ARC-Easy_Llama-3.2-1B-dgieizsr

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: 1.7423
• Model Preparation Time: 0.0058
• Mdl: 1432.7945
• Accumulated Loss: 993.1375
• Correct Preds: 382.0
• Total Preds: 570.0
• Accuracy: 0.6702
• Correct Gen Preds: 376.0
• Gen Accuracy: 0.6596
• Correct Gen Preds 32: 112.0
• Correct Preds 32: 116.0
• Total Labels 32: 158.0
• Accuracy 32: 0.7342
• Gen Accuracy 32: 0.7089
• Correct Gen Preds 33: 96.0
• Correct Preds 33: 98.0
• Total Labels 33: 152.0
• Accuracy 33: 0.6447
• Gen Accuracy 33: 0.6316
• Correct Gen Preds 34: 92.0
• Correct Preds 34: 92.0
• Total Labels 34: 142.0
• Accuracy 34: 0.6479
• Gen Accuracy 34: 0.6479
• Correct Gen Preds 35: 76.0
• Correct Preds 35: 76.0
• Total Labels 35: 118.0
• Accuracy 35: 0.6441
• Gen Accuracy 35: 0.6441
• 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	0.0058	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
1.4268	1.0	1	1.5354	0.0058	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
1.4268	2.0	2	1.7216	0.0058	1415.7560	981.3273	186.0	570.0	0.3263	186.0	0.3263	0.0	0.0	158.0	0.0	0.0	142.0	142.0	152.0	0.9342	0.9342	1.0	1.0	142.0	0.0070	0.0070	43.0	43.0	118.0	0.3644	0.3644	0.0	0.0	0.0	0.0	0.0
1.7554	3.0	3	1.3826	0.0058	1136.9625	788.0824	227.0	570.0	0.3982	227.0	0.3982	154.0	154.0	158.0	0.9747	0.9747	18.0	18.0	152.0	0.1184	0.1184	12.0	12.0	142.0	0.0845	0.0845	43.0	43.0	118.0	0.3644	0.3644	0.0	0.0	0.0	0.0	0.0
0.7737	4.0	4	1.2202	0.0058	1003.4295	695.5243	349.0	570.0	0.6123	348.0	0.6105	95.0	96.0	158.0	0.6076	0.6013	64.0	64.0	152.0	0.4211	0.4211	95.0	95.0	142.0	0.6690	0.6690	94.0	94.0	118.0	0.7966	0.7966	0.0	0.0	0.0	0.0	0.0
0.3114	5.0	5	1.7423	0.0058	1432.7945	993.1375	382.0	570.0	0.6702	376.0	0.6596	112.0	116.0	158.0	0.7342	0.7089	96.0	98.0	152.0	0.6447	0.6316	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
0.0299	6.0	6	3.2490	0.0058	2671.7616	1851.9240	381.0	570.0	0.6684	381.0	0.6684	128.0	128.0	158.0	0.8101	0.8101	97.0	97.0	152.0	0.6382	0.6382	86.0	86.0	142.0	0.6056	0.6056	70.0	70.0	118.0	0.5932	0.5932	0.0	0.0	0.0	0.0	0.0
0.0001	7.0	7	4.5498	0.0058	3741.4738	2593.3920	371.0	570.0	0.6509	371.0	0.6509	131.0	131.0	158.0	0.8291	0.8291	95.0	95.0	152.0	0.625	0.625	79.0	79.0	142.0	0.5563	0.5563	66.0	66.0	118.0	0.5593	0.5593	0.0	0.0	0.0	0.0	0.0
0.0	8.0	8	5.3621	0.0058	4409.4085	3056.3690	366.0	570.0	0.6421	366.0	0.6421	133.0	133.0	158.0	0.8418	0.8418	94.0	94.0	152.0	0.6184	0.6184	76.0	76.0	142.0	0.5352	0.5352	63.0	63.0	118.0	0.5339	0.5339	0.0	0.0	0.0	0.0	0.0
0.0	9.0	9	5.9649	0.0058	4905.1650	3400.0013	357.0	570.0	0.6263	356.0	0.6246	134.0	134.0	158.0	0.8481	0.8481	91.0	91.0	152.0	0.5987	0.5987	73.0	73.0	142.0	0.5141	0.5141	58.0	59.0	118.0	0.5	0.4915	0.0	0.0	0.0	0.0	0.0
0.0	10.0	10	6.3868	0.0058	5252.1360	3640.5033	352.0	570.0	0.6175	352.0	0.6175	134.0	134.0	158.0	0.8481	0.8481	90.0	90.0	152.0	0.5921	0.5921	71.0	71.0	142.0	0.5	0.5	57.0	57.0	118.0	0.4831	0.4831	0.0	0.0	0.0	0.0	0.0
0.0	11.0	11	6.6452	0.0058	5464.5938	3787.7678	349.0	570.0	0.6123	348.0	0.6105	133.0	133.0	158.0	0.8418	0.8418	89.0	89.0	152.0	0.5855	0.5855	70.0	70.0	142.0	0.4930	0.4930	56.0	57.0	118.0	0.4831	0.4746	0.0	0.0	0.0	0.0	0.0
0.0	12.0	12	6.8484	0.0058	5631.6837	3903.5857	346.0	570.0	0.6070	344.0	0.6035	135.0	135.0	158.0	0.8544	0.8544	88.0	88.0	152.0	0.5789	0.5789	70.0	70.0	142.0	0.4930	0.4930	51.0	53.0	118.0	0.4492	0.4322	0.0	0.0	0.0	0.0	0.0
0.0	13.0	13	7.0312	0.0058	5782.0460	4007.8089	343.0	570.0	0.6018	342.0	0.6	134.0	134.0	158.0	0.8481	0.8481	88.0	88.0	152.0	0.5789	0.5789	70.0	70.0	142.0	0.4930	0.4930	50.0	51.0	118.0	0.4322	0.4237	0.0	0.0	0.0	0.0	0.0
0.0	14.0	14	7.0962	0.0058	5835.4383	4044.8176	343.0	570.0	0.6018	342.0	0.6	135.0	135.0	158.0	0.8544	0.8544	88.0	88.0	152.0	0.5789	0.5789	70.0	70.0	142.0	0.4930	0.4930	49.0	50.0	118.0	0.4237	0.4153	0.0	0.0	0.0	0.0	0.0
0.0	15.0	15	7.1828	0.0058	5906.7087	4094.2185	344.0	570.0	0.6035	343.0	0.6018	135.0	135.0	158.0	0.8544	0.8544	88.0	88.0	152.0	0.5789	0.5789	70.0	70.0	142.0	0.4930	0.4930	50.0	51.0	118.0	0.4322	0.4237	0.0	0.0	0.0	0.0	0.0
0.0	16.0	16	7.2694	0.0058	5977.8847	4143.5539	343.0	570.0	0.6018	342.0	0.6	135.0	135.0	158.0	0.8544	0.8544	88.0	88.0	152.0	0.5789	0.5789	69.0	69.0	142.0	0.4859	0.4859	50.0	51.0	118.0	0.4322	0.4237	0.0	0.0	0.0	0.0	0.0
0.0	17.0	17	7.2328	0.0058	5947.7969	4122.6987	343.0	570.0	0.6018	342.0	0.6	135.0	135.0	158.0	0.8544	0.8544	89.0	89.0	152.0	0.5855	0.5855	69.0	69.0	142.0	0.4859	0.4859	49.0	50.0	118.0	0.4237	0.4153	0.0	0.0	0.0	0.0	0.0
0.0	18.0	18	7.2996	0.0058	6002.7481	4160.7879	341.0	570.0	0.5982	340.0	0.5965	135.0	135.0	158.0	0.8544	0.8544	88.0	88.0	152.0	0.5789	0.5789	68.0	68.0	142.0	0.4789	0.4789	49.0	50.0	118.0	0.4237	0.4153	0.0	0.0	0.0	0.0	0.0
0.0	19.0	19	7.3357	0.0058	6032.4416	4181.3699	338.0	570.0	0.5930	337.0	0.5912	135.0	135.0	158.0	0.8544	0.8544	88.0	88.0	152.0	0.5789	0.5789	67.0	67.0	142.0	0.4718	0.4718	47.0	48.0	118.0	0.4068	0.3983	0.0	0.0	0.0	0.0	0.0
0.0	20.0	20	7.3491	0.0058	6043.4237	4188.9821	340.0	570.0	0.5965	339.0	0.5947	135.0	135.0	158.0	0.8544	0.8544	88.0	88.0	152.0	0.5789	0.5789	67.0	67.0	142.0	0.4718	0.4718	49.0	50.0	118.0	0.4237	0.4153	0.0	0.0	0.0	0.0	0.0
0.0	21.0	21	7.3808	0.0058	6069.5012	4207.0577	340.0	570.0	0.5965	339.0	0.5947	135.0	135.0	158.0	0.8544	0.8544	88.0	88.0	152.0	0.5789	0.5789	68.0	68.0	142.0	0.4789	0.4789	48.0	49.0	118.0	0.4153	0.4068	0.0	0.0	0.0	0.0	0.0
0.0	22.0	22	7.4168	0.0058	6099.0769	4227.5579	341.0	570.0	0.5982	340.0	0.5965	135.0	135.0	158.0	0.8544	0.8544	88.0	88.0	152.0	0.5789	0.5789	68.0	68.0	142.0	0.4789	0.4789	49.0	50.0	118.0	0.4237	0.4153	0.0	0.0	0.0	0.0	0.0
0.0	23.0	23	7.3966	0.0058	6082.4774	4216.0521	337.0	570.0	0.5912	336.0	0.5895	135.0	135.0	158.0	0.8544	0.8544	88.0	88.0	152.0	0.5789	0.5789	65.0	65.0	142.0	0.4577	0.4577	48.0	49.0	118.0	0.4153	0.4068	0.0	0.0	0.0	0.0	0.0
0.0	24.0	24	7.4176	0.0058	6099.7834	4228.0477	340.0	570.0	0.5965	339.0	0.5947	135.0	135.0	158.0	0.8544	0.8544	89.0	89.0	152.0	0.5855	0.5855	67.0	67.0	142.0	0.4718	0.4718	48.0	49.0	118.0	0.4153	0.4068	0.0	0.0	0.0	0.0	0.0
0.0	25.0	25	7.4073	0.0058	6091.2924	4222.1621	342.0	570.0	0.6	341.0	0.5982	135.0	135.0	158.0	0.8544	0.8544	88.0	88.0	152.0	0.5789	0.5789	68.0	68.0	142.0	0.4789	0.4789	50.0	51.0	118.0	0.4322	0.4237	0.0	0.0	0.0	0.0	0.0
0.0	26.0	26	7.4170	0.0058	6099.2693	4227.6913	338.0	570.0	0.5930	337.0	0.5912	135.0	135.0	158.0	0.8544	0.8544	88.0	88.0	152.0	0.5789	0.5789	66.0	66.0	142.0	0.4648	0.4648	48.0	49.0	118.0	0.4153	0.4068	0.0	0.0	0.0	0.0	0.0
0.0	27.0	27	7.4082	0.0058	6092.0538	4222.6899	338.0	570.0	0.5930	337.0	0.5912	135.0	135.0	158.0	0.8544	0.8544	89.0	89.0	152.0	0.5855	0.5855	66.0	66.0	142.0	0.4648	0.4648	47.0	48.0	118.0	0.4068	0.3983	0.0	0.0	0.0	0.0	0.0
0.0	28.0	28	7.4274	0.0058	6107.8584	4233.6448	337.0	570.0	0.5912	336.0	0.5895	135.0	135.0	158.0	0.8544	0.8544	88.0	88.0	152.0	0.5789	0.5789	65.0	65.0	142.0	0.4577	0.4577	48.0	49.0	118.0	0.4153	0.4068	0.0	0.0	0.0	0.0	0.0
0.0	29.0	29	7.4072	0.0058	6091.2090	4222.1044	338.0	570.0	0.5930	337.0	0.5912	135.0	135.0	158.0	0.8544	0.8544	88.0	88.0	152.0	0.5789	0.5789	66.0	66.0	142.0	0.4648	0.4648	48.0	49.0	118.0	0.4153	0.4068	0.0	0.0	0.0	0.0	0.0
0.0	30.0	30	7.4474	0.0058	6124.2609	4245.0142	335.0	570.0	0.5877	334.0	0.5860	135.0	135.0	158.0	0.8544	0.8544	88.0	88.0	152.0	0.5789	0.5789	66.0	66.0	142.0	0.4648	0.4648	45.0	46.0	118.0	0.3898	0.3814	0.0	0.0	0.0	0.0	0.0
0.0	31.0	31	7.4315	0.0058	6111.2123	4235.9696	335.0	570.0	0.5877	334.0	0.5860	135.0	135.0	158.0	0.8544	0.8544	88.0	88.0	152.0	0.5789	0.5789	65.0	65.0	142.0	0.4577	0.4577	46.0	47.0	118.0	0.3983	0.3898	0.0	0.0	0.0	0.0	0.0
0.0	32.0	32	7.4460	0.0058	6123.0898	4244.2024	339.0	570.0	0.5947	338.0	0.5930	135.0	135.0	158.0	0.8544	0.8544	89.0	89.0	152.0	0.5855	0.5855	66.0	66.0	142.0	0.4648	0.4648	48.0	49.0	118.0	0.4153	0.4068	0.0	0.0	0.0	0.0	0.0
0.0	33.0	33	7.4455	0.0058	6122.7347	4243.9563	337.0	570.0	0.5912	336.0	0.5895	135.0	135.0	158.0	0.8544	0.8544	88.0	88.0	152.0	0.5789	0.5789	65.0	65.0	142.0	0.4577	0.4577	48.0	49.0	118.0	0.4153	0.4068	0.0	0.0	0.0	0.0	0.0
0.0	34.0	34	7.4216	0.0058	6103.0761	4230.3300	340.0	570.0	0.5965	339.0	0.5947	135.0	135.0	158.0	0.8544	0.8544	89.0	89.0	152.0	0.5855	0.5855	67.0	67.0	142.0	0.4718	0.4718	48.0	49.0	118.0	0.4153	0.4068	0.0	0.0	0.0	0.0	0.0
0.0	35.0	35	7.4348	0.0058	6113.9214	4237.8474	340.0	570.0	0.5965	339.0	0.5947	135.0	135.0	158.0	0.8544	0.8544	88.0	88.0	152.0	0.5789	0.5789	67.0	67.0	142.0	0.4718	0.4718	49.0	50.0	118.0	0.4237	0.4153	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