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: constant
lr_scheduler_warmup_ratio: 0.001
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	0.0059	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
1.0683	1.0	6	0.9749	0.0059	801.7082	555.7018	373.0	570.0	0.6544	228.0	0.4	48.0	107.0	158.0	0.6772	0.3038	48.0	85.0	152.0	0.5592	0.3158	71.0	97.0	142.0	0.6831	0.5	61.0	84.0	118.0	0.7119	0.5169
0.5277	2.0	12	1.1013	0.0059	905.6689	627.7619	395.0	570.0	0.6930	391.0	0.6860	95.0	97.0	158.0	0.6139	0.6013	120.0	122.0	152.0	0.8026	0.7895	98.0	98.0	142.0	0.6901	0.6901	78.0	78.0	118.0	0.6610	0.6610
0.3604	3.0	18	1.1988	0.0059	985.8352	683.3289	401.0	570.0	0.7035	401.0	0.7035	112.0	112.0	158.0	0.7089	0.7089	119.0	119.0	152.0	0.7829	0.7829	92.0	92.0	142.0	0.6479	0.6479	78.0	78.0	118.0	0.6610	0.6610
0.1435	4.0	24	1.7570	0.0059	1444.8662	1001.5049	388.0	570.0	0.6807	341.0	0.5982	70.0	93.0	158.0	0.5886	0.4430	113.0	126.0	152.0	0.8289	0.7434	81.0	87.0	142.0	0.6127	0.5704	77.0	82.0	118.0	0.6949	0.6525
0.0169	5.0	30	2.2097	0.0059	1817.1203	1259.5318	395.0	570.0	0.6930	340.0	0.5965	79.0	102.0	158.0	0.6456	0.5	99.0	121.0	152.0	0.7961	0.6513	88.0	94.0	142.0	0.6620	0.6197	74.0	78.0	118.0	0.6610	0.6271
0.0068	6.0	36	2.5137	0.0059	2067.1196	1432.8181	394.0	570.0	0.6912	391.0	0.6860	102.0	105.0	158.0	0.6646	0.6456	116.0	116.0	152.0	0.7632	0.7632	99.0	99.0	142.0	0.6972	0.6972	74.0	74.0	118.0	0.6271	0.6271
0.0001	7.0	42	3.2012	0.0059	2632.4308	1824.6620	399.0	570.0	0.7	368.0	0.6456	85.0	116.0	158.0	0.7342	0.5380	122.0	122.0	152.0	0.8026	0.8026	97.0	97.0	142.0	0.6831	0.6831	64.0	64.0	118.0	0.5424	0.5424
0.0005	8.0	48	3.2132	0.0059	2642.3392	1831.5300	400.0	570.0	0.7018	283.0	0.4965	15.0	110.0	158.0	0.6962	0.0949	109.0	125.0	152.0	0.8224	0.7171	97.0	101.0	142.0	0.7113	0.6831	62.0	64.0	118.0	0.5424	0.5254
0.0	9.0	54	3.6815	0.0059	3027.4329	2098.4566	395.0	570.0	0.6930	395.0	0.6930	107.0	107.0	158.0	0.6772	0.6772	122.0	122.0	152.0	0.8026	0.8026	101.0	101.0	142.0	0.7113	0.7113	65.0	65.0	118.0	0.5508	0.5508
0.0	10.0	60	3.7049	0.0059	3046.6547	2111.7801	394.0	570.0	0.6912	394.0	0.6912	106.0	106.0	158.0	0.6709	0.6709	121.0	121.0	152.0	0.7961	0.7961	102.0	102.0	142.0	0.7183	0.7183	65.0	65.0	118.0	0.5508	0.5508
0.0	11.0	66	3.6993	0.0059	3042.0740	2108.6050	392.0	570.0	0.6877	392.0	0.6877	106.0	106.0	158.0	0.6709	0.6709	121.0	121.0	152.0	0.7961	0.7961	101.0	101.0	142.0	0.7113	0.7113	64.0	64.0	118.0	0.5424	0.5424
0.0	12.0	72	3.7184	0.0059	3057.7976	2119.5038	393.0	570.0	0.6895	393.0	0.6895	104.0	104.0	158.0	0.6582	0.6582	122.0	122.0	152.0	0.8026	0.8026	102.0	102.0	142.0	0.7183	0.7183	65.0	65.0	118.0	0.5508	0.5508
0.0	13.0	78	3.7193	0.0059	3058.5522	2120.0269	394.0	570.0	0.6912	394.0	0.6912	106.0	106.0	158.0	0.6709	0.6709	120.0	120.0	152.0	0.7895	0.7895	103.0	103.0	142.0	0.7254	0.7254	65.0	65.0	118.0	0.5508	0.5508
0.0	14.0	84	3.7480	0.0059	3082.1203	2136.3630	392.0	570.0	0.6877	392.0	0.6877	102.0	102.0	158.0	0.6456	0.6456	122.0	122.0	152.0	0.8026	0.8026	103.0	103.0	142.0	0.7254	0.7254	65.0	65.0	118.0	0.5508	0.5508
0.0	15.0	90	3.7192	0.0059	3058.4020	2119.9227	395.0	570.0	0.6930	395.0	0.6930	106.0	106.0	158.0	0.6709	0.6709	121.0	121.0	152.0	0.7961	0.7961	103.0	103.0	142.0	0.7254	0.7254	65.0	65.0	118.0	0.5508	0.5508
0.0	16.0	96	3.7461	0.0059	3080.5849	2135.2987	393.0	570.0	0.6895	393.0	0.6895	104.0	104.0	158.0	0.6582	0.6582	122.0	122.0	152.0	0.8026	0.8026	103.0	103.0	142.0	0.7254	0.7254	64.0	64.0	118.0	0.5424	0.5424
0.0	17.0	102	3.7132	0.0059	3053.4611	2116.4979	393.0	570.0	0.6895	393.0	0.6895	105.0	105.0	158.0	0.6646	0.6646	121.0	121.0	152.0	0.7961	0.7961	102.0	102.0	142.0	0.7183	0.7183	65.0	65.0	118.0	0.5508	0.5508
0.0	18.0	108	3.7256	0.0059	3063.7148	2123.6053	394.0	570.0	0.6912	394.0	0.6912	105.0	105.0	158.0	0.6646	0.6646	122.0	122.0	152.0	0.8026	0.8026	102.0	102.0	142.0	0.7183	0.7183	65.0	65.0	118.0	0.5508	0.5508
0.0	19.0	114	3.7262	0.0059	3064.1976	2123.9400	394.0	570.0	0.6912	393.0	0.6895	105.0	105.0	158.0	0.6646	0.6646	120.0	121.0	152.0	0.7961	0.7895	103.0	103.0	142.0	0.7254	0.7254	65.0	65.0	118.0	0.5508	0.5508
0.0	20.0	120	3.7217	0.0059	3060.5267	2121.3955	395.0	570.0	0.6930	395.0	0.6930	105.0	105.0	158.0	0.6646	0.6646	122.0	122.0	152.0	0.8026	0.8026	103.0	103.0	142.0	0.7254	0.7254	65.0	65.0	118.0	0.5508	0.5508
0.0	21.0	126	3.7315	0.0059	3068.5119	2126.9304	391.0	570.0	0.6860	391.0	0.6860	105.0	105.0	158.0	0.6646	0.6646	120.0	120.0	152.0	0.7895	0.7895	101.0	101.0	142.0	0.7113	0.7113	65.0	65.0	118.0	0.5508	0.5508
0.0	22.0	132	3.7535	0.0059	3086.5992	2139.4675	391.0	570.0	0.6860	391.0	0.6860	103.0	103.0	158.0	0.6519	0.6519	121.0	121.0	152.0	0.7961	0.7961	102.0	102.0	142.0	0.7183	0.7183	65.0	65.0	118.0	0.5508	0.5508
0.0	23.0	138	3.7403	0.0059	3075.7688	2131.9605	393.0	570.0	0.6895	393.0	0.6895	105.0	105.0	158.0	0.6646	0.6646	122.0	122.0	152.0	0.8026	0.8026	101.0	101.0	142.0	0.7113	0.7113	65.0	65.0	118.0	0.5508	0.5508
0.0	24.0	144	3.7400	0.0059	3075.5425	2131.8036	392.0	570.0	0.6877	392.0	0.6877	104.0	104.0	158.0	0.6582	0.6582	121.0	121.0	152.0	0.7961	0.7961	103.0	103.0	142.0	0.7254	0.7254	64.0	64.0	118.0	0.5424	0.5424
0.0	25.0	150	3.7529	0.0059	3086.1470	2139.1541	393.0	570.0	0.6895	393.0	0.6895	105.0	105.0	158.0	0.6646	0.6646	121.0	121.0	152.0	0.7961	0.7961	103.0	103.0	142.0	0.7254	0.7254	64.0	64.0	118.0	0.5424	0.5424
0.0	26.0	156	3.7576	0.0059	3090.0174	2141.8369	393.0	570.0	0.6895	393.0	0.6895	104.0	104.0	158.0	0.6582	0.6582	121.0	121.0	152.0	0.7961	0.7961	103.0	103.0	142.0	0.7254	0.7254	65.0	65.0	118.0	0.5508	0.5508
0.0	27.0	162	3.7336	0.0059	3070.3089	2128.1760	391.0	570.0	0.6860	391.0	0.6860	105.0	105.0	158.0	0.6646	0.6646	119.0	119.0	152.0	0.7829	0.7829	102.0	102.0	142.0	0.7183	0.7183	65.0	65.0	118.0	0.5508	0.5508
0.0	28.0	168	3.7409	0.0059	3076.3039	2132.3314	393.0	570.0	0.6895	393.0	0.6895	104.0	104.0	158.0	0.6582	0.6582	122.0	122.0	152.0	0.8026	0.8026	102.0	102.0	142.0	0.7183	0.7183	65.0	65.0	118.0	0.5508	0.5508
0.0	29.0	174	3.7383	0.0059	3074.1601	2130.8454	394.0	570.0	0.6912	394.0	0.6912	104.0	104.0	158.0	0.6582	0.6582	122.0	122.0	152.0	0.8026	0.8026	103.0	103.0	142.0	0.7254	0.7254	65.0	65.0	118.0	0.5508	0.5508
0.0	30.0	180	3.7635	0.0059	3094.8925	2145.2160	393.0	570.0	0.6895	393.0	0.6895	105.0	105.0	158.0	0.6646	0.6646	121.0	121.0	152.0	0.7961	0.7961	102.0	102.0	142.0	0.7183	0.7183	65.0	65.0	118.0	0.5508	0.5508
0.0	31.0	186	3.7428	0.0059	3077.8653	2133.4136	395.0	570.0	0.6930	395.0	0.6930	105.0	105.0	158.0	0.6646	0.6646	122.0	122.0	152.0	0.8026	0.8026	103.0	103.0	142.0	0.7254	0.7254	65.0	65.0	118.0	0.5508	0.5508
0.0	32.0	192	3.7542	0.0059	3087.2230	2139.8999	392.0	570.0	0.6877	392.0	0.6877	104.0	104.0	158.0	0.6582	0.6582	121.0	121.0	152.0	0.7961	0.7961	102.0	102.0	142.0	0.7183	0.7183	65.0	65.0	118.0	0.5508	0.5508
0.0	33.0	198	3.7554	0.0059	3088.1625	2140.5511	390.0	570.0	0.6842	390.0	0.6842	103.0	103.0	158.0	0.6519	0.6519	121.0	121.0	152.0	0.7961	0.7961	102.0	102.0	142.0	0.7183	0.7183	64.0	64.0	118.0	0.5424	0.5424
0.0	34.0	204	3.7777	0.0059	3106.5325	2153.2843	392.0	570.0	0.6877	392.0	0.6877	105.0	105.0	158.0	0.6646	0.6646	121.0	121.0	152.0	0.7961	0.7961	102.0	102.0	142.0	0.7183	0.7183	64.0	64.0	118.0	0.5424	0.5424
0.0	35.0	210	3.7392	0.0059	3074.9190	2131.3715	395.0	570.0	0.6930	394.0	0.6912	105.0	105.0	158.0	0.6646	0.6646	122.0	123.0	152.0	0.8092	0.8026	102.0	102.0	142.0	0.7183	0.7183	65.0	65.0	118.0	0.5508	0.5508
0.0	36.0	216	3.7779	0.0059	3106.7230	2153.4163	392.0	570.0	0.6877	392.0	0.6877	105.0	105.0	158.0	0.6646	0.6646	121.0	121.0	152.0	0.7961	0.7961	102.0	102.0	142.0	0.7183	0.7183	64.0	64.0	118.0	0.5424	0.5424
0.0	37.0	222	3.7607	0.0059	3092.5676	2143.6045	392.0	570.0	0.6877	391.0	0.6860	104.0	104.0	158.0	0.6582	0.6582	120.0	121.0	152.0	0.7961	0.7895	103.0	103.0	142.0	0.7254	0.7254	64.0	64.0	118.0	0.5424	0.5424
0.0	38.0	228	3.7725	0.0059	3102.2551	2150.3194	394.0	570.0	0.6912	394.0	0.6912	105.0	105.0	158.0	0.6646	0.6646	121.0	121.0	152.0	0.7961	0.7961	103.0	103.0	142.0	0.7254	0.7254	65.0	65.0	118.0	0.5508	0.5508
0.0	39.0	234	3.7865	0.0059	3113.8017	2158.3229	395.0	570.0	0.6930	395.0	0.6930	106.0	106.0	158.0	0.6709	0.6709	121.0	121.0	152.0	0.7961	0.7961	104.0	104.0	142.0	0.7324	0.7324	64.0	64.0	118.0	0.5424	0.5424
0.0	40.0	240	3.7868	0.0059	3114.0347	2158.4843	392.0	570.0	0.6877	392.0	0.6877	105.0	105.0	158.0	0.6646	0.6646	121.0	121.0	152.0	0.7961	0.7961	101.0	101.0	142.0	0.7113	0.7113	65.0	65.0	118.0	0.5508	0.5508
0.0	41.0	246	3.7816	0.0059	3109.7744	2155.5314	396.0	570.0	0.6947	396.0	0.6947	106.0	106.0	158.0	0.6709	0.6709	121.0	121.0	152.0	0.7961	0.7961	104.0	104.0	142.0	0.7324	0.7324	65.0	65.0	118.0	0.5508	0.5508
0.0	42.0	252	3.7873	0.0059	3114.4142	2158.7474	393.0	570.0	0.6895	393.0	0.6895	105.0	105.0	158.0	0.6646	0.6646	120.0	120.0	152.0	0.7895	0.7895	103.0	103.0	142.0	0.7254	0.7254	65.0	65.0	118.0	0.5508	0.5508
0.0	43.0	258	3.7803	0.0059	3108.6988	2154.7858	393.0	570.0	0.6895	393.0	0.6895	104.0	104.0	158.0	0.6582	0.6582	122.0	122.0	152.0	0.8026	0.8026	103.0	103.0	142.0	0.7254	0.7254	64.0	64.0	118.0	0.5424	0.5424
0.0	44.0	264	3.7862	0.0059	3113.5669	2158.1601	395.0	570.0	0.6930	395.0	0.6930	106.0	106.0	158.0	0.6709	0.6709	120.0	120.0	152.0	0.7895	0.7895	104.0	104.0	142.0	0.7324	0.7324	65.0	65.0	118.0	0.5508	0.5508
0.0	45.0	270	3.7778	0.0059	3106.6010	2153.3317	392.0	570.0	0.6877	391.0	0.6860	104.0	104.0	158.0	0.6582	0.6582	121.0	122.0	152.0	0.8026	0.7961	102.0	102.0	142.0	0.7183	0.7183	64.0	64.0	118.0	0.5424	0.5424
0.0	46.0	276	3.7831	0.0059	3110.9460	2156.3435	392.0	570.0	0.6877	392.0	0.6877	104.0	104.0	158.0	0.6582	0.6582	121.0	121.0	152.0	0.7961	0.7961	102.0	102.0	142.0	0.7183	0.7183	65.0	65.0	118.0	0.5508	0.5508
0.0	47.0	282	3.7771	0.0059	3106.0288	2152.9351	394.0	570.0	0.6912	394.0	0.6912	104.0	104.0	158.0	0.6582	0.6582	122.0	122.0	152.0	0.8026	0.8026	104.0	104.0	142.0	0.7324	0.7324	64.0	64.0	118.0	0.5424	0.5424
0.0	48.0	288	3.8045	0.0059	3128.5739	2168.5621	393.0	570.0	0.6895	393.0	0.6895	104.0	104.0	158.0	0.6582	0.6582	121.0	121.0	152.0	0.7961	0.7961	103.0	103.0	142.0	0.7254	0.7254	65.0	65.0	118.0	0.5508	0.5508
0.0	49.0	294	3.8032	0.0059	3127.5086	2167.8238	392.0	570.0	0.6877	392.0	0.6877	104.0	104.0	158.0	0.6582	0.6582	121.0	121.0	152.0	0.7961	0.7961	102.0	102.0	142.0	0.7183	0.7183	65.0	65.0	118.0	0.5508	0.5508
0.0	50.0	300	3.8088	0.0059	3132.1456	2171.0379	393.0	570.0	0.6895	392.0	0.6877	105.0	105.0	158.0	0.6646	0.6646	120.0	121.0	152.0	0.7961	0.7895	102.0	102.0	142.0	0.7183	0.7183	65.0	65.0	118.0	0.5508	0.5508
0.0	51.0	306	3.8009	0.0059	3125.6343	2166.5246	394.0	570.0	0.6912	394.0	0.6912	106.0	106.0	158.0	0.6709	0.6709	120.0	120.0	152.0	0.7895	0.7895	103.0	103.0	142.0	0.7254	0.7254	65.0	65.0	118.0	0.5508	0.5508
0.0	52.0	312	3.7764	0.0059	3105.5095	2152.5751	393.0	570.0	0.6895	392.0	0.6877	106.0	106.0	158.0	0.6709	0.6709	119.0	120.0	152.0	0.7895	0.7829	102.0	102.0	142.0	0.7183	0.7183	65.0	65.0	118.0	0.5508	0.5508
0.0	53.0	318	3.8320	0.0059	3151.1936	2184.2410	392.0	570.0	0.6877	392.0	0.6877	104.0	104.0	158.0	0.6582	0.6582	121.0	121.0	152.0	0.7961	0.7961	102.0	102.0	142.0	0.7183	0.7183	65.0	65.0	118.0	0.5508	0.5508

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-9glx6cni

ARC-Easy_Llama-3.2-1B-9glx6cni

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

donoway/
ARC-Easy_Llama-3.2-1B-9glx6cni