ARC-Easy_Llama-3.2-1B-5p7mxi8l

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: 0.7052
• Model Preparation Time: 0.0056
• Mdl: 579.8957
• Accumulated Loss: 401.9531
• Correct Preds: 437.0
• Total Preds: 570.0
• Accuracy: 0.7667
• Correct Gen Preds: 436.0
• Gen Accuracy: 0.7649
• Correct Gen Preds 32: 129.0
• Correct Preds 32: 130.0
• Total Labels 32: 158.0
• Accuracy 32: 0.8228
• Gen Accuracy 32: 0.8165
• Correct Gen Preds 33: 116.0
• Correct Preds 33: 116.0
• Total Labels 33: 152.0
• Accuracy 33: 0.7632
• Gen Accuracy 33: 0.7632
• Correct Gen Preds 34: 108.0
• Correct Preds 34: 108.0
• Total Labels 34: 142.0
• Accuracy 34: 0.7606
• Gen Accuracy 34: 0.7606
• Correct Gen Preds 35: 83.0
• Correct Preds 35: 83.0
• Total Labels 35: 118.0
• Accuracy 35: 0.7034
• Gen Accuracy 35: 0.7034
• 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	0.0056	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
0.8152	1.0	26	0.7928	0.0056	651.9305	451.8838	414.0	570.0	0.7263	414.0	0.7263	128.0	128.0	158.0	0.8101	0.8101	108.0	108.0	152.0	0.7105	0.7105	103.0	103.0	142.0	0.7254	0.7254	75.0	75.0	118.0	0.6356	0.6356	0.0	0.0	0.0	0.0	0.0
0.3843	2.0	52	0.7052	0.0056	579.8957	401.9531	437.0	570.0	0.7667	436.0	0.7649	129.0	130.0	158.0	0.8228	0.8165	116.0	116.0	152.0	0.7632	0.7632	108.0	108.0	142.0	0.7606	0.7606	83.0	83.0	118.0	0.7034	0.7034	0.0	0.0	0.0	0.0	0.0
0.2692	3.0	78	0.8492	0.0056	698.3545	484.0624	432.0	570.0	0.7579	432.0	0.7579	114.0	114.0	158.0	0.7215	0.7215	123.0	123.0	152.0	0.8092	0.8092	114.0	114.0	142.0	0.8028	0.8028	81.0	81.0	118.0	0.6864	0.6864	0.0	0.0	0.0	0.0	0.0
0.0474	4.0	104	1.3013	0.0056	1070.0786	741.7219	405.0	570.0	0.7105	64.0	0.1123	2.0	98.0	158.0	0.6203	0.0127	25.0	117.0	152.0	0.7697	0.1645	25.0	120.0	142.0	0.8451	0.1761	12.0	70.0	118.0	0.5932	0.1017	0.0	0.0	0.0	0.0	0.0
0.063	5.0	130	1.8921	0.0056	1555.9118	1078.4759	435.0	570.0	0.7632	424.0	0.7439	109.0	120.0	158.0	0.7595	0.6899	118.0	118.0	152.0	0.7763	0.7763	115.0	115.0	142.0	0.8099	0.8099	82.0	82.0	118.0	0.6949	0.6949	0.0	0.0	0.0	0.0	0.0
0.0876	6.0	156	1.4352	0.0056	1180.2063	818.0567	421.0	570.0	0.7386	404.0	0.7088	84.0	101.0	158.0	0.6392	0.5316	122.0	122.0	152.0	0.8026	0.8026	118.0	118.0	142.0	0.8310	0.8310	80.0	80.0	118.0	0.6780	0.6780	0.0	0.0	0.0	0.0	0.0
0.2587	7.0	182	2.4597	0.0056	2022.7388	1402.0557	436.0	570.0	0.7649	436.0	0.7649	118.0	118.0	158.0	0.7468	0.7468	123.0	123.0	152.0	0.8092	0.8092	121.0	121.0	142.0	0.8521	0.8521	74.0	74.0	118.0	0.6271	0.6271	0.0	0.0	0.0	0.0	0.0
0.0023	8.0	208	2.2028	0.0056	1811.4433	1255.5968	434.0	570.0	0.7614	434.0	0.7614	125.0	125.0	158.0	0.7911	0.7911	115.0	115.0	152.0	0.7566	0.7566	116.0	116.0	142.0	0.8169	0.8169	78.0	78.0	118.0	0.6610	0.6610	0.0	0.0	0.0	0.0	0.0
0.0001	9.0	234	2.1737	0.0056	1787.5456	1239.0322	435.0	570.0	0.7632	435.0	0.7632	123.0	123.0	158.0	0.7785	0.7785	113.0	113.0	152.0	0.7434	0.7434	119.0	119.0	142.0	0.8380	0.8380	80.0	80.0	118.0	0.6780	0.6780	0.0	0.0	0.0	0.0	0.0
0.0	10.0	260	2.3012	0.0056	1892.3237	1311.6588	433.0	570.0	0.7596	433.0	0.7596	119.0	119.0	158.0	0.7532	0.7532	113.0	113.0	152.0	0.7434	0.7434	119.0	119.0	142.0	0.8380	0.8380	82.0	82.0	118.0	0.6949	0.6949	0.0	0.0	0.0	0.0	0.0
0.0	11.0	286	2.3707	0.0056	1949.4977	1351.2888	429.0	570.0	0.7526	429.0	0.7526	120.0	120.0	158.0	0.7595	0.7595	113.0	113.0	152.0	0.7434	0.7434	119.0	119.0	142.0	0.8380	0.8380	77.0	77.0	118.0	0.6525	0.6525	0.0	0.0	0.0	0.0	0.0
0.0	12.0	312	2.4007	0.0056	1974.2088	1368.4173	428.0	570.0	0.7509	428.0	0.7509	118.0	118.0	158.0	0.7468	0.7468	114.0	114.0	152.0	0.75	0.75	118.0	118.0	142.0	0.8310	0.8310	78.0	78.0	118.0	0.6610	0.6610	0.0	0.0	0.0	0.0	0.0
0.0	13.0	338	2.3878	0.0056	1963.5566	1361.0337	430.0	570.0	0.7544	430.0	0.7544	119.0	119.0	158.0	0.7532	0.7532	113.0	113.0	152.0	0.7434	0.7434	119.0	119.0	142.0	0.8380	0.8380	79.0	79.0	118.0	0.6695	0.6695	0.0	0.0	0.0	0.0	0.0
0.0	14.0	364	2.4055	0.0056	1978.1533	1371.1514	430.0	570.0	0.7544	430.0	0.7544	119.0	119.0	158.0	0.7532	0.7532	113.0	113.0	152.0	0.7434	0.7434	119.0	119.0	142.0	0.8380	0.8380	79.0	79.0	118.0	0.6695	0.6695	0.0	0.0	0.0	0.0	0.0
0.0	15.0	390	2.3994	0.0056	1973.0895	1367.6414	432.0	570.0	0.7579	432.0	0.7579	121.0	121.0	158.0	0.7658	0.7658	114.0	114.0	152.0	0.75	0.75	119.0	119.0	142.0	0.8380	0.8380	78.0	78.0	118.0	0.6610	0.6610	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