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
1.4008	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
1.4008	2.0	2	2.1073	0.006	1732.9467	1201.1871	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
1.8703	3.0	3	1.4116	0.006	1160.7698	804.5843	221.0	570.0	0.3877	221.0	0.3877	128.0	128.0	158.0	0.8101	0.8101	13.0	13.0	152.0	0.0855	0.0855	0.0	0.0	142.0	0.0	0.0	80.0	80.0	118.0	0.6780	0.6780
0.8616	4.0	4	2.9120	0.006	2394.6752	1659.8624	229.0	570.0	0.4018	229.0	0.4018	156.0	156.0	158.0	0.9873	0.9873	9.0	9.0	152.0	0.0592	0.0592	36.0	36.0	142.0	0.2535	0.2535	28.0	28.0	118.0	0.2373	0.2373
0.8814	5.0	5	1.2337	0.006	1014.5261	703.2159	352.0	570.0	0.6175	351.0	0.6158	119.0	120.0	158.0	0.7595	0.7532	59.0	59.0	152.0	0.3882	0.3882	101.0	101.0	142.0	0.7113	0.7113	72.0	72.0	118.0	0.6102	0.6102
0.2693	6.0	6	1.3112	0.006	1078.2076	747.3565	359.0	570.0	0.6298	358.0	0.6281	58.0	59.0	158.0	0.3734	0.3671	112.0	112.0	152.0	0.7368	0.7368	110.0	110.0	142.0	0.7746	0.7746	78.0	78.0	118.0	0.6610	0.6610
0.1177	7.0	7	1.6180	0.006	1330.5221	922.2476	364.0	570.0	0.6386	363.0	0.6368	66.0	67.0	158.0	0.4241	0.4177	114.0	114.0	152.0	0.75	0.75	106.0	106.0	142.0	0.7465	0.7465	77.0	77.0	118.0	0.6525	0.6525
0.0215	8.0	8	2.3570	0.006	1938.2804	1343.5136	368.0	570.0	0.6456	368.0	0.6456	81.0	81.0	158.0	0.5127	0.5127	106.0	106.0	152.0	0.6974	0.6974	106.0	106.0	142.0	0.7465	0.7465	75.0	75.0	118.0	0.6356	0.6356
0.0005	9.0	9	3.1356	0.006	2578.5187	1787.2930	373.0	570.0	0.6544	373.0	0.6544	87.0	87.0	158.0	0.5506	0.5506	106.0	106.0	152.0	0.6974	0.6974	103.0	103.0	142.0	0.7254	0.7254	77.0	77.0	118.0	0.6525	0.6525
0.0	10.0	10	3.6755	0.006	3022.5058	2095.0413	373.0	570.0	0.6544	373.0	0.6544	94.0	94.0	158.0	0.5949	0.5949	106.0	106.0	152.0	0.6974	0.6974	99.0	99.0	142.0	0.6972	0.6972	74.0	74.0	118.0	0.6271	0.6271
0.0	11.0	11	4.0632	0.006	3341.3365	2316.0380	374.0	570.0	0.6561	374.0	0.6561	99.0	99.0	158.0	0.6266	0.6266	106.0	106.0	152.0	0.6974	0.6974	97.0	97.0	142.0	0.6831	0.6831	72.0	72.0	118.0	0.6102	0.6102
0.0	12.0	12	4.4189	0.006	3633.8424	2518.7876	377.0	570.0	0.6614	377.0	0.6614	108.0	108.0	158.0	0.6835	0.6835	106.0	106.0	152.0	0.6974	0.6974	91.0	91.0	142.0	0.6408	0.6408	72.0	72.0	118.0	0.6102	0.6102
0.0	13.0	13	4.6996	0.006	3864.6172	2678.7485	375.0	570.0	0.6579	375.0	0.6579	110.0	110.0	158.0	0.6962	0.6962	106.0	106.0	152.0	0.6974	0.6974	88.0	88.0	142.0	0.6197	0.6197	71.0	71.0	118.0	0.6017	0.6017
0.0	14.0	14	4.9383	0.006	4060.9810	2814.8576	377.0	570.0	0.6614	377.0	0.6614	112.0	112.0	158.0	0.7089	0.7089	107.0	107.0	152.0	0.7039	0.7039	87.0	87.0	142.0	0.6127	0.6127	71.0	71.0	118.0	0.6017	0.6017
0.0	15.0	15	5.0850	0.006	4181.5466	2898.4273	377.0	570.0	0.6614	377.0	0.6614	113.0	113.0	158.0	0.7152	0.7152	107.0	107.0	152.0	0.7039	0.7039	86.0	86.0	142.0	0.6056	0.6056	71.0	71.0	118.0	0.6017	0.6017
0.0	16.0	16	5.2052	0.006	4280.4252	2966.9647	376.0	570.0	0.6596	376.0	0.6596	115.0	115.0	158.0	0.7278	0.7278	106.0	106.0	152.0	0.6974	0.6974	85.0	85.0	142.0	0.5986	0.5986	70.0	70.0	118.0	0.5932	0.5932
0.0	17.0	17	5.3576	0.006	4405.7475	3053.8314	375.0	570.0	0.6579	375.0	0.6579	115.0	115.0	158.0	0.7278	0.7278	105.0	105.0	152.0	0.6908	0.6908	85.0	85.0	142.0	0.5986	0.5986	70.0	70.0	118.0	0.5932	0.5932
0.0	18.0	18	5.4076	0.006	4446.8326	3082.3095	377.0	570.0	0.6614	377.0	0.6614	117.0	117.0	158.0	0.7405	0.7405	105.0	105.0	152.0	0.6908	0.6908	85.0	85.0	142.0	0.5986	0.5986	70.0	70.0	118.0	0.5932	0.5932
0.0	19.0	19	5.4880	0.006	4512.9806	3128.1598	376.0	570.0	0.6596	376.0	0.6596	117.0	117.0	158.0	0.7405	0.7405	105.0	105.0	152.0	0.6908	0.6908	85.0	85.0	142.0	0.5986	0.5986	69.0	69.0	118.0	0.5847	0.5847
0.0	20.0	20	5.5562	0.006	4569.0443	3167.0202	376.0	570.0	0.6596	376.0	0.6596	117.0	117.0	158.0	0.7405	0.7405	107.0	107.0	152.0	0.7039	0.7039	84.0	84.0	142.0	0.5915	0.5915	68.0	68.0	118.0	0.5763	0.5763
0.0	21.0	21	5.5900	0.006	4596.8882	3186.3201	374.0	570.0	0.6561	374.0	0.6561	117.0	117.0	158.0	0.7405	0.7405	105.0	105.0	152.0	0.6908	0.6908	84.0	84.0	142.0	0.5915	0.5915	68.0	68.0	118.0	0.5763	0.5763
0.0	22.0	22	5.5906	0.006	4597.3878	3186.6664	373.0	570.0	0.6544	373.0	0.6544	117.0	117.0	158.0	0.7405	0.7405	104.0	104.0	152.0	0.6842	0.6842	83.0	83.0	142.0	0.5845	0.5845	69.0	69.0	118.0	0.5847	0.5847
0.0	23.0	23	5.6844	0.006	4674.4978	3240.1150	372.0	570.0	0.6526	372.0	0.6526	115.0	115.0	158.0	0.7278	0.7278	106.0	106.0	152.0	0.6974	0.6974	83.0	83.0	142.0	0.5845	0.5845	68.0	68.0	118.0	0.5763	0.5763
0.0	24.0	24	5.6873	0.006	4676.8816	3241.7673	373.0	570.0	0.6544	373.0	0.6544	116.0	116.0	158.0	0.7342	0.7342	106.0	106.0	152.0	0.6974	0.6974	83.0	83.0	142.0	0.5845	0.5845	68.0	68.0	118.0	0.5763	0.5763
0.0	25.0	25	5.7152	0.006	4699.8429	3257.6828	373.0	570.0	0.6544	373.0	0.6544	118.0	118.0	158.0	0.7468	0.7468	104.0	104.0	152.0	0.6842	0.6842	83.0	83.0	142.0	0.5845	0.5845	68.0	68.0	118.0	0.5763	0.5763
0.0	26.0	26	5.7321	0.006	4713.7448	3267.3190	372.0	570.0	0.6526	372.0	0.6526	116.0	116.0	158.0	0.7342	0.7342	105.0	105.0	152.0	0.6908	0.6908	83.0	83.0	142.0	0.5845	0.5845	68.0	68.0	118.0	0.5763	0.5763
0.0	27.0	27	5.7109	0.006	4696.2915	3255.2212	374.0	570.0	0.6561	374.0	0.6561	117.0	117.0	158.0	0.7405	0.7405	106.0	106.0	152.0	0.6974	0.6974	83.0	83.0	142.0	0.5845	0.5845	68.0	68.0	118.0	0.5763	0.5763
0.0	28.0	28	5.7248	0.006	4707.7021	3263.1304	374.0	570.0	0.6561	374.0	0.6561	118.0	118.0	158.0	0.7468	0.7468	105.0	105.0	152.0	0.6908	0.6908	82.0	82.0	142.0	0.5775	0.5775	69.0	69.0	118.0	0.5847	0.5847
0.0	29.0	29	5.7557	0.006	4733.0906	3280.7284	371.0	570.0	0.6509	371.0	0.6509	116.0	116.0	158.0	0.7342	0.7342	106.0	106.0	152.0	0.6974	0.6974	82.0	82.0	142.0	0.5775	0.5775	67.0	67.0	118.0	0.5678	0.5678
0.0	30.0	30	5.7547	0.006	4732.2990	3280.1797	374.0	570.0	0.6561	374.0	0.6561	117.0	117.0	158.0	0.7405	0.7405	107.0	107.0	152.0	0.7039	0.7039	82.0	82.0	142.0	0.5775	0.5775	68.0	68.0	118.0	0.5763	0.5763
0.0	31.0	31	5.7254	0.006	4708.2410	3263.5040	376.0	570.0	0.6596	376.0	0.6596	118.0	118.0	158.0	0.7468	0.7468	107.0	107.0	152.0	0.7039	0.7039	83.0	83.0	142.0	0.5845	0.5845	68.0	68.0	118.0	0.5763	0.5763
0.0	32.0	32	5.7587	0.006	4735.5646	3282.4433	370.0	570.0	0.6491	370.0	0.6491	117.0	117.0	158.0	0.7405	0.7405	103.0	103.0	152.0	0.6776	0.6776	82.0	82.0	142.0	0.5775	0.5775	68.0	68.0	118.0	0.5763	0.5763
0.0	33.0	33	5.7623	0.006	4738.5261	3284.4960	373.0	570.0	0.6544	373.0	0.6544	118.0	118.0	158.0	0.7468	0.7468	105.0	105.0	152.0	0.6908	0.6908	82.0	82.0	142.0	0.5775	0.5775	68.0	68.0	118.0	0.5763	0.5763
0.0	34.0	34	5.7341	0.006	4715.3508	3268.4321	375.0	570.0	0.6579	375.0	0.6579	119.0	119.0	158.0	0.7532	0.7532	105.0	105.0	152.0	0.6908	0.6908	83.0	83.0	142.0	0.5845	0.5845	68.0	68.0	118.0	0.5763	0.5763
0.0	35.0	35	5.7825	0.006	4755.1337	3296.0075	371.0	570.0	0.6509	371.0	0.6509	117.0	117.0	158.0	0.7405	0.7405	104.0	104.0	152.0	0.6842	0.6842	82.0	82.0	142.0	0.5775	0.5775	68.0	68.0	118.0	0.5763	0.5763
0.0	36.0	36	5.7274	0.006	4709.8409	3264.6129	373.0	570.0	0.6544	373.0	0.6544	118.0	118.0	158.0	0.7468	0.7468	104.0	104.0	152.0	0.6842	0.6842	83.0	83.0	142.0	0.5845	0.5845	68.0	68.0	118.0	0.5763	0.5763
0.0	37.0	37	5.7777	0.006	4751.2280	3293.3003	370.0	570.0	0.6491	370.0	0.6491	117.0	117.0	158.0	0.7405	0.7405	103.0	103.0	152.0	0.6776	0.6776	82.0	82.0	142.0	0.5775	0.5775	68.0	68.0	118.0	0.5763	0.5763
0.0	38.0	38	5.7209	0.006	4704.5101	3260.9179	373.0	570.0	0.6544	373.0	0.6544	117.0	117.0	158.0	0.7405	0.7405	105.0	105.0	152.0	0.6908	0.6908	83.0	83.0	142.0	0.5845	0.5845	68.0	68.0	118.0	0.5763	0.5763
0.0	39.0	39	5.7626	0.006	4738.7797	3284.6718	373.0	570.0	0.6544	373.0	0.6544	118.0	118.0	158.0	0.7468	0.7468	105.0	105.0	152.0	0.6908	0.6908	82.0	82.0	142.0	0.5775	0.5775	68.0	68.0	118.0	0.5763	0.5763
0.0	40.0	40	5.7875	0.006	4759.2782	3298.8802	374.0	570.0	0.6561	374.0	0.6561	118.0	118.0	158.0	0.7468	0.7468	106.0	106.0	152.0	0.6974	0.6974	82.0	82.0	142.0	0.5775	0.5775	68.0	68.0	118.0	0.5763	0.5763
0.0	41.0	41	5.7475	0.006	4726.3976	3276.0891	376.0	570.0	0.6596	376.0	0.6596	120.0	120.0	158.0	0.7595	0.7595	106.0	106.0	152.0	0.6974	0.6974	82.0	82.0	142.0	0.5775	0.5775	68.0	68.0	118.0	0.5763	0.5763
0.0	42.0	42	5.7711	0.006	4745.7636	3289.5126	373.0	570.0	0.6544	373.0	0.6544	118.0	118.0	158.0	0.7468	0.7468	105.0	105.0	152.0	0.6908	0.6908	82.0	82.0	142.0	0.5775	0.5775	68.0	68.0	118.0	0.5763	0.5763

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-u85m8hv7

ARC-Easy_Llama-3.2-1B-u85m8hv7

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

donoway/
ARC-Easy_Llama-3.2-1B-u85m8hv7