ARC-Easy_Llama-3.2-1B-xc26qld6

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: 2.9012
• Model Preparation Time: 0.0061
• Mdl: 2385.7544
• Accumulated Loss: 1653.6789
• Correct Preds: 415.0
• Total Preds: 570.0
• Accuracy: 0.7281
• Correct Gen Preds: 226.0
• Gen Accuracy: 0.3965
• Correct Gen Preds 32: 6.0
• Correct Preds 32: 123.0
• Total Labels 32: 158.0
• Accuracy 32: 0.7785
• Gen Accuracy 32: 0.0380
• Correct Gen Preds 33: 103.0
• Correct Preds 33: 112.0
• Total Labels 33: 152.0
• Accuracy 33: 0.7368
• Gen Accuracy 33: 0.6776
• Correct Gen Preds 34: 74.0
• Correct Preds 34: 112.0
• Total Labels 34: 142.0
• Accuracy 34: 0.7887
• Gen Accuracy 34: 0.5211
• Correct Gen Preds 35: 43.0
• Correct Preds 35: 68.0
• Total Labels 35: 118.0
• Accuracy 35: 0.5763
• Gen Accuracy 35: 0.3644
• 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: 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	Correct Gen Preds 36	Correct Preds 36	Total Labels 36	Accuracy 36	Gen Accuracy 36
No log	0	0	1.5354	0.0061	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
1.0782	1.0	15	0.8806	0.0061	724.1325	501.9304	395.0	570.0	0.6930	0.0	0.0	0.0	111.0	158.0	0.7025	0.0	0.0	99.0	152.0	0.6513	0.0	0.0	110.0	142.0	0.7746	0.0	0.0	75.0	118.0	0.6356	0.0	0.0	0.0	0.0	0.0	0.0
0.6705	2.0	30	0.8447	0.0061	694.6422	481.4893	394.0	570.0	0.6912	0.0	0.0	0.0	89.0	158.0	0.5633	0.0	0.0	112.0	152.0	0.7368	0.0	0.0	114.0	142.0	0.8028	0.0	0.0	79.0	118.0	0.6695	0.0	0.0	0.0	0.0	0.0	0.0
0.2979	3.0	45	1.0292	0.0061	846.3429	586.6402	402.0	570.0	0.7053	0.0	0.0	0.0	113.0	158.0	0.7152	0.0	0.0	118.0	152.0	0.7763	0.0	0.0	103.0	142.0	0.7254	0.0	0.0	68.0	118.0	0.5763	0.0	0.0	0.0	0.0	0.0	0.0
0.2678	4.0	60	1.5881	0.0061	1305.9724	905.2311	393.0	570.0	0.6895	0.0	0.0	0.0	127.0	158.0	0.8038	0.0	0.0	114.0	152.0	0.75	0.0	0.0	89.0	142.0	0.6268	0.0	0.0	63.0	118.0	0.5339	0.0	0.0	0.0	0.0	0.0	0.0
0.1021	5.0	75	1.8729	0.0061	1540.1515	1067.5517	404.0	570.0	0.7088	0.0	0.0	0.0	102.0	158.0	0.6456	0.0	0.0	101.0	152.0	0.6645	0.0	0.0	118.0	142.0	0.8310	0.0	0.0	83.0	118.0	0.7034	0.0	0.0	0.0	0.0	0.0	0.0
0.0009	6.0	90	2.9155	0.0061	2397.5041	1661.8232	412.0	570.0	0.7228	59.0	0.1035	0.0	107.0	158.0	0.6772	0.0	59.0	116.0	152.0	0.7632	0.3882	0.0	112.0	142.0	0.7887	0.0	0.0	77.0	118.0	0.6525	0.0	0.0	0.0	0.0	0.0	0.0
0.0	7.0	105	3.3063	0.0061	2718.8587	1884.5693	404.0	570.0	0.7088	211.0	0.3702	2.0	98.0	158.0	0.6203	0.0127	116.0	125.0	152.0	0.8224	0.7632	69.0	112.0	142.0	0.7887	0.4859	24.0	69.0	118.0	0.5847	0.2034	0.0	0.0	0.0	0.0	0.0
0.0002	8.0	120	2.9012	0.0061	2385.7544	1653.6789	415.0	570.0	0.7281	226.0	0.3965	6.0	123.0	158.0	0.7785	0.0380	103.0	112.0	152.0	0.7368	0.6776	74.0	112.0	142.0	0.7887	0.5211	43.0	68.0	118.0	0.5763	0.3644	0.0	0.0	0.0	0.0	0.0
0.0044	9.0	135	2.4787	0.0061	2038.3538	1412.8792	410.0	570.0	0.7193	232.0	0.4070	2.0	108.0	158.0	0.6835	0.0127	103.0	114.0	152.0	0.75	0.6776	81.0	116.0	142.0	0.8169	0.5704	46.0	72.0	118.0	0.6102	0.3898	0.0	0.0	0.0	0.0	0.0
0.0001	10.0	150	2.8459	0.0061	2340.2525	1622.1394	411.0	570.0	0.7211	285.0	0.5	21.0	96.0	158.0	0.6076	0.1329	104.0	117.0	152.0	0.7697	0.6842	104.0	119.0	142.0	0.8380	0.7324	56.0	79.0	118.0	0.6695	0.4746	0.0	0.0	0.0	0.0	0.0
0.0002	11.0	165	3.0387	0.0061	2498.8154	1732.0469	405.0	570.0	0.7105	356.0	0.6246	70.0	106.0	158.0	0.6709	0.4430	114.0	118.0	152.0	0.7763	0.75	108.0	111.0	142.0	0.7817	0.7606	64.0	70.0	118.0	0.5932	0.5424	0.0	0.0	0.0	0.0	0.0
0.0	12.0	180	3.1544	0.0061	2593.9363	1797.9796	405.0	570.0	0.7105	377.0	0.6614	82.0	109.0	158.0	0.6899	0.5190	120.0	120.0	152.0	0.7895	0.7895	109.0	109.0	142.0	0.7676	0.7676	66.0	67.0	118.0	0.5678	0.5593	0.0	0.0	0.0	0.0	0.0
0.0	13.0	195	3.1696	0.0061	2606.4974	1806.6863	407.0	570.0	0.7140	385.0	0.6754	86.0	108.0	158.0	0.6835	0.5443	120.0	120.0	152.0	0.7895	0.7895	111.0	111.0	142.0	0.7817	0.7817	68.0	68.0	118.0	0.5763	0.5763	0.0	0.0	0.0	0.0	0.0
0.0	14.0	210	3.1936	0.0061	2626.1842	1820.3322	405.0	570.0	0.7105	387.0	0.6789	88.0	106.0	158.0	0.6709	0.5570	120.0	120.0	152.0	0.7895	0.7895	110.0	110.0	142.0	0.7746	0.7746	69.0	69.0	118.0	0.5847	0.5847	0.0	0.0	0.0	0.0	0.0
0.0	15.0	225	3.1623	0.0061	2600.4985	1802.5282	410.0	570.0	0.7193	389.0	0.6825	87.0	108.0	158.0	0.6835	0.5506	120.0	120.0	152.0	0.7895	0.7895	112.0	112.0	142.0	0.7887	0.7887	70.0	70.0	118.0	0.5932	0.5932	0.0	0.0	0.0	0.0	0.0
0.0	16.0	240	3.1958	0.0061	2628.0486	1821.6245	409.0	570.0	0.7175	388.0	0.6807	89.0	108.0	158.0	0.6835	0.5633	120.0	120.0	152.0	0.7895	0.7895	110.0	111.0	142.0	0.7817	0.7746	69.0	70.0	118.0	0.5932	0.5847	0.0	0.0	0.0	0.0	0.0
0.0	17.0	255	3.1951	0.0061	2627.4330	1821.1978	408.0	570.0	0.7158	386.0	0.6772	88.0	108.0	158.0	0.6835	0.5570	120.0	120.0	152.0	0.7895	0.7895	109.0	110.0	142.0	0.7746	0.7676	69.0	70.0	118.0	0.5932	0.5847	0.0	0.0	0.0	0.0	0.0
0.0	18.0	270	3.1887	0.0061	2622.1541	1817.5387	405.0	570.0	0.7105	383.0	0.6719	87.0	107.0	158.0	0.6772	0.5506	120.0	120.0	152.0	0.7895	0.7895	108.0	109.0	142.0	0.7676	0.7606	68.0	69.0	118.0	0.5847	0.5763	0.0	0.0	0.0	0.0	0.0
0.0	19.0	285	3.1854	0.0061	2619.4677	1815.6767	409.0	570.0	0.7175	389.0	0.6825	89.0	108.0	158.0	0.6835	0.5633	120.0	120.0	152.0	0.7895	0.7895	110.0	111.0	142.0	0.7817	0.7746	70.0	70.0	118.0	0.5932	0.5932	0.0	0.0	0.0	0.0	0.0
0.0	20.0	300	3.2024	0.0061	2633.4221	1825.3491	407.0	570.0	0.7140	385.0	0.6754	87.0	107.0	158.0	0.6772	0.5506	120.0	120.0	152.0	0.7895	0.7895	108.0	110.0	142.0	0.7746	0.7606	70.0	70.0	118.0	0.5932	0.5932	0.0	0.0	0.0	0.0	0.0
0.0	21.0	315	3.2060	0.0061	2636.4040	1827.4160	403.0	570.0	0.7070	386.0	0.6772	89.0	106.0	158.0	0.6709	0.5633	120.0	120.0	152.0	0.7895	0.7895	108.0	108.0	142.0	0.7606	0.7606	69.0	69.0	118.0	0.5847	0.5847	0.0	0.0	0.0	0.0	0.0
0.0	22.0	330	3.1978	0.0061	2629.6576	1822.7397	408.0	570.0	0.7158	387.0	0.6789	88.0	107.0	158.0	0.6772	0.5570	120.0	120.0	152.0	0.7895	0.7895	110.0	111.0	142.0	0.7817	0.7746	69.0	70.0	118.0	0.5932	0.5847	0.0	0.0	0.0	0.0	0.0
0.0	23.0	345	3.2004	0.0061	2631.8114	1824.2326	408.0	570.0	0.7158	386.0	0.6772	89.0	109.0	158.0	0.6899	0.5633	120.0	120.0	152.0	0.7895	0.7895	107.0	109.0	142.0	0.7676	0.7535	70.0	70.0	118.0	0.5932	0.5932	0.0	0.0	0.0	0.0	0.0
0.0	24.0	360	3.1856	0.0061	2619.6676	1815.8152	405.0	570.0	0.7105	385.0	0.6754	87.0	106.0	158.0	0.6709	0.5506	120.0	120.0	152.0	0.7895	0.7895	109.0	110.0	142.0	0.7746	0.7676	69.0	69.0	118.0	0.5847	0.5847	0.0	0.0	0.0	0.0	0.0
0.0	25.0	375	3.1994	0.0061	2630.9994	1823.6698	408.0	570.0	0.7158	389.0	0.6825	88.0	107.0	158.0	0.6772	0.5570	120.0	120.0	152.0	0.7895	0.7895	112.0	112.0	142.0	0.7887	0.7887	69.0	69.0	118.0	0.5847	0.5847	0.0	0.0	0.0	0.0	0.0
0.0	26.0	390	3.2091	0.0061	2638.9259	1829.1640	406.0	570.0	0.7123	384.0	0.6737	87.0	107.0	158.0	0.6772	0.5506	120.0	120.0	152.0	0.7895	0.7895	108.0	110.0	142.0	0.7746	0.7606	69.0	69.0	118.0	0.5847	0.5847	0.0	0.0	0.0	0.0	0.0
0.0	27.0	405	3.2149	0.0061	2643.7430	1832.5030	406.0	570.0	0.7123	388.0	0.6807	88.0	105.0	158.0	0.6646	0.5570	120.0	120.0	152.0	0.7895	0.7895	112.0	113.0	142.0	0.7958	0.7887	68.0	68.0	118.0	0.5763	0.5763	0.0	0.0	0.0	0.0	0.0
0.0	28.0	420	3.2152	0.0061	2643.9757	1832.6643	408.0	570.0	0.7158	390.0	0.6842	90.0	108.0	158.0	0.6835	0.5696	120.0	120.0	152.0	0.7895	0.7895	111.0	111.0	142.0	0.7817	0.7817	69.0	69.0	118.0	0.5847	0.5847	0.0	0.0	0.0	0.0	0.0
0.0	29.0	435	3.2105	0.0061	2640.1343	1830.0016	409.0	570.0	0.7175	390.0	0.6842	89.0	108.0	158.0	0.6835	0.5633	120.0	120.0	152.0	0.7895	0.7895	111.0	111.0	142.0	0.7817	0.7817	70.0	70.0	118.0	0.5932	0.5932	0.0	0.0	0.0	0.0	0.0
0.0	30.0	450	3.2265	0.0061	2653.2736	1839.1091	408.0	570.0	0.7158	389.0	0.6825	89.0	108.0	158.0	0.6835	0.5633	120.0	120.0	152.0	0.7895	0.7895	109.0	109.0	142.0	0.7676	0.7676	71.0	71.0	118.0	0.6017	0.6017	0.0	0.0	0.0	0.0	0.0
0.0	31.0	465	3.2205	0.0061	2648.3636	1835.7057	409.0	570.0	0.7175	390.0	0.6842	90.0	108.0	158.0	0.6835	0.5696	121.0	121.0	152.0	0.7961	0.7961	109.0	110.0	142.0	0.7746	0.7676	70.0	70.0	118.0	0.5932	0.5932	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