ARC-Easy_Llama-3.2-1B-8984x13s

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: 3.0916
• Model Preparation Time: 0.0059
• Mdl: 2542.3237
• Accumulated Loss: 1762.2045
• Correct Preds: 378.0
• Total Preds: 570.0
• Accuracy: 0.6632
• Correct Gen Preds: 378.0
• Gen Accuracy: 0.6632
• Correct Gen Preds 32: 131.0
• Correct Preds 32: 131.0
• Total Labels 32: 158.0
• Accuracy 32: 0.8291
• Gen Accuracy 32: 0.8291
• Correct Gen Preds 33: 96.0
• Correct Preds 33: 96.0
• Total Labels 33: 152.0
• Accuracy 33: 0.6316
• Gen Accuracy 33: 0.6316
• Correct Gen Preds 34: 85.0
• Correct Preds 34: 85.0
• Total Labels 34: 142.0
• Accuracy 34: 0.5986
• Gen Accuracy 34: 0.5986
• Correct Gen Preds 35: 66.0
• Correct Preds 35: 66.0
• Total Labels 35: 118.0
• Accuracy 35: 0.5593
• Gen Accuracy 35: 0.5593
• 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	0.0059	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
1.435	1.0	1	2.1188	0.0059	1742.3509	1207.7056	183.0	570.0	0.3211	183.0	0.3211	0.0	0.0	158.0	0.0	0.0	151.0	151.0	152.0	0.9934	0.9934	32.0	32.0	142.0	0.2254	0.2254	0.0	0.0	118.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
2.005	2.0	2	1.3228	0.0059	1087.7696	753.9844	166.0	570.0	0.2912	166.0	0.2912	157.0	157.0	158.0	0.9937	0.9937	7.0	7.0	152.0	0.0461	0.0461	2.0	2.0	142.0	0.0141	0.0141	0.0	0.0	118.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.9558	3.0	3	2.0441	0.0059	1680.9188	1165.1241	219.0	570.0	0.3842	219.0	0.3842	154.0	154.0	158.0	0.9747	0.9747	3.0	3.0	152.0	0.0197	0.0197	44.0	44.0	142.0	0.3099	0.3099	18.0	18.0	118.0	0.1525	0.1525	0.0	0.0	0.0	0.0	0.0
0.4507	4.0	4	1.4535	0.0059	1195.2711	828.4988	345.0	570.0	0.6053	344.0	0.6035	132.0	133.0	158.0	0.8418	0.8354	77.0	77.0	152.0	0.5066	0.5066	86.0	86.0	142.0	0.6056	0.6056	49.0	49.0	118.0	0.4153	0.4153	0.0	0.0	0.0	0.0	0.0
0.0879	5.0	5	1.6615	0.0059	1366.3466	947.0793	377.0	570.0	0.6614	374.0	0.6561	115.0	117.0	158.0	0.7405	0.7278	110.0	110.0	152.0	0.7237	0.7237	92.0	93.0	142.0	0.6549	0.6479	57.0	57.0	118.0	0.4831	0.4831	0.0	0.0	0.0	0.0	0.0
0.0036	6.0	6	3.0916	0.0059	2542.3237	1762.2045	378.0	570.0	0.6632	378.0	0.6632	131.0	131.0	158.0	0.8291	0.8291	96.0	96.0	152.0	0.6316	0.6316	85.0	85.0	142.0	0.5986	0.5986	66.0	66.0	118.0	0.5593	0.5593	0.0	0.0	0.0	0.0	0.0
0.0	7.0	7	4.4610	0.0059	3668.4395	2542.7685	369.0	570.0	0.6474	369.0	0.6474	136.0	136.0	158.0	0.8608	0.8608	83.0	83.0	152.0	0.5461	0.5461	85.0	85.0	142.0	0.5986	0.5986	65.0	65.0	118.0	0.5508	0.5508	0.0	0.0	0.0	0.0	0.0
0.0	8.0	8	5.2835	0.0059	4344.8373	3011.6117	364.0	570.0	0.6386	364.0	0.6386	137.0	137.0	158.0	0.8671	0.8671	81.0	81.0	152.0	0.5329	0.5329	81.0	81.0	142.0	0.5704	0.5704	65.0	65.0	118.0	0.5508	0.5508	0.0	0.0	0.0	0.0	0.0
0.0	9.0	9	5.7743	0.0059	4748.4422	3291.3693	360.0	570.0	0.6316	360.0	0.6316	138.0	138.0	158.0	0.8734	0.8734	79.0	79.0	152.0	0.5197	0.5197	80.0	80.0	142.0	0.5634	0.5634	63.0	63.0	118.0	0.5339	0.5339	0.0	0.0	0.0	0.0	0.0
0.0	10.0	10	6.0608	0.0059	4984.0139	3454.6552	355.0	570.0	0.6228	355.0	0.6228	139.0	139.0	158.0	0.8797	0.8797	76.0	76.0	152.0	0.5	0.5	79.0	79.0	142.0	0.5563	0.5563	61.0	61.0	118.0	0.5169	0.5169	0.0	0.0	0.0	0.0	0.0
0.0	11.0	11	6.2689	0.0059	5155.1789	3573.2977	353.0	570.0	0.6193	353.0	0.6193	137.0	137.0	158.0	0.8671	0.8671	77.0	77.0	152.0	0.5066	0.5066	79.0	79.0	142.0	0.5563	0.5563	60.0	60.0	118.0	0.5085	0.5085	0.0	0.0	0.0	0.0	0.0
0.0	12.0	12	6.3825	0.0059	5248.5200	3637.9969	352.0	570.0	0.6175	352.0	0.6175	138.0	138.0	158.0	0.8734	0.8734	75.0	75.0	152.0	0.4934	0.4934	78.0	78.0	142.0	0.5493	0.5493	61.0	61.0	118.0	0.5169	0.5169	0.0	0.0	0.0	0.0	0.0
0.0	13.0	13	6.4953	0.0059	5341.3420	3702.3361	349.0	570.0	0.6123	349.0	0.6123	139.0	139.0	158.0	0.8797	0.8797	74.0	74.0	152.0	0.4868	0.4868	78.0	78.0	142.0	0.5493	0.5493	58.0	58.0	118.0	0.4915	0.4915	0.0	0.0	0.0	0.0	0.0
0.0	14.0	14	6.5606	0.0059	5395.0053	3739.5327	352.0	570.0	0.6175	352.0	0.6175	141.0	141.0	158.0	0.8924	0.8924	75.0	75.0	152.0	0.4934	0.4934	76.0	76.0	142.0	0.5352	0.5352	60.0	60.0	118.0	0.5085	0.5085	0.0	0.0	0.0	0.0	0.0
0.0	15.0	15	6.6424	0.0059	5462.2665	3786.1546	345.0	570.0	0.6053	345.0	0.6053	139.0	139.0	158.0	0.8797	0.8797	73.0	73.0	152.0	0.4803	0.4803	75.0	75.0	142.0	0.5282	0.5282	58.0	58.0	118.0	0.4915	0.4915	0.0	0.0	0.0	0.0	0.0
0.0	16.0	16	6.7109	0.0059	5518.6330	3825.2249	344.0	570.0	0.6035	344.0	0.6035	140.0	140.0	158.0	0.8861	0.8861	71.0	71.0	152.0	0.4671	0.4671	75.0	75.0	142.0	0.5282	0.5282	58.0	58.0	118.0	0.4915	0.4915	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