ARC-Easy_Llama-3.2-1B-c5zpj7yt

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: 3.0644
• Model Preparation Time: 0.0056
• Mdl: 2519.9946
• Accumulated Loss: 1746.7271
• Correct Preds: 426.0
• Total Preds: 570.0
• Accuracy: 0.7474
• Correct Gen Preds: 424.0
• Gen Accuracy: 0.7439
• Correct Gen Preds 32: 112.0
• Correct Preds 32: 114.0
• Total Labels 32: 158.0
• Accuracy 32: 0.7215
• Gen Accuracy 32: 0.7089
• 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: 110.0
• Correct Preds 34: 110.0
• Total Labels 34: 142.0
• Accuracy 34: 0.7746
• Gen Accuracy 34: 0.7746
• Correct Gen Preds 35: 88.0
• Correct Preds 35: 88.0
• Total Labels 35: 118.0
• Accuracy 35: 0.7458
• Gen Accuracy 35: 0.7458
• 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
1.231	1.0	21	0.8642	0.0056	710.6478	492.5835	399.0	570.0	0.7	399.0	0.7	111.0	111.0	158.0	0.7025	0.7025	118.0	118.0	152.0	0.7763	0.7763	94.0	94.0	142.0	0.6620	0.6620	76.0	76.0	118.0	0.6441	0.6441	0.0	0.0	0.0	0.0	0.0
0.2582	2.0	42	0.8145	0.0056	669.8101	464.2770	412.0	570.0	0.7228	411.0	0.7211	111.0	112.0	158.0	0.7089	0.7025	108.0	108.0	152.0	0.7105	0.7105	114.0	114.0	142.0	0.8028	0.8028	78.0	78.0	118.0	0.6610	0.6610	0.0	0.0	0.0	0.0	0.0
0.3617	3.0	63	0.9462	0.0056	778.0698	539.3169	423.0	570.0	0.7421	423.0	0.7421	107.0	107.0	158.0	0.6772	0.6772	116.0	116.0	152.0	0.7632	0.7632	113.0	113.0	142.0	0.7958	0.7958	87.0	87.0	118.0	0.7373	0.7373	0.0	0.0	0.0	0.0	0.0
0.1217	4.0	84	1.0448	0.0056	859.1813	595.5391	411.0	570.0	0.7211	411.0	0.7211	111.0	111.0	158.0	0.7025	0.7025	108.0	108.0	152.0	0.7105	0.7105	115.0	115.0	142.0	0.8099	0.8099	77.0	77.0	118.0	0.6525	0.6525	0.0	0.0	0.0	0.0	0.0
0.0029	5.0	105	1.5061	0.0056	1238.5034	858.4652	413.0	570.0	0.7246	413.0	0.7246	113.0	113.0	158.0	0.7152	0.7152	121.0	121.0	152.0	0.7961	0.7961	114.0	114.0	142.0	0.8028	0.8028	65.0	65.0	118.0	0.5508	0.5508	0.0	0.0	0.0	0.0	0.0
0.0032	6.0	126	2.6304	0.0056	2163.0996	1499.3464	420.0	570.0	0.7368	420.0	0.7368	118.0	118.0	158.0	0.7468	0.7468	108.0	108.0	152.0	0.7105	0.7105	109.0	109.0	142.0	0.7676	0.7676	85.0	85.0	118.0	0.7203	0.7203	0.0	0.0	0.0	0.0	0.0
0.1536	7.0	147	2.2242	0.0056	1829.0571	1267.8058	386.0	570.0	0.6772	386.0	0.6772	122.0	122.0	158.0	0.7722	0.7722	124.0	124.0	152.0	0.8158	0.8158	70.0	70.0	142.0	0.4930	0.4930	70.0	70.0	118.0	0.5932	0.5932	0.0	0.0	0.0	0.0	0.0
0.0001	8.0	168	3.0644	0.0056	2519.9946	1746.7271	426.0	570.0	0.7474	424.0	0.7439	112.0	114.0	158.0	0.7215	0.7089	114.0	114.0	152.0	0.75	0.75	110.0	110.0	142.0	0.7746	0.7746	88.0	88.0	118.0	0.7458	0.7458	0.0	0.0	0.0	0.0	0.0
0.0	9.0	189	3.3656	0.0056	2767.6484	1918.3877	422.0	570.0	0.7404	421.0	0.7386	110.0	111.0	158.0	0.7025	0.6962	115.0	115.0	152.0	0.7566	0.7566	113.0	113.0	142.0	0.7958	0.7958	83.0	83.0	118.0	0.7034	0.7034	0.0	0.0	0.0	0.0	0.0
0.0	10.0	210	3.3896	0.0056	2787.3787	1932.0637	424.0	570.0	0.7439	423.0	0.7421	110.0	111.0	158.0	0.7025	0.6962	115.0	115.0	152.0	0.7566	0.7566	114.0	114.0	142.0	0.8028	0.8028	84.0	84.0	118.0	0.7119	0.7119	0.0	0.0	0.0	0.0	0.0
0.0	11.0	231	3.3985	0.0056	2794.7256	1937.1562	425.0	570.0	0.7456	424.0	0.7439	110.0	111.0	158.0	0.7025	0.6962	115.0	115.0	152.0	0.7566	0.7566	115.0	115.0	142.0	0.8099	0.8099	84.0	84.0	118.0	0.7119	0.7119	0.0	0.0	0.0	0.0	0.0
0.0	12.0	252	3.4000	0.0056	2795.9230	1937.9861	423.0	570.0	0.7421	422.0	0.7404	109.0	110.0	158.0	0.6962	0.6899	114.0	114.0	152.0	0.75	0.75	115.0	115.0	142.0	0.8099	0.8099	84.0	84.0	118.0	0.7119	0.7119	0.0	0.0	0.0	0.0	0.0
0.0	13.0	273	3.4058	0.0056	2800.6989	1941.2965	422.0	570.0	0.7404	421.0	0.7386	110.0	111.0	158.0	0.7025	0.6962	114.0	114.0	152.0	0.75	0.75	113.0	113.0	142.0	0.7958	0.7958	84.0	84.0	118.0	0.7119	0.7119	0.0	0.0	0.0	0.0	0.0
0.0	14.0	294	3.3965	0.0056	2793.0484	1935.9936	421.0	570.0	0.7386	420.0	0.7368	109.0	110.0	158.0	0.6962	0.6899	114.0	114.0	152.0	0.75	0.75	113.0	113.0	142.0	0.7958	0.7958	84.0	84.0	118.0	0.7119	0.7119	0.0	0.0	0.0	0.0	0.0
0.0	15.0	315	3.4048	0.0056	2799.9003	1940.7430	425.0	570.0	0.7456	424.0	0.7439	110.0	111.0	158.0	0.7025	0.6962	115.0	115.0	152.0	0.7566	0.7566	115.0	115.0	142.0	0.8099	0.8099	84.0	84.0	118.0	0.7119	0.7119	0.0	0.0	0.0	0.0	0.0
0.0	16.0	336	3.4080	0.0056	2802.5538	1942.5823	422.0	570.0	0.7404	421.0	0.7386	109.0	110.0	158.0	0.6962	0.6899	115.0	115.0	152.0	0.7566	0.7566	114.0	114.0	142.0	0.8028	0.8028	83.0	83.0	118.0	0.7034	0.7034	0.0	0.0	0.0	0.0	0.0
0.0	17.0	357	3.4077	0.0056	2802.2653	1942.3823	422.0	570.0	0.7404	421.0	0.7386	110.0	111.0	158.0	0.7025	0.6962	114.0	114.0	152.0	0.75	0.75	113.0	113.0	142.0	0.7958	0.7958	84.0	84.0	118.0	0.7119	0.7119	0.0	0.0	0.0	0.0	0.0
0.0	18.0	378	3.4307	0.0056	2821.2035	1955.5093	425.0	570.0	0.7456	423.0	0.7421	110.0	112.0	158.0	0.7089	0.6962	114.0	114.0	152.0	0.75	0.75	115.0	115.0	142.0	0.8099	0.8099	84.0	84.0	118.0	0.7119	0.7119	0.0	0.0	0.0	0.0	0.0
0.0	19.0	399	3.4143	0.0056	2807.7121	1946.1577	423.0	570.0	0.7421	422.0	0.7404	110.0	111.0	158.0	0.7025	0.6962	114.0	114.0	152.0	0.75	0.75	114.0	114.0	142.0	0.8028	0.8028	84.0	84.0	118.0	0.7119	0.7119	0.0	0.0	0.0	0.0	0.0
0.0	20.0	420	3.4168	0.0056	2809.7511	1947.5710	425.0	570.0	0.7456	423.0	0.7421	110.0	112.0	158.0	0.7089	0.6962	115.0	115.0	152.0	0.7566	0.7566	114.0	114.0	142.0	0.8028	0.8028	84.0	84.0	118.0	0.7119	0.7119	0.0	0.0	0.0	0.0	0.0
0.0	21.0	441	3.4299	0.0056	2820.5617	1955.0644	420.0	570.0	0.7368	419.0	0.7351	109.0	110.0	158.0	0.6962	0.6899	114.0	114.0	152.0	0.75	0.75	112.0	112.0	142.0	0.7887	0.7887	84.0	84.0	118.0	0.7119	0.7119	0.0	0.0	0.0	0.0	0.0
0.0	22.0	462	3.4188	0.0056	2811.3780	1948.6987	425.0	570.0	0.7456	424.0	0.7439	110.0	111.0	158.0	0.7025	0.6962	114.0	114.0	152.0	0.75	0.75	115.0	115.0	142.0	0.8099	0.8099	85.0	85.0	118.0	0.7203	0.7203	0.0	0.0	0.0	0.0	0.0
0.0	23.0	483	3.4234	0.0056	2815.1498	1951.3131	422.0	570.0	0.7404	421.0	0.7386	109.0	110.0	158.0	0.6962	0.6899	114.0	114.0	152.0	0.75	0.75	114.0	114.0	142.0	0.8028	0.8028	84.0	84.0	118.0	0.7119	0.7119	0.0	0.0	0.0	0.0	0.0
0.0	24.0	504	3.4272	0.0056	2818.3485	1953.5303	423.0	570.0	0.7421	422.0	0.7404	110.0	111.0	158.0	0.7025	0.6962	114.0	114.0	152.0	0.75	0.75	114.0	114.0	142.0	0.8028	0.8028	84.0	84.0	118.0	0.7119	0.7119	0.0	0.0	0.0	0.0	0.0
0.0	25.0	525	3.4370	0.0056	2826.3394	1959.0692	426.0	570.0	0.7474	424.0	0.7439	110.0	112.0	158.0	0.7089	0.6962	115.0	115.0	152.0	0.7566	0.7566	115.0	115.0	142.0	0.8099	0.8099	84.0	84.0	118.0	0.7119	0.7119	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