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
1.3992	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
1.3992	2.0	2	2.7006	0.0063	2220.8218	1539.3563	202.0	570.0	0.3544	202.0	0.3544	0.0	0.0	158.0	0.0	0.0	62.0	62.0	152.0	0.4079	0.4079	140.0	140.0	142.0	0.9859	0.9859	0.0	0.0	118.0	0.0	0.0
1.9024	3.0	3	1.3172	0.0063	1083.1641	750.7922	190.0	570.0	0.3333	190.0	0.3333	9.0	9.0	158.0	0.0570	0.0570	150.0	150.0	152.0	0.9868	0.9868	19.0	19.0	142.0	0.1338	0.1338	12.0	12.0	118.0	0.1017	0.1017
0.6103	4.0	4	1.4635	0.0063	1203.4512	834.1688	338.0	570.0	0.5930	336.0	0.5895	131.0	133.0	158.0	0.8418	0.8291	91.0	91.0	152.0	0.5987	0.5987	76.0	76.0	142.0	0.5352	0.5352	38.0	38.0	118.0	0.3220	0.3220
0.0428	5.0	5	2.2266	0.0063	1831.0526	1269.1889	351.0	570.0	0.6158	330.0	0.5789	121.0	136.0	158.0	0.8608	0.7658	94.0	95.0	152.0	0.625	0.6184	78.0	82.0	142.0	0.5775	0.5493	37.0	38.0	118.0	0.3220	0.3136
0.0003	6.0	6	2.6947	0.0063	2215.9451	1535.9761	347.0	570.0	0.6088	306.0	0.5368	110.0	133.0	158.0	0.8418	0.6962	88.0	92.0	152.0	0.6053	0.5789	71.0	83.0	142.0	0.5845	0.5	37.0	39.0	118.0	0.3305	0.3136
0.0	7.0	7	2.8748	0.0063	2364.0644	1638.6446	343.0	570.0	0.6018	278.0	0.4877	95.0	130.0	158.0	0.8228	0.6013	78.0	87.0	152.0	0.5724	0.5132	67.0	84.0	142.0	0.5915	0.4718	38.0	42.0	118.0	0.3559	0.3220
0.0	8.0	8	2.9759	0.0063	2447.1750	1696.2525	336.0	570.0	0.5895	259.0	0.4544	87.0	128.0	158.0	0.8101	0.5506	72.0	84.0	152.0	0.5526	0.4737	61.0	80.0	142.0	0.5634	0.4296	39.0	44.0	118.0	0.3729	0.3305
0.0	9.0	9	3.0029	0.0063	2469.3675	1711.6351	331.0	570.0	0.5807	236.0	0.4140	78.0	125.0	158.0	0.7911	0.4937	64.0	81.0	152.0	0.5329	0.4211	57.0	81.0	142.0	0.5704	0.4014	37.0	44.0	118.0	0.3729	0.3136
0.0	10.0	10	3.0291	0.0063	2490.9245	1726.5773	320.0	570.0	0.5614	221.0	0.3877	74.0	122.0	158.0	0.7722	0.4684	59.0	77.0	152.0	0.5066	0.3882	56.0	79.0	142.0	0.5563	0.3944	32.0	42.0	118.0	0.3559	0.2712
0.0	11.0	11	3.0620	0.0063	2518.0218	1745.3597	318.0	570.0	0.5579	213.0	0.3737	70.0	122.0	158.0	0.7722	0.4430	57.0	76.0	152.0	0.5	0.375	57.0	79.0	142.0	0.5563	0.4014	29.0	41.0	118.0	0.3475	0.2458
0.0	12.0	12	3.1331	0.0063	2576.4547	1785.8623	314.0	570.0	0.5509	208.0	0.3649	68.0	122.0	158.0	0.7722	0.4304	55.0	75.0	152.0	0.4934	0.3618	57.0	80.0	142.0	0.5634	0.4014	28.0	37.0	118.0	0.3136	0.2373
0.0	13.0	13	3.1756	0.0063	2611.4490	1810.1185	317.0	570.0	0.5561	205.0	0.3596	67.0	121.0	158.0	0.7658	0.4241	53.0	74.0	152.0	0.4868	0.3487	56.0	81.0	142.0	0.5704	0.3944	29.0	41.0	118.0	0.3475	0.2458
0.0	14.0	14	3.1954	0.0063	2627.6804	1821.3693	313.0	570.0	0.5491	202.0	0.3544	67.0	121.0	158.0	0.7658	0.4241	53.0	73.0	152.0	0.4803	0.3487	55.0	79.0	142.0	0.5563	0.3873	27.0	40.0	118.0	0.3390	0.2288
0.0	15.0	15	3.2397	0.0063	2664.1020	1846.6148	314.0	570.0	0.5509	200.0	0.3509	64.0	121.0	158.0	0.7658	0.4051	53.0	73.0	152.0	0.4803	0.3487	56.0	81.0	142.0	0.5704	0.3944	27.0	39.0	118.0	0.3305	0.2288
0.0	16.0	16	3.2887	0.0063	2704.4351	1874.5716	314.0	570.0	0.5509	198.0	0.3474	64.0	121.0	158.0	0.7658	0.4051	53.0	71.0	152.0	0.4671	0.3487	55.0	82.0	142.0	0.5775	0.3873	26.0	40.0	118.0	0.3390	0.2203
0.0	17.0	17	3.3231	0.0063	2732.6981	1894.1620	311.0	570.0	0.5456	196.0	0.3439	64.0	121.0	158.0	0.7658	0.4051	51.0	70.0	152.0	0.4605	0.3355	54.0	80.0	142.0	0.5634	0.3803	27.0	40.0	118.0	0.3390	0.2288
0.0	18.0	18	3.3377	0.0063	2744.7374	1902.5070	310.0	570.0	0.5439	196.0	0.3439	64.0	121.0	158.0	0.7658	0.4051	52.0	69.0	152.0	0.4539	0.3421	53.0	80.0	142.0	0.5634	0.3732	27.0	40.0	118.0	0.3390	0.2288
0.0	19.0	19	3.3610	0.0063	2763.8330	1915.7430	309.0	570.0	0.5421	197.0	0.3456	64.0	122.0	158.0	0.7722	0.4051	51.0	69.0	152.0	0.4539	0.3355	56.0	79.0	142.0	0.5563	0.3944	26.0	39.0	118.0	0.3305	0.2203
0.0	20.0	20	3.3848	0.0063	2783.4671	1929.3524	311.0	570.0	0.5456	198.0	0.3474	66.0	123.0	158.0	0.7785	0.4177	51.0	68.0	152.0	0.4474	0.3355	54.0	80.0	142.0	0.5634	0.3803	27.0	40.0	118.0	0.3390	0.2288
0.0	21.0	21	3.3561	0.0063	2759.8295	1912.9680	312.0	570.0	0.5474	200.0	0.3509	67.0	123.0	158.0	0.7785	0.4241	53.0	68.0	152.0	0.4474	0.3487	53.0	81.0	142.0	0.5704	0.3732	27.0	40.0	118.0	0.3390	0.2288
0.0	22.0	22	3.4079	0.0063	2802.4235	1942.4919	311.0	570.0	0.5456	197.0	0.3456	68.0	123.0	158.0	0.7785	0.4304	49.0	70.0	152.0	0.4605	0.3224	53.0	79.0	142.0	0.5563	0.3732	27.0	39.0	118.0	0.3305	0.2288
0.0	23.0	23	3.4059	0.0063	2800.7869	1941.3575	313.0	570.0	0.5491	198.0	0.3474	67.0	122.0	158.0	0.7722	0.4241	51.0	70.0	152.0	0.4605	0.3355	53.0	81.0	142.0	0.5704	0.3732	27.0	40.0	118.0	0.3390	0.2288
0.0	24.0	24	3.4307	0.0063	2821.1525	1955.4739	312.0	570.0	0.5474	198.0	0.3474	67.0	122.0	158.0	0.7722	0.4241	50.0	69.0	152.0	0.4539	0.3289	53.0	80.0	142.0	0.5634	0.3732	28.0	41.0	118.0	0.3475	0.2373
0.0	25.0	25	3.4314	0.0063	2821.7596	1955.8947	312.0	570.0	0.5474	199.0	0.3491	67.0	122.0	158.0	0.7722	0.4241	51.0	69.0	152.0	0.4539	0.3355	54.0	80.0	142.0	0.5634	0.3803	27.0	41.0	118.0	0.3475	0.2288
0.0	26.0	26	3.4420	0.0063	2830.4716	1961.9334	313.0	570.0	0.5491	204.0	0.3579	69.0	122.0	158.0	0.7722	0.4367	51.0	70.0	152.0	0.4605	0.3355	55.0	80.0	142.0	0.5634	0.3873	29.0	41.0	118.0	0.3475	0.2458
0.0	27.0	27	3.4460	0.0063	2833.7589	1964.2120	312.0	570.0	0.5474	197.0	0.3456	66.0	122.0	158.0	0.7722	0.4177	51.0	68.0	152.0	0.4474	0.3355	53.0	81.0	142.0	0.5704	0.3732	27.0	41.0	118.0	0.3475	0.2288
0.0	28.0	28	3.4630	0.0063	2847.7515	1973.9109	313.0	570.0	0.5491	198.0	0.3474	68.0	123.0	158.0	0.7785	0.4304	49.0	69.0	152.0	0.4539	0.3224	52.0	80.0	142.0	0.5634	0.3662	29.0	41.0	118.0	0.3475	0.2458
0.0	29.0	29	3.4611	0.0063	2846.1980	1972.8341	312.0	570.0	0.5474	199.0	0.3491	68.0	122.0	158.0	0.7722	0.4304	50.0	69.0	152.0	0.4539	0.3289	52.0	80.0	142.0	0.5634	0.3662	29.0	41.0	118.0	0.3475	0.2458
0.0	30.0	30	3.4590	0.0063	2844.4834	1971.6457	310.0	570.0	0.5439	194.0	0.3404	68.0	122.0	158.0	0.7722	0.4304	49.0	68.0	152.0	0.4474	0.3224	51.0	80.0	142.0	0.5634	0.3592	26.0	40.0	118.0	0.3390	0.2203
0.0	31.0	31	3.4672	0.0063	2851.2328	1976.3240	310.0	570.0	0.5439	195.0	0.3421	67.0	123.0	158.0	0.7785	0.4241	51.0	68.0	152.0	0.4474	0.3355	51.0	81.0	142.0	0.5704	0.3592	26.0	38.0	118.0	0.3220	0.2203
0.0	32.0	32	3.4768	0.0063	2859.1086	1981.7830	309.0	570.0	0.5421	197.0	0.3456	67.0	121.0	158.0	0.7658	0.4241	50.0	68.0	152.0	0.4474	0.3289	54.0	80.0	142.0	0.5634	0.3803	26.0	40.0	118.0	0.3390	0.2203
0.0	33.0	33	3.4676	0.0063	2851.5542	1976.5468	312.0	570.0	0.5474	197.0	0.3456	67.0	122.0	158.0	0.7722	0.4241	49.0	68.0	152.0	0.4474	0.3224	53.0	81.0	142.0	0.5704	0.3732	28.0	41.0	118.0	0.3475	0.2373
0.0	34.0	34	3.4550	0.0063	2841.1333	1969.3235	310.0	570.0	0.5439	197.0	0.3456	69.0	122.0	158.0	0.7722	0.4367	49.0	68.0	152.0	0.4474	0.3224	53.0	80.0	142.0	0.5634	0.3732	26.0	40.0	118.0	0.3390	0.2203
0.0	35.0	35	3.4710	0.0063	2854.3484	1978.4835	311.0	570.0	0.5456	199.0	0.3491	69.0	123.0	158.0	0.7785	0.4367	51.0	69.0	152.0	0.4539	0.3355	53.0	80.0	142.0	0.5634	0.3732	26.0	39.0	118.0	0.3305	0.2203

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-7kenrtho

ARC-Easy_Llama-3.2-1B-7kenrtho

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

donoway/
ARC-Easy_Llama-3.2-1B-7kenrtho