ARC-Easy_Llama-3.2-1B-l559dbas

This model is a fine-tuned version of meta-llama/Llama-3.2-1B on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 2.9879
• Model Preparation Time: 0.0056
• Mdl: 2457.0767
• Accumulated Loss: 1703.1158
• Correct Preds: 385.0
• Total Preds: 570.0
• Accuracy: 0.6754
• Correct Gen Preds: 384.0
• Gen Accuracy: 0.6737
• Correct Gen Preds 32: 103.0
• Correct Preds 32: 104.0
• Total Labels 32: 158.0
• Accuracy 32: 0.6582
• Gen Accuracy 32: 0.6519
• Correct Gen Preds 33: 109.0
• Correct Preds 33: 109.0
• Total Labels 33: 152.0
• Accuracy 33: 0.7171
• Gen Accuracy 33: 0.7171
• Correct Gen Preds 34: 94.0
• Correct Preds 34: 94.0
• Total Labels 34: 142.0
• Accuracy 34: 0.6620
• Gen Accuracy 34: 0.6620
• Correct Gen Preds 35: 78.0
• Correct Preds 35: 78.0
• Total Labels 35: 118.0
• Accuracy 35: 0.6610
• Gen Accuracy 35: 0.6610
• Correct Gen Preds 36: 0.0
• Correct Preds 36: 0.0
• Total Labels 36: 0.0
• Accuracy 36: 0.0
• Gen Accuracy 36: 0.0

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• 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	Correct Gen Preds 36	Correct Preds 36	Total Labels 36	Accuracy 36	Gen Accuracy 36
No log	0	0	1.5354	0.0056	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	0.0	0.0	0.0	0.0	0.0
1.4423	1.0	2	1.4479	0.0056	1190.6260	825.2790	153.0	570.0	0.2684	153.0	0.2684	0.0	0.0	158.0	0.0	0.0	152.0	152.0	152.0	1.0	1.0	1.0	1.0	142.0	0.0070	0.0070	0.0	0.0	118.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
1.0655	2.0	4	1.3378	0.0056	1100.1439	762.5616	241.0	570.0	0.4228	241.0	0.4228	29.0	29.0	158.0	0.1835	0.1835	38.0	38.0	152.0	0.25	0.25	60.0	60.0	142.0	0.4225	0.4225	114.0	114.0	118.0	0.9661	0.9661	0.0	0.0	0.0	0.0	0.0
0.4378	3.0	6	2.1706	0.0056	1784.9987	1237.2668	358.0	570.0	0.6281	357.0	0.6263	138.0	139.0	158.0	0.8797	0.8734	108.0	108.0	152.0	0.7105	0.7105	68.0	68.0	142.0	0.4789	0.4789	43.0	43.0	118.0	0.3644	0.3644	0.0	0.0	0.0	0.0	0.0
0.0303	4.0	8	2.2402	0.0056	1842.1844	1276.9049	383.0	570.0	0.6719	381.0	0.6684	116.0	118.0	158.0	0.7468	0.7342	114.0	114.0	152.0	0.75	0.75	87.0	87.0	142.0	0.6127	0.6127	64.0	64.0	118.0	0.5424	0.5424	0.0	0.0	0.0	0.0	0.0
0.0024	5.0	10	2.9879	0.0056	2457.0767	1703.1158	385.0	570.0	0.6754	384.0	0.6737	103.0	104.0	158.0	0.6582	0.6519	109.0	109.0	152.0	0.7171	0.7171	94.0	94.0	142.0	0.6620	0.6620	78.0	78.0	118.0	0.6610	0.6610	0.0	0.0	0.0	0.0	0.0
0.0061	6.0	12	3.9261	0.0056	3228.5722	2237.8757	373.0	570.0	0.6544	372.0	0.6526	110.0	111.0	158.0	0.7025	0.6962	100.0	100.0	152.0	0.6579	0.6579	89.0	89.0	142.0	0.6268	0.6268	73.0	73.0	118.0	0.6186	0.6186	0.0	0.0	0.0	0.0	0.0
0.0007	7.0	14	4.4608	0.0056	3668.3114	2542.6797	371.0	570.0	0.6509	370.0	0.6491	111.0	112.0	158.0	0.7089	0.7025	98.0	98.0	152.0	0.6447	0.6447	89.0	89.0	142.0	0.6268	0.6268	72.0	72.0	118.0	0.6102	0.6102	0.0	0.0	0.0	0.0	0.0
0.0	8.0	16	4.7296	0.0056	3889.3393	2695.8846	371.0	570.0	0.6509	368.0	0.6456	110.0	112.0	158.0	0.7089	0.6962	97.0	98.0	152.0	0.6447	0.6382	89.0	89.0	142.0	0.6268	0.6268	72.0	72.0	118.0	0.6102	0.6102	0.0	0.0	0.0	0.0	0.0
0.0	9.0	18	4.8297	0.0056	3971.6743	2752.9549	374.0	570.0	0.6561	369.0	0.6474	111.0	114.0	158.0	0.7215	0.7025	97.0	98.0	152.0	0.6447	0.6382	92.0	92.0	142.0	0.6479	0.6479	69.0	70.0	118.0	0.5932	0.5847	0.0	0.0	0.0	0.0	0.0
0.0	10.0	20	4.9249	0.0056	4049.8935	2807.1722	373.0	570.0	0.6544	367.0	0.6439	110.0	113.0	158.0	0.7152	0.6962	97.0	98.0	152.0	0.6447	0.6382	91.0	92.0	142.0	0.6479	0.6408	69.0	70.0	118.0	0.5932	0.5847	0.0	0.0	0.0	0.0	0.0
0.0	11.0	22	4.9434	0.0056	4065.1251	2817.7300	374.0	570.0	0.6561	365.0	0.6404	108.0	113.0	158.0	0.7152	0.6835	97.0	98.0	152.0	0.6447	0.6382	92.0	93.0	142.0	0.6549	0.6479	68.0	70.0	118.0	0.5932	0.5763	0.0	0.0	0.0	0.0	0.0
0.0	12.0	24	4.9699	0.0056	4086.9417	2832.8521	376.0	570.0	0.6596	366.0	0.6421	109.0	114.0	158.0	0.7215	0.6899	97.0	99.0	152.0	0.6513	0.6382	92.0	93.0	142.0	0.6549	0.6479	68.0	70.0	118.0	0.5932	0.5763	0.0	0.0	0.0	0.0	0.0
0.0	13.0	26	4.9692	0.0056	4086.3242	2832.4241	375.0	570.0	0.6579	366.0	0.6421	109.0	114.0	158.0	0.7215	0.6899	98.0	100.0	152.0	0.6579	0.6447	91.0	91.0	142.0	0.6408	0.6408	68.0	70.0	118.0	0.5932	0.5763	0.0	0.0	0.0	0.0	0.0
0.0	14.0	28	4.9771	0.0056	4092.8907	2836.9757	375.0	570.0	0.6579	366.0	0.6421	108.0	112.0	158.0	0.7089	0.6835	98.0	100.0	152.0	0.6579	0.6447	92.0	93.0	142.0	0.6549	0.6479	68.0	70.0	118.0	0.5932	0.5763	0.0	0.0	0.0	0.0	0.0
0.0	15.0	30	4.9850	0.0056	4099.3400	2841.4460	372.0	570.0	0.6526	364.0	0.6386	108.0	112.0	158.0	0.7089	0.6835	96.0	98.0	152.0	0.6447	0.6316	92.0	92.0	142.0	0.6479	0.6479	68.0	70.0	118.0	0.5932	0.5763	0.0	0.0	0.0	0.0	0.0
0.0	16.0	32	4.9839	0.0056	4098.4617	2840.8372	374.0	570.0	0.6561	366.0	0.6421	109.0	113.0	158.0	0.7152	0.6899	97.0	99.0	152.0	0.6513	0.6382	92.0	92.0	142.0	0.6479	0.6479	68.0	70.0	118.0	0.5932	0.5763	0.0	0.0	0.0	0.0	0.0
0.0	17.0	34	5.0025	0.0056	4113.7474	2851.4324	374.0	570.0	0.6561	366.0	0.6421	108.0	112.0	158.0	0.7089	0.6835	97.0	99.0	152.0	0.6513	0.6382	93.0	93.0	142.0	0.6549	0.6549	68.0	70.0	118.0	0.5932	0.5763	0.0	0.0	0.0	0.0	0.0
0.0	18.0	36	4.9950	0.0056	4107.5294	2847.1224	376.0	570.0	0.6596	367.0	0.6439	108.0	112.0	158.0	0.7089	0.6835	98.0	100.0	152.0	0.6579	0.6447	93.0	94.0	142.0	0.6620	0.6549	68.0	70.0	118.0	0.5932	0.5763	0.0	0.0	0.0	0.0	0.0
0.0	19.0	38	5.0231	0.0056	4130.7078	2863.1885	372.0	570.0	0.6526	364.0	0.6386	108.0	112.0	158.0	0.7089	0.6835	96.0	98.0	152.0	0.6447	0.6316	92.0	92.0	142.0	0.6479	0.6479	68.0	70.0	118.0	0.5932	0.5763	0.0	0.0	0.0	0.0	0.0
0.0	20.0	40	5.0244	0.0056	4131.7571	2863.9158	372.0	570.0	0.6526	365.0	0.6404	108.0	111.0	158.0	0.7025	0.6835	97.0	99.0	152.0	0.6513	0.6382	92.0	92.0	142.0	0.6479	0.6479	68.0	70.0	118.0	0.5932	0.5763	0.0	0.0	0.0	0.0	0.0
0.0	21.0	42	5.0174	0.0056	4125.9996	2859.9250	372.0	570.0	0.6526	364.0	0.6386	108.0	112.0	158.0	0.7089	0.6835	96.0	98.0	152.0	0.6447	0.6316	92.0	92.0	142.0	0.6479	0.6479	68.0	70.0	118.0	0.5932	0.5763	0.0	0.0	0.0	0.0	0.0
0.0	22.0	44	5.0179	0.0056	4126.3717	2860.1829	372.0	570.0	0.6526	364.0	0.6386	108.0	112.0	158.0	0.7089	0.6835	97.0	99.0	152.0	0.6513	0.6382	91.0	91.0	142.0	0.6408	0.6408	68.0	70.0	118.0	0.5932	0.5763	0.0	0.0	0.0	0.0	0.0
0.0	23.0	46	5.0296	0.0056	4136.0003	2866.8570	375.0	570.0	0.6579	366.0	0.6421	107.0	111.0	158.0	0.7025	0.6772	98.0	100.0	152.0	0.6579	0.6447	93.0	94.0	142.0	0.6620	0.6549	68.0	70.0	118.0	0.5932	0.5763	0.0	0.0	0.0	0.0	0.0
0.0	24.0	48	5.0417	0.0056	4145.9467	2873.7513	373.0	570.0	0.6544	363.0	0.6368	107.0	111.0	158.0	0.7025	0.6772	96.0	99.0	152.0	0.6513	0.6316	92.0	93.0	142.0	0.6549	0.6479	68.0	70.0	118.0	0.5932	0.5763	0.0	0.0	0.0	0.0	0.0
0.0	25.0	50	5.0072	0.0056	4117.5628	2854.0771	376.0	570.0	0.6596	367.0	0.6439	109.0	113.0	158.0	0.7152	0.6899	97.0	99.0	152.0	0.6513	0.6382	93.0	94.0	142.0	0.6620	0.6549	68.0	70.0	118.0	0.5932	0.5763	0.0	0.0	0.0	0.0	0.0
0.0	26.0	52	5.0587	0.0056	4159.9671	2883.4695	373.0	570.0	0.6544	364.0	0.6386	108.0	112.0	158.0	0.7089	0.6835	95.0	97.0	152.0	0.6382	0.625	93.0	94.0	142.0	0.6620	0.6549	68.0	70.0	118.0	0.5932	0.5763	0.0	0.0	0.0	0.0	0.0
0.0	27.0	54	5.0465	0.0056	4149.9418	2876.5205	371.0	570.0	0.6509	363.0	0.6368	108.0	112.0	158.0	0.7089	0.6835	97.0	99.0	152.0	0.6513	0.6382	90.0	90.0	142.0	0.6338	0.6338	68.0	70.0	118.0	0.5932	0.5763	0.0	0.0	0.0	0.0	0.0
0.0	28.0	56	5.0558	0.0056	4157.5306	2881.7806	372.0	570.0	0.6526	364.0	0.6386	108.0	112.0	158.0	0.7089	0.6835	96.0	98.0	152.0	0.6447	0.6316	92.0	92.0	142.0	0.6479	0.6479	68.0	70.0	118.0	0.5932	0.5763	0.0	0.0	0.0	0.0	0.0
0.0	29.0	58	5.0650	0.0056	4165.1732	2887.0781	373.0	570.0	0.6544	364.0	0.6386	108.0	112.0	158.0	0.7089	0.6835	96.0	98.0	152.0	0.6447	0.6316	92.0	93.0	142.0	0.6549	0.6479	68.0	70.0	118.0	0.5932	0.5763	0.0	0.0	0.0	0.0	0.0
0.0	30.0	60	5.0523	0.0056	4154.7218	2879.8337	374.0	570.0	0.6561	366.0	0.6421	108.0	112.0	158.0	0.7089	0.6835	98.0	100.0	152.0	0.6579	0.6447	92.0	92.0	142.0	0.6479	0.6479	68.0	70.0	118.0	0.5932	0.5763	0.0	0.0	0.0	0.0	0.0
0.0	31.0	62	5.0553	0.0056	4157.1328	2881.5049	376.0	570.0	0.6596	368.0	0.6456	108.0	112.0	158.0	0.7089	0.6835	99.0	101.0	152.0	0.6645	0.6513	93.0	93.0	142.0	0.6549	0.6549	68.0	70.0	118.0	0.5932	0.5763	0.0	0.0	0.0	0.0	0.0
0.0	32.0	64	5.0612	0.0056	4161.9988	2884.8777	373.0	570.0	0.6544	365.0	0.6404	108.0	112.0	158.0	0.7089	0.6835	97.0	99.0	152.0	0.6513	0.6382	92.0	92.0	142.0	0.6479	0.6479	68.0	70.0	118.0	0.5932	0.5763	0.0	0.0	0.0	0.0	0.0
0.0	33.0	66	5.0628	0.0056	4163.2916	2885.7738	375.0	570.0	0.6579	366.0	0.6421	108.0	112.0	158.0	0.7089	0.6835	99.0	101.0	152.0	0.6645	0.6513	91.0	92.0	142.0	0.6479	0.6408	68.0	70.0	118.0	0.5932	0.5763	0.0	0.0	0.0	0.0	0.0
0.0	34.0	68	5.0506	0.0056	4153.2914	2878.8422	374.0	570.0	0.6561	365.0	0.6404	107.0	111.0	158.0	0.7025	0.6772	98.0	100.0	152.0	0.6579	0.6447	93.0	94.0	142.0	0.6620	0.6549	67.0	69.0	118.0	0.5847	0.5678	0.0	0.0	0.0	0.0	0.0
0.0	35.0	70	5.0623	0.0056	4162.9043	2885.5054	376.0	570.0	0.6596	367.0	0.6439	108.0	112.0	158.0	0.7089	0.6835	98.0	100.0	152.0	0.6579	0.6447	93.0	94.0	142.0	0.6620	0.6549	68.0	70.0	118.0	0.5932	0.5763	0.0	0.0	0.0	0.0	0.0

Framework versions

• Transformers 4.51.3
• Pytorch 2.6.0+cu124
• Datasets 3.5.0
• Tokenizers 0.21.1