ARC-Easy_Llama-3.2-1B-41kqc86o

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: 2.3820
• Model Preparation Time: 0.006
• Mdl: 1958.8296
• Accumulated Loss: 1357.7572
• Correct Preds: 425.0
• Total Preds: 570.0
• Accuracy: 0.7456
• Correct Gen Preds: 424.0
• Gen Accuracy: 0.7439
• Correct Gen Preds 32: 114.0
• Correct Preds 32: 115.0
• Total Labels 32: 158.0
• Accuracy 32: 0.7278
• Gen Accuracy 32: 0.7215
• Correct Gen Preds 33: 122.0
• Correct Preds 33: 122.0
• Total Labels 33: 152.0
• Accuracy 33: 0.8026
• Gen Accuracy 33: 0.8026
• Correct Gen Preds 34: 114.0
• Correct Preds 34: 114.0
• Total Labels 34: 142.0
• Accuracy 34: 0.8028
• Gen Accuracy 34: 0.8028
• Correct Gen Preds 35: 74.0
• Correct Preds 35: 74.0
• Total Labels 35: 118.0
• Accuracy 35: 0.6271
• Gen Accuracy 35: 0.6271
• 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	0.006	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
0.9278	1.0	17	0.9560	0.006	786.1592	544.9240	363.0	570.0	0.6368	362.0	0.6351	75.0	76.0	158.0	0.4810	0.4747	118.0	118.0	152.0	0.7763	0.7763	101.0	101.0	142.0	0.7113	0.7113	68.0	68.0	118.0	0.5763	0.5763	0.0	0.0	0.0	0.0	0.0
0.5026	2.0	34	0.7886	0.006	648.5231	449.5220	421.0	570.0	0.7386	420.0	0.7368	114.0	115.0	158.0	0.7278	0.7215	111.0	111.0	152.0	0.7303	0.7303	110.0	110.0	142.0	0.7746	0.7746	85.0	85.0	118.0	0.7203	0.7203	0.0	0.0	0.0	0.0	0.0
0.1634	3.0	51	1.2834	0.006	1055.3659	731.5239	414.0	570.0	0.7263	411.0	0.7211	107.0	109.0	158.0	0.6899	0.6772	118.0	118.0	152.0	0.7763	0.7763	100.0	101.0	142.0	0.7113	0.7042	86.0	86.0	118.0	0.7288	0.7288	0.0	0.0	0.0	0.0	0.0
0.0839	4.0	68	1.5344	0.006	1261.7559	874.5825	423.0	570.0	0.7421	422.0	0.7404	101.0	102.0	158.0	0.6456	0.6392	129.0	129.0	152.0	0.8487	0.8487	116.0	116.0	142.0	0.8169	0.8169	76.0	76.0	118.0	0.6441	0.6441	0.0	0.0	0.0	0.0	0.0
0.0711	5.0	85	1.9035	0.006	1565.2957	1084.9803	418.0	570.0	0.7333	417.0	0.7316	101.0	102.0	158.0	0.6456	0.6392	124.0	124.0	152.0	0.8158	0.8158	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
0.1712	6.0	102	2.1386	0.006	1758.6841	1219.0269	414.0	570.0	0.7263	411.0	0.7211	126.0	128.0	158.0	0.8101	0.7975	114.0	115.0	152.0	0.7566	0.75	101.0	101.0	142.0	0.7113	0.7113	70.0	70.0	118.0	0.5932	0.5932	0.0	0.0	0.0	0.0	0.0
0.1767	7.0	119	2.3820	0.006	1958.8296	1357.7572	425.0	570.0	0.7456	424.0	0.7439	114.0	115.0	158.0	0.7278	0.7215	122.0	122.0	152.0	0.8026	0.8026	114.0	114.0	142.0	0.8028	0.8028	74.0	74.0	118.0	0.6271	0.6271	0.0	0.0	0.0	0.0	0.0
0.0158	8.0	136	2.0511	0.006	1686.6677	1169.1090	414.0	570.0	0.7263	413.0	0.7246	115.0	116.0	158.0	0.7342	0.7278	110.0	110.0	152.0	0.7237	0.7237	115.0	115.0	142.0	0.8099	0.8099	73.0	73.0	118.0	0.6186	0.6186	0.0	0.0	0.0	0.0	0.0
0.0001	9.0	153	2.8882	0.006	2375.0503	1646.2594	405.0	570.0	0.7105	398.0	0.6982	84.0	90.0	158.0	0.5696	0.5316	120.0	121.0	152.0	0.7961	0.7895	110.0	110.0	142.0	0.7746	0.7746	84.0	84.0	118.0	0.7119	0.7119	0.0	0.0	0.0	0.0	0.0
0.0	10.0	170	2.7777	0.006	2284.1916	1583.2809	414.0	570.0	0.7263	413.0	0.7246	106.0	107.0	158.0	0.6772	0.6709	122.0	122.0	152.0	0.8026	0.8026	112.0	112.0	142.0	0.7887	0.7887	73.0	73.0	118.0	0.6186	0.6186	0.0	0.0	0.0	0.0	0.0
0.0	11.0	187	2.8946	0.006	2380.2971	1649.8962	410.0	570.0	0.7193	409.0	0.7175	99.0	100.0	158.0	0.6329	0.6266	122.0	122.0	152.0	0.8026	0.8026	112.0	112.0	142.0	0.7887	0.7887	76.0	76.0	118.0	0.6441	0.6441	0.0	0.0	0.0	0.0	0.0
0.0	12.0	204	2.9371	0.006	2415.2833	1674.1468	411.0	570.0	0.7211	410.0	0.7193	97.0	98.0	158.0	0.6203	0.6139	121.0	121.0	152.0	0.7961	0.7961	113.0	113.0	142.0	0.7958	0.7958	79.0	79.0	118.0	0.6695	0.6695	0.0	0.0	0.0	0.0	0.0
0.0	13.0	221	3.0100	0.006	2475.2057	1715.6818	408.0	570.0	0.7158	407.0	0.7140	97.0	98.0	158.0	0.6203	0.6139	119.0	119.0	152.0	0.7829	0.7829	111.0	111.0	142.0	0.7817	0.7817	80.0	80.0	118.0	0.6780	0.6780	0.0	0.0	0.0	0.0	0.0
0.0	14.0	238	3.0076	0.006	2473.2184	1714.3044	409.0	570.0	0.7175	408.0	0.7158	97.0	98.0	158.0	0.6203	0.6139	120.0	120.0	152.0	0.7895	0.7895	112.0	112.0	142.0	0.7887	0.7887	79.0	79.0	118.0	0.6695	0.6695	0.0	0.0	0.0	0.0	0.0
0.0	15.0	255	3.0392	0.006	2499.2324	1732.3359	408.0	570.0	0.7158	407.0	0.7140	96.0	97.0	158.0	0.6139	0.6076	120.0	120.0	152.0	0.7895	0.7895	112.0	112.0	142.0	0.7887	0.7887	79.0	79.0	118.0	0.6695	0.6695	0.0	0.0	0.0	0.0	0.0
0.0	16.0	272	3.0357	0.006	2496.3936	1730.3682	408.0	570.0	0.7158	407.0	0.7140	96.0	97.0	158.0	0.6139	0.6076	120.0	120.0	152.0	0.7895	0.7895	112.0	112.0	142.0	0.7887	0.7887	79.0	79.0	118.0	0.6695	0.6695	0.0	0.0	0.0	0.0	0.0
0.0	17.0	289	3.0563	0.006	2513.3049	1742.0902	409.0	570.0	0.7175	408.0	0.7158	96.0	97.0	158.0	0.6139	0.6076	120.0	120.0	152.0	0.7895	0.7895	112.0	112.0	142.0	0.7887	0.7887	80.0	80.0	118.0	0.6780	0.6780	0.0	0.0	0.0	0.0	0.0
0.0	18.0	306	3.0748	0.006	2528.4881	1752.6144	409.0	570.0	0.7175	408.0	0.7158	97.0	98.0	158.0	0.6203	0.6139	120.0	120.0	152.0	0.7895	0.7895	112.0	112.0	142.0	0.7887	0.7887	79.0	79.0	118.0	0.6695	0.6695	0.0	0.0	0.0	0.0	0.0
0.0	19.0	323	3.0687	0.006	2523.4931	1749.1521	410.0	570.0	0.7193	409.0	0.7175	97.0	98.0	158.0	0.6203	0.6139	120.0	120.0	152.0	0.7895	0.7895	112.0	112.0	142.0	0.7887	0.7887	80.0	80.0	118.0	0.6780	0.6780	0.0	0.0	0.0	0.0	0.0
0.0	20.0	340	3.0976	0.006	2547.2585	1765.6251	406.0	570.0	0.7123	405.0	0.7105	95.0	96.0	158.0	0.6076	0.6013	119.0	119.0	152.0	0.7829	0.7829	112.0	112.0	142.0	0.7887	0.7887	79.0	79.0	118.0	0.6695	0.6695	0.0	0.0	0.0	0.0	0.0
0.0	21.0	357	3.0811	0.006	2533.6756	1756.2101	409.0	570.0	0.7175	408.0	0.7158	96.0	97.0	158.0	0.6139	0.6076	120.0	120.0	152.0	0.7895	0.7895	112.0	112.0	142.0	0.7887	0.7887	80.0	80.0	118.0	0.6780	0.6780	0.0	0.0	0.0	0.0	0.0
0.0	22.0	374	3.1021	0.006	2550.9523	1768.1854	408.0	570.0	0.7158	407.0	0.7140	96.0	97.0	158.0	0.6139	0.6076	120.0	120.0	152.0	0.7895	0.7895	112.0	112.0	142.0	0.7887	0.7887	79.0	79.0	118.0	0.6695	0.6695	0.0	0.0	0.0	0.0	0.0
0.0	23.0	391	3.1118	0.006	2558.9477	1773.7274	409.0	570.0	0.7175	408.0	0.7158	96.0	97.0	158.0	0.6139	0.6076	120.0	120.0	152.0	0.7895	0.7895	112.0	112.0	142.0	0.7887	0.7887	80.0	80.0	118.0	0.6780	0.6780	0.0	0.0	0.0	0.0	0.0
0.0	24.0	408	3.1083	0.006	2556.0379	1771.7105	408.0	570.0	0.7158	407.0	0.7140	96.0	97.0	158.0	0.6139	0.6076	120.0	120.0	152.0	0.7895	0.7895	112.0	112.0	142.0	0.7887	0.7887	79.0	79.0	118.0	0.6695	0.6695	0.0	0.0	0.0	0.0	0.0
0.0	25.0	425	3.1119	0.006	2559.0555	1773.8021	410.0	570.0	0.7193	409.0	0.7175	97.0	98.0	158.0	0.6203	0.6139	120.0	120.0	152.0	0.7895	0.7895	112.0	112.0	142.0	0.7887	0.7887	80.0	80.0	118.0	0.6780	0.6780	0.0	0.0	0.0	0.0	0.0
0.0	26.0	442	3.1180	0.006	2564.0249	1777.2466	408.0	570.0	0.7158	407.0	0.7140	96.0	97.0	158.0	0.6139	0.6076	120.0	120.0	152.0	0.7895	0.7895	112.0	112.0	142.0	0.7887	0.7887	79.0	79.0	118.0	0.6695	0.6695	0.0	0.0	0.0	0.0	0.0
0.0	27.0	459	3.1338	0.006	2577.0435	1786.2704	408.0	570.0	0.7158	407.0	0.7140	96.0	97.0	158.0	0.6139	0.6076	120.0	120.0	152.0	0.7895	0.7895	112.0	112.0	142.0	0.7887	0.7887	79.0	79.0	118.0	0.6695	0.6695	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