ARC-Easy_Llama-3.2-1B-vpn58hdl

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: 3.5991
• Model Preparation Time: 0.0055
• Mdl: 2959.6362
• Accumulated Loss: 2051.4635
• Correct Preds: 396.0
• Total Preds: 570.0
• Accuracy: 0.6947
• Correct Gen Preds: 395.0
• Gen Accuracy: 0.6930
• Correct Gen Preds 32: 93.0
• Correct Preds 32: 93.0
• Total Labels 32: 158.0
• Accuracy 32: 0.5886
• Gen Accuracy 32: 0.5886
• Correct Gen Preds 33: 114.0
• Correct Preds 33: 114.0
• Total Labels 33: 152.0
• Accuracy 33: 0.75
• Gen Accuracy 33: 0.75
• Correct Gen Preds 34: 101.0
• Correct Preds 34: 102.0
• Total Labels 34: 142.0
• Accuracy 34: 0.7183
• Gen Accuracy 34: 0.7113
• Correct Gen Preds 35: 87.0
• Correct Preds 35: 87.0
• Total Labels 35: 118.0
• Accuracy 35: 0.7373
• Gen Accuracy 35: 0.7373
• 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	0.0055	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
1.1762	1.0	4	1.0791	0.0055	887.4155	615.1096	348.0	570.0	0.6105	348.0	0.6105	134.0	134.0	158.0	0.8481	0.8481	104.0	104.0	152.0	0.6842	0.6842	49.0	49.0	142.0	0.3451	0.3451	61.0	61.0	118.0	0.5169	0.5169	0.0	0.0	0.0	0.0	0.0
0.8009	2.0	8	0.8773	0.0055	721.4505	500.0714	391.0	570.0	0.6860	390.0	0.6842	103.0	104.0	158.0	0.6582	0.6519	125.0	125.0	152.0	0.8224	0.8224	99.0	99.0	142.0	0.6972	0.6972	63.0	63.0	118.0	0.5339	0.5339	0.0	0.0	0.0	0.0	0.0
0.5679	3.0	12	1.4855	0.0055	1221.5559	846.7180	393.0	570.0	0.6895	282.0	0.4947	66.0	124.0	158.0	0.7848	0.4177	100.0	107.0	152.0	0.7039	0.6579	70.0	90.0	142.0	0.6338	0.4930	46.0	72.0	118.0	0.6102	0.3898	0.0	0.0	0.0	0.0	0.0
0.0902	4.0	16	1.7387	0.0055	1429.7937	991.0575	393.0	570.0	0.6895	376.0	0.6596	95.0	107.0	158.0	0.6772	0.6013	116.0	117.0	152.0	0.7697	0.7632	83.0	86.0	142.0	0.6056	0.5845	82.0	83.0	118.0	0.7034	0.6949	0.0	0.0	0.0	0.0	0.0
0.0006	5.0	20	3.5991	0.0055	2959.6362	2051.4635	396.0	570.0	0.6947	395.0	0.6930	93.0	93.0	158.0	0.5886	0.5886	114.0	114.0	152.0	0.75	0.75	101.0	102.0	142.0	0.7183	0.7113	87.0	87.0	118.0	0.7373	0.7373	0.0	0.0	0.0	0.0	0.0
0.0	6.0	24	4.5761	0.0055	3763.0911	2608.3760	384.0	570.0	0.6737	384.0	0.6737	78.0	78.0	158.0	0.4937	0.4937	114.0	114.0	152.0	0.75	0.75	106.0	106.0	142.0	0.7465	0.7465	86.0	86.0	118.0	0.7288	0.7288	0.0	0.0	0.0	0.0	0.0
0.0	7.0	28	4.8394	0.0055	3979.6272	2758.4674	379.0	570.0	0.6649	379.0	0.6649	80.0	80.0	158.0	0.5063	0.5063	120.0	120.0	152.0	0.7895	0.7895	99.0	99.0	142.0	0.6972	0.6972	80.0	80.0	118.0	0.6780	0.6780	0.0	0.0	0.0	0.0	0.0
0.0	8.0	32	4.8878	0.0055	4019.4060	2786.0399	382.0	570.0	0.6702	382.0	0.6702	90.0	90.0	158.0	0.5696	0.5696	119.0	119.0	152.0	0.7829	0.7829	96.0	96.0	142.0	0.6761	0.6761	77.0	77.0	118.0	0.6525	0.6525	0.0	0.0	0.0	0.0	0.0
0.0	9.0	36	4.9652	0.0055	4083.1030	2830.1913	379.0	570.0	0.6649	379.0	0.6649	91.0	91.0	158.0	0.5759	0.5759	119.0	119.0	152.0	0.7829	0.7829	93.0	93.0	142.0	0.6549	0.6549	76.0	76.0	118.0	0.6441	0.6441	0.0	0.0	0.0	0.0	0.0
0.0	10.0	40	5.0019	0.0055	4113.2443	2851.0837	381.0	570.0	0.6684	381.0	0.6684	92.0	92.0	158.0	0.5823	0.5823	119.0	119.0	152.0	0.7829	0.7829	93.0	93.0	142.0	0.6549	0.6549	77.0	77.0	118.0	0.6525	0.6525	0.0	0.0	0.0	0.0	0.0
0.0	11.0	44	4.9861	0.0055	4100.2734	2842.0930	381.0	570.0	0.6684	381.0	0.6684	93.0	93.0	158.0	0.5886	0.5886	120.0	120.0	152.0	0.7895	0.7895	92.0	92.0	142.0	0.6479	0.6479	76.0	76.0	118.0	0.6441	0.6441	0.0	0.0	0.0	0.0	0.0
0.0	12.0	48	4.9972	0.0055	4109.3645	2848.3944	381.0	570.0	0.6684	381.0	0.6684	93.0	93.0	158.0	0.5886	0.5886	120.0	120.0	152.0	0.7895	0.7895	93.0	93.0	142.0	0.6549	0.6549	75.0	75.0	118.0	0.6356	0.6356	0.0	0.0	0.0	0.0	0.0
0.0	13.0	52	4.9966	0.0055	4108.8499	2848.0377	381.0	570.0	0.6684	381.0	0.6684	93.0	93.0	158.0	0.5886	0.5886	119.0	119.0	152.0	0.7829	0.7829	93.0	93.0	142.0	0.6549	0.6549	76.0	76.0	118.0	0.6441	0.6441	0.0	0.0	0.0	0.0	0.0
0.0	14.0	56	5.0252	0.0055	4132.4066	2864.3660	381.0	570.0	0.6684	381.0	0.6684	94.0	94.0	158.0	0.5949	0.5949	120.0	120.0	152.0	0.7895	0.7895	93.0	93.0	142.0	0.6549	0.6549	74.0	74.0	118.0	0.6271	0.6271	0.0	0.0	0.0	0.0	0.0
0.0	15.0	60	5.0016	0.0055	4112.9924	2850.9091	381.0	570.0	0.6684	381.0	0.6684	95.0	95.0	158.0	0.6013	0.6013	120.0	120.0	152.0	0.7895	0.7895	92.0	92.0	142.0	0.6479	0.6479	74.0	74.0	118.0	0.6271	0.6271	0.0	0.0	0.0	0.0	0.0
0.0	16.0	64	5.0225	0.0055	4130.1731	2862.8178	381.0	570.0	0.6684	381.0	0.6684	94.0	94.0	158.0	0.5949	0.5949	119.0	119.0	152.0	0.7829	0.7829	93.0	93.0	142.0	0.6549	0.6549	75.0	75.0	118.0	0.6356	0.6356	0.0	0.0	0.0	0.0	0.0
0.0	17.0	68	5.0185	0.0055	4126.8593	2860.5209	378.0	570.0	0.6632	378.0	0.6632	94.0	94.0	158.0	0.5949	0.5949	119.0	119.0	152.0	0.7829	0.7829	92.0	92.0	142.0	0.6479	0.6479	73.0	73.0	118.0	0.6186	0.6186	0.0	0.0	0.0	0.0	0.0
0.0	18.0	72	5.0248	0.0055	4132.0761	2864.1369	380.0	570.0	0.6667	380.0	0.6667	94.0	94.0	158.0	0.5949	0.5949	120.0	120.0	152.0	0.7895	0.7895	93.0	93.0	142.0	0.6549	0.6549	73.0	73.0	118.0	0.6186	0.6186	0.0	0.0	0.0	0.0	0.0
0.0	19.0	76	5.0189	0.0055	4127.2249	2860.7743	382.0	570.0	0.6702	382.0	0.6702	94.0	94.0	158.0	0.5949	0.5949	119.0	119.0	152.0	0.7829	0.7829	93.0	93.0	142.0	0.6549	0.6549	76.0	76.0	118.0	0.6441	0.6441	0.0	0.0	0.0	0.0	0.0
0.0	20.0	80	4.9974	0.0055	4109.5270	2848.5070	382.0	570.0	0.6702	382.0	0.6702	95.0	95.0	158.0	0.6013	0.6013	119.0	119.0	152.0	0.7829	0.7829	93.0	93.0	142.0	0.6549	0.6549	75.0	75.0	118.0	0.6356	0.6356	0.0	0.0	0.0	0.0	0.0
0.0	21.0	84	5.0082	0.0055	4118.4250	2854.6746	380.0	570.0	0.6667	380.0	0.6667	94.0	94.0	158.0	0.5949	0.5949	120.0	120.0	152.0	0.7895	0.7895	93.0	93.0	142.0	0.6549	0.6549	73.0	73.0	118.0	0.6186	0.6186	0.0	0.0	0.0	0.0	0.0
0.0	22.0	88	5.0418	0.0055	4146.0205	2873.8024	380.0	570.0	0.6667	380.0	0.6667	94.0	94.0	158.0	0.5949	0.5949	120.0	120.0	152.0	0.7895	0.7895	93.0	93.0	142.0	0.6549	0.6549	73.0	73.0	118.0	0.6186	0.6186	0.0	0.0	0.0	0.0	0.0
0.0	23.0	92	4.9837	0.0055	4098.2995	2840.7248	381.0	570.0	0.6684	381.0	0.6684	93.0	93.0	158.0	0.5886	0.5886	119.0	119.0	152.0	0.7829	0.7829	94.0	94.0	142.0	0.6620	0.6620	75.0	75.0	118.0	0.6356	0.6356	0.0	0.0	0.0	0.0	0.0
0.0	24.0	96	5.0103	0.0055	4120.1767	2855.8888	380.0	570.0	0.6667	380.0	0.6667	95.0	95.0	158.0	0.6013	0.6013	120.0	120.0	152.0	0.7895	0.7895	92.0	92.0	142.0	0.6479	0.6479	73.0	73.0	118.0	0.6186	0.6186	0.0	0.0	0.0	0.0	0.0
0.0	25.0	100	4.9809	0.0055	4095.9670	2839.1080	381.0	570.0	0.6684	381.0	0.6684	94.0	94.0	158.0	0.5949	0.5949	120.0	120.0	152.0	0.7895	0.7895	93.0	93.0	142.0	0.6549	0.6549	74.0	74.0	118.0	0.6271	0.6271	0.0	0.0	0.0	0.0	0.0
0.0	26.0	104	4.9789	0.0055	4094.3683	2837.9999	381.0	570.0	0.6684	381.0	0.6684	95.0	95.0	158.0	0.6013	0.6013	119.0	119.0	152.0	0.7829	0.7829	93.0	93.0	142.0	0.6549	0.6549	74.0	74.0	118.0	0.6271	0.6271	0.0	0.0	0.0	0.0	0.0
0.0	27.0	108	4.9982	0.0055	4110.1885	2848.9656	382.0	570.0	0.6702	382.0	0.6702	95.0	95.0	158.0	0.6013	0.6013	119.0	119.0	152.0	0.7829	0.7829	93.0	93.0	142.0	0.6549	0.6549	75.0	75.0	118.0	0.6356	0.6356	0.0	0.0	0.0	0.0	0.0
0.0	28.0	112	5.0292	0.0055	4135.6796	2866.6347	376.0	570.0	0.6596	376.0	0.6596	92.0	92.0	158.0	0.5823	0.5823	119.0	119.0	152.0	0.7829	0.7829	92.0	92.0	142.0	0.6479	0.6479	73.0	73.0	118.0	0.6186	0.6186	0.0	0.0	0.0	0.0	0.0
0.0	29.0	116	5.0058	0.0055	4116.4280	2853.2905	380.0	570.0	0.6667	380.0	0.6667	94.0	94.0	158.0	0.5949	0.5949	119.0	119.0	152.0	0.7829	0.7829	93.0	93.0	142.0	0.6549	0.6549	74.0	74.0	118.0	0.6271	0.6271	0.0	0.0	0.0	0.0	0.0
0.0	30.0	120	5.0002	0.0055	4111.8789	2850.1373	379.0	570.0	0.6649	379.0	0.6649	94.0	94.0	158.0	0.5949	0.5949	119.0	119.0	152.0	0.7829	0.7829	92.0	92.0	142.0	0.6479	0.6479	74.0	74.0	118.0	0.6271	0.6271	0.0	0.0	0.0	0.0	0.0
0.0	31.0	124	5.0147	0.0055	4123.7293	2858.3513	380.0	570.0	0.6667	380.0	0.6667	95.0	95.0	158.0	0.6013	0.6013	119.0	119.0	152.0	0.7829	0.7829	91.0	91.0	142.0	0.6408	0.6408	75.0	75.0	118.0	0.6356	0.6356	0.0	0.0	0.0	0.0	0.0
0.0	32.0	128	5.0075	0.0055	4117.8203	2854.2555	382.0	570.0	0.6702	382.0	0.6702	96.0	96.0	158.0	0.6076	0.6076	119.0	119.0	152.0	0.7829	0.7829	93.0	93.0	142.0	0.6549	0.6549	74.0	74.0	118.0	0.6271	0.6271	0.0	0.0	0.0	0.0	0.0
0.0	33.0	132	4.9936	0.0055	4106.4361	2846.3646	381.0	570.0	0.6684	381.0	0.6684	94.0	94.0	158.0	0.5949	0.5949	119.0	119.0	152.0	0.7829	0.7829	93.0	93.0	142.0	0.6549	0.6549	75.0	75.0	118.0	0.6356	0.6356	0.0	0.0	0.0	0.0	0.0
0.0	34.0	136	5.0292	0.0055	4135.7242	2866.6656	380.0	570.0	0.6667	380.0	0.6667	93.0	93.0	158.0	0.5886	0.5886	119.0	119.0	152.0	0.7829	0.7829	94.0	94.0	142.0	0.6620	0.6620	74.0	74.0	118.0	0.6271	0.6271	0.0	0.0	0.0	0.0	0.0
0.0	35.0	140	5.0201	0.0055	4128.2413	2861.4788	381.0	570.0	0.6684	381.0	0.6684	96.0	96.0	158.0	0.6076	0.6076	119.0	119.0	152.0	0.7829	0.7829	93.0	93.0	142.0	0.6549	0.6549	73.0	73.0	118.0	0.6186	0.6186	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