687	0.006	2030.1287	1407.1780	221.0	570.0	0.3877	221.0	0.3877	0.0	0.0	158.0	0.0	0.0	85.0	85.0	152.0	0.5592	0.5592	136.0	136.0	142.0	0.9577	0.9577	0.0	0.0	118.0	0.0	0.0
1.7603	3.0	3	1.2555	0.006	1032.4540	715.6426	291.0	570.0	0.5105	291.0	0.5105	98.0	98.0	158.0	0.6203	0.6203	130.0	130.0	152.0	0.8553	0.8553	40.0	40.0	142.0	0.2817	0.2817	23.0	23.0	118.0	0.1949	0.1949
0.4267	4.0	4	2.5733	0.006	2116.1258	1466.7867	261.0	570.0	0.4579	260.0	0.4561	151.0	152.0	158.0	0.9620	0.9557	39.0	39.0	152.0	0.2566	0.2566	42.0	42.0	142.0	0.2958	0.2958	28.0	28.0	118.0	0.2373	0.2373
0.0491	5.0	5	3.1596	0.006	2598.2545	1800.9728	284.0	570.0	0.4982	284.0	0.4982	151.0	151.0	158.0	0.9557	0.9557	56.0	56.0	152.0	0.3684	0.3684	50.0	50.0	142.0	0.3521	0.3521	27.0	27.0	118.0	0.2288	0.2288
0.0044	6.0	6	4.0391	0.006	3321.5305	2302.3095	262.0	570.0	0.4596	259.0	0.4544	151.0	152.0	158.0	0.9620	0.9557	41.0	41.0	152.0	0.2697	0.2697	44.0	45.0	142.0	0.3169	0.3099	23.0	24.0	118.0	0.2034	0.1949
0.0001	7.0	7	4.4151	0.006	3630.7350	2516.6338	253.0	570.0	0.4439	239.0	0.4193	144.0	152.0	158.0	0.9620	0.9114	36.0	38.0	152.0	0.25	0.2368	38.0	41.0	142.0	0.2887	0.2676	21.0	22.0	118.0	0.1864	0.1780
0.0	8.0	8	4.5569	0.006	3747.3361	2597.4554	250.0	570.0	0.4386	223.0	0.3912	135.0	154.0	158.0	0.9747	0.8544	35.0	38.0	152.0	0.25	0.2303	35.0	39.0	142.0	0.2746	0.2465	18.0	19.0	118.0	0.1610	0.1525
0.0	9.0	9	4.6453	0.006	3819.9784	2647.8072	247.0	570.0	0.4333	204.0	0.3579	123.0	152.0	158.0	0.9620	0.7785	33.0	39.0	152.0	0.2566	0.2171	31.0	37.0	142.0	0.2606	0.2183	17.0	19.0	118.0	0.1610	0.1441
0.0001	10.0	10	4.8047	0.006	3951.0414	2738.6532	242.0	570.0	0.4246	203.0	0.3561	123.0	152.0	158.0	0.9620	0.7785	35.0	39.0	152.0	0.2566	0.2303	30.0	33.0	142.0	0.2324	0.2113	15.0	18.0	118.0	0.1525	0.1271
0.0	11.0	11	5.0241	0.006	4131.5031	2863.7397	236.0	570.0	0.4140	201.0	0.3526	125.0	153.0	158.0	0.9684	0.7911	34.0	37.0	152.0	0.2434	0.2237	28.0	29.0	142.0	0.2042	0.1972	14.0	17.0	118.0	0.1441	0.1186
0.0	12.0	12	5.2229	0.006	4295.0154	2977.0778	235.0	570.0	0.4123	203.0	0.3561	129.0	154.0	158.0	0.9747	0.8165	32.0	36.0	152.0	0.2368	0.2105	28.0	29.0	142.0	0.2042	0.1972	14.0	16.0	118.0	0.1356	0.1186
0.0	13.0	13	5.3741	0.006	4419.3154	3063.2360	235.0	570.0	0.4123	202.0	0.3544	129.0	155.0	158.0	0.9810	0.8165	31.0	35.0	152.0	0.2303	0.2039	28.0	29.0	142.0	0.2042	0.1972	14.0	16.0	118.0	0.1356	0.1186
0.0	14.0	14	5.5052	0.006	4527.0926	3137.9415	235.0	570.0	0.4123	207.0	0.3632	135.0	156.0	158.0	0.9873	0.8544	31.0	35.0	152.0	0.2303	0.2039	27.0	28.0	142.0	0.1972	0.1901	14.0	16.0	118.0	0.1356	0.1186
0.0	15.0	15	5.5976	0.006	4603.0781	3190.6106	234.0	570.0	0.4105	207.0	0.3632	135.0	156.0	158.0	0.9873	0.8544	32.0	35.0	152.0	0.2303	0.2105	26.0	28.0	142.0	0.1972	0.1831	14.0	15.0	118.0	0.1271	0.1186
0.0	16.0	16	5.6853	0.006	4675.2022	3240.6032	228.0	570.0	0.4	206.0	0.3614	138.0	155.0	158.0	0.9810	0.8734	29.0	32.0	152.0	0.2105	0.1908	26.0	27.0	142.0	0.1901	0.1831	13.0	14.0	118.0	0.1186	0.1102
0.0	17.0	17	5.7800	0.006	4753.1165	3294.6093	228.0	570.0	0.4	207.0	0.3632	141.0	156.0	158.0	0.9873	0.8924	29.0	31.0	152.0	0.2039	0.1908	24.0	27.0	142.0	0.1901	0.1690	13.0	14.0	118.0	0.1186	0.1102
0.0	18.0	18	5.8437	0.006	4805.4763	3330.9024	227.0	570.0	0.3982	207.0	0.3632	141.0	156.0	158.0	0.9873	0.8924	29.0	30.0	152.0	0.1974	0.1908	24.0	27.0	142.0	0.1901	0.1690	13.0	14.0	118.0	0.1186	0.1102
0.0	19.0	19	5.9488	0.006	4891.9541	3390.8442	226.0	570.0	0.3965	206.0	0.3614	141.0	156.0	158.0	0.9873	0.8924	28.0	29.0	152.0	0.1908	0.1842	24.0	27.0	142.0	0.1901	0.1690	13.0	14.0	118.0	0.1186	0.1102
0.0	20.0	20	5.9804	0.006	4917.8580	3408.7994	226.0	570.0	0.3965	206.0	0.3614	141.0	156.0	158.0	0.9873	0.8924	28.0	29.0	152.0	0.1908	0.1842	24.0	27.0	142.0	0.1901	0.1690	13.0	14.0	118.0	0.1186	0.1102
0.0	21.0	21	6.0239	0.006	4953.6373	3433.5997	226.0	570.0	0.3965	206.0	0.3614	141.0	156.0	158.0	0.9873	0.8924	28.0	29.0	152.0	0.1908	0.1842	24.0	27.0	142.0	0.1901	0.1690	13.0	14.0	118.0	0.1186	0.1102
0.0	22.0	22	6.0758	0.006	4996.3676	3463.2181	225.0	570.0	0.3947	206.0	0.3614	141.0	156.0	158.0	0.9873	0.8924	28.0	29.0	152.0	0.1908	0.1842	24.0	26.0	142.0	0.1831	0.1690	13.0	14.0	118.0	0.1186	0.1102
0.0	23.0	23	6.0958	0.006	5012.8294	3474.6285	225.0	570.0	0.3947	207.0	0.3632	141.0	156.0	158.0	0.9873	0.8924	28.0	29.0	152.0	0.1908	0.1842	25.0	26.0	142.0	0.1831	0.1761	13.0	14.0	118.0	0.1186	0.1102
0.0	24.0	24	6.1508	0.006	5057.9994	3505.9380	225.0	570.0	0.3947	209.0	0.3667	144.0	156.0	158.0	0.9873	0.9114	28.0	29.0	152.0	0.1908	0.1842	24.0	26.0	142.0	0.1831	0.1690	13.0	14.0	118.0	0.1186	0.1102
0.0	25.0	25	6.1477	0.006	5055.4455	3504.1678	224.0	570.0	0.3930	208.0	0.3649	143.0	156.0	158.0	0.9873	0.9051	27.0	28.0	152.0	0.1842	0.1776	25.0	26.0	142.0	0.1831	0.1761	13.0	14.0	118.0	0.1186	0.1102
0.0	26.0	26	6.1921	0.006	5092.0041	3529.5083	224.0	570.0	0.3930	208.0	0.3649	144.0	156.0	158.0	0.9873	0.9114	26.0	27.0	152.0	0.1776	0.1711	25.0	27.0	142.0	0.1901	0.1761	13.0	14.0	118.0	0.1186	0.1102
0.0	27.0	27	6.2041	0.006	5101.8523	3536.3346	224.0	570.0	0.3930	208.0	0.3649	144.0	156.0	158.0	0.9873	0.9114	26.0	27.0	152.0	0.1776	0.1711	25.0	27.0	142.0	0.1901	0.1761	13.0	14.0	118.0	0.1186	0.1102
0.0	28.0	28	6.2060	0.006	5103.4059	3537.4114	225.0	570.0	0.3947	208.0	0.3649	144.0	156.0	158.0	0.9873	0.9114	27.0	28.0	152.0	0.1842	0.1776	24.0	27.0	142.0	0.1901	0.1690	13.0	14.0	118.0	0.1186	0.1102
0.0	29.0	29	6.2192	0.006	5114.2474	3544.9262	225.0	570.0	0.3947	209.0	0.3667	145.0	156.0	158.0	0.9873	0.9177	27.0	28.0	152.0	0.1842	0.1776	24.0	27.0	142.0	0.1901	0.1690	13.0	14.0	118.0	0.1186	0.1102
0.0	30.0	30	6.2327	0.006	5125.3556	3552.6258	221.0	570.0	0.3877	206.0	0.3614	144.0	156.0	158.0	0.9873	0.9114	26.0	27.0	152.0	0.1776	0.1711	23.0	25.0	142.0	0.1761	0.1620	13.0	13.0	118.0	0.1102	0.1102
0.0	31.0	31	6.2450	0.006	5135.5071	3559.6623	222.0	570.0	0.3895	206.0	0.3614	144.0	156.0	158.0	0.9873	0.9114	26.0	27.0	152.0	0.1776	0.1711	23.0	26.0	142.0	0.1831	0.1620	13.0	13.0	118.0	0.1102	0.1102
0.0	32.0	32	6.2478	0.006	5137.7630	3561.2259	224.0	570.0	0.3930	210.0	0.3684	146.0	156.0	158.0	0.9873	0.9241	27.0	28.0	152.0	0.1842	0.1776	24.0	26.0	142.0	0.1831	0.1690	13.0	14.0	118.0	0.1186	0.1102
0.0	33.0	33	6.2653	0.006	5152.1581	3571.2038	224.0	570.0	0.3930	209.0	0.3667	146.0	156.0	158.0	0.9873	0.9241	26.0	27.0	152.0	0.1776	0.1711	24.0	27.0	142.0	0.1901	0.1690	13.0	14.0	118.0	0.1186	0.1102

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-xl28q3hn

ARC-Easy_Llama-3.2-1B-xl28q3hn

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

donoway/
ARC-Easy_Llama-3.2-1B-xl28q3hn