ARC-Easy_Llama-3.2-1B-szzhws4z

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: 2.5495
• Model Preparation Time: 0.006
• Mdl: 2096.5226
• Accumulated Loss: 1453.1987
• Correct Preds: 411.0
• Total Preds: 570.0
• Accuracy: 0.7211
• Correct Gen Preds: 410.0
• Gen Accuracy: 0.7193
• Correct Gen Preds 32: 112.0
• Correct Preds 32: 113.0
• Total Labels 32: 158.0
• Accuracy 32: 0.7152
• Gen Accuracy 32: 0.7089
• Correct Gen Preds 33: 117.0
• Correct Preds 33: 117.0
• Total Labels 33: 152.0
• Accuracy 33: 0.7697
• Gen Accuracy 33: 0.7697
• 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: 73.0
• Correct Preds 35: 73.0
• Total Labels 35: 118.0
• Accuracy 35: 0.6186
• Gen Accuracy 35: 0.6186
• 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
0.6626	1.0	11	0.9446	0.006	776.7691	538.4153	380.0	570.0	0.6667	380.0	0.6667	122.0	122.0	158.0	0.7722	0.7722	103.0	103.0	152.0	0.6776	0.6776	94.0	94.0	142.0	0.6620	0.6620	61.0	61.0	118.0	0.5169	0.5169	0.0	0.0	0.0	0.0	0.0
0.4951	2.0	22	0.8259	0.006	679.1973	470.7837	406.0	570.0	0.7123	406.0	0.7123	100.0	100.0	158.0	0.6329	0.6329	111.0	111.0	152.0	0.7303	0.7303	115.0	115.0	142.0	0.8099	0.8099	80.0	80.0	118.0	0.6780	0.6780	0.0	0.0	0.0	0.0	0.0
0.1373	3.0	33	0.9945	0.006	817.8361	566.8808	408.0	570.0	0.7158	408.0	0.7158	115.0	115.0	158.0	0.7278	0.7278	114.0	114.0	152.0	0.75	0.75	96.0	96.0	142.0	0.6761	0.6761	83.0	83.0	118.0	0.7034	0.7034	0.0	0.0	0.0	0.0	0.0
0.0001	4.0	44	2.5495	0.006	2096.5226	1453.1987	411.0	570.0	0.7211	410.0	0.7193	112.0	113.0	158.0	0.7152	0.7089	117.0	117.0	152.0	0.7697	0.7697	108.0	108.0	142.0	0.7606	0.7606	73.0	73.0	118.0	0.6186	0.6186	0.0	0.0	0.0	0.0	0.0
0.0002	5.0	55	2.4175	0.006	1988.0273	1377.9955	406.0	570.0	0.7123	406.0	0.7123	98.0	98.0	158.0	0.6203	0.6203	117.0	117.0	152.0	0.7697	0.7697	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	6.0	66	2.4839	0.006	2042.5931	1415.8176	404.0	570.0	0.7088	404.0	0.7088	117.0	117.0	158.0	0.7405	0.7405	108.0	108.0	152.0	0.7105	0.7105	107.0	107.0	142.0	0.7535	0.7535	72.0	72.0	118.0	0.6102	0.6102	0.0	0.0	0.0	0.0	0.0
0.0001	7.0	77	2.6310	0.006	2163.5625	1499.6673	406.0	570.0	0.7123	406.0	0.7123	123.0	123.0	158.0	0.7785	0.7785	106.0	106.0	152.0	0.6974	0.6974	103.0	103.0	142.0	0.7254	0.7254	74.0	74.0	118.0	0.6271	0.6271	0.0	0.0	0.0	0.0	0.0
0.0	8.0	88	2.7352	0.006	2249.2482	1559.0600	400.0	570.0	0.7018	400.0	0.7018	112.0	112.0	158.0	0.7089	0.7089	108.0	108.0	152.0	0.7105	0.7105	101.0	101.0	142.0	0.7113	0.7113	79.0	79.0	118.0	0.6695	0.6695	0.0	0.0	0.0	0.0	0.0
0.0	9.0	99	2.7916	0.006	2295.6207	1591.2030	400.0	570.0	0.7018	400.0	0.7018	111.0	111.0	158.0	0.7025	0.7025	109.0	109.0	152.0	0.7171	0.7171	101.0	101.0	142.0	0.7113	0.7113	79.0	79.0	118.0	0.6695	0.6695	0.0	0.0	0.0	0.0	0.0
0.0	10.0	110	2.8267	0.006	2324.5097	1611.2273	396.0	570.0	0.6947	396.0	0.6947	109.0	109.0	158.0	0.6899	0.6899	109.0	109.0	152.0	0.7171	0.7171	99.0	99.0	142.0	0.6972	0.6972	79.0	79.0	118.0	0.6695	0.6695	0.0	0.0	0.0	0.0	0.0
0.0	11.0	121	2.8506	0.006	2344.1674	1624.8530	398.0	570.0	0.6982	398.0	0.6982	110.0	110.0	158.0	0.6962	0.6962	110.0	110.0	152.0	0.7237	0.7237	100.0	100.0	142.0	0.7042	0.7042	78.0	78.0	118.0	0.6610	0.6610	0.0	0.0	0.0	0.0	0.0
0.0	12.0	132	2.8582	0.006	2350.3907	1629.1667	401.0	570.0	0.7035	401.0	0.7035	111.0	111.0	158.0	0.7025	0.7025	110.0	110.0	152.0	0.7237	0.7237	102.0	102.0	142.0	0.7183	0.7183	78.0	78.0	118.0	0.6610	0.6610	0.0	0.0	0.0	0.0	0.0
0.0	13.0	143	2.8602	0.006	2352.0259	1630.3001	400.0	570.0	0.7018	400.0	0.7018	109.0	109.0	158.0	0.6899	0.6899	111.0	111.0	152.0	0.7303	0.7303	102.0	102.0	142.0	0.7183	0.7183	78.0	78.0	118.0	0.6610	0.6610	0.0	0.0	0.0	0.0	0.0
0.0	14.0	154	2.8996	0.006	2384.4652	1652.7854	401.0	570.0	0.7035	401.0	0.7035	112.0	112.0	158.0	0.7089	0.7089	111.0	111.0	152.0	0.7303	0.7303	99.0	99.0	142.0	0.6972	0.6972	79.0	79.0	118.0	0.6695	0.6695	0.0	0.0	0.0	0.0	0.0
0.0	15.0	165	2.8866	0.006	2373.7384	1645.3501	401.0	570.0	0.7035	401.0	0.7035	110.0	110.0	158.0	0.6962	0.6962	111.0	111.0	152.0	0.7303	0.7303	100.0	100.0	142.0	0.7042	0.7042	80.0	80.0	118.0	0.6780	0.6780	0.0	0.0	0.0	0.0	0.0
0.0	16.0	176	2.8719	0.006	2361.6528	1636.9730	403.0	570.0	0.7070	403.0	0.7070	111.0	111.0	158.0	0.7025	0.7025	112.0	112.0	152.0	0.7368	0.7368	100.0	100.0	142.0	0.7042	0.7042	80.0	80.0	118.0	0.6780	0.6780	0.0	0.0	0.0	0.0	0.0
0.0	17.0	187	2.9309	0.006	2410.2127	1670.6321	400.0	570.0	0.7018	400.0	0.7018	111.0	111.0	158.0	0.7025	0.7025	110.0	110.0	152.0	0.7237	0.7237	100.0	100.0	142.0	0.7042	0.7042	79.0	79.0	118.0	0.6695	0.6695	0.0	0.0	0.0	0.0	0.0
0.0	18.0	198	2.9284	0.006	2408.1119	1669.1760	400.0	570.0	0.7018	400.0	0.7018	110.0	110.0	158.0	0.6962	0.6962	111.0	111.0	152.0	0.7303	0.7303	100.0	100.0	142.0	0.7042	0.7042	79.0	79.0	118.0	0.6695	0.6695	0.0	0.0	0.0	0.0	0.0
0.0	19.0	209	2.9469	0.006	2423.3530	1679.7403	399.0	570.0	0.7	399.0	0.7	111.0	111.0	158.0	0.7025	0.7025	110.0	110.0	152.0	0.7237	0.7237	100.0	100.0	142.0	0.7042	0.7042	78.0	78.0	118.0	0.6610	0.6610	0.0	0.0	0.0	0.0	0.0
0.0	20.0	220	2.9537	0.006	2428.9387	1683.6120	401.0	570.0	0.7035	401.0	0.7035	110.0	110.0	158.0	0.6962	0.6962	111.0	111.0	152.0	0.7303	0.7303	101.0	101.0	142.0	0.7113	0.7113	79.0	79.0	118.0	0.6695	0.6695	0.0	0.0	0.0	0.0	0.0
0.0	21.0	231	2.9368	0.006	2415.0125	1673.9591	401.0	570.0	0.7035	401.0	0.7035	112.0	112.0	158.0	0.7089	0.7089	110.0	110.0	152.0	0.7237	0.7237	100.0	100.0	142.0	0.7042	0.7042	79.0	79.0	118.0	0.6695	0.6695	0.0	0.0	0.0	0.0	0.0
0.0	22.0	242	2.9519	0.006	2427.4650	1682.5905	397.0	570.0	0.6965	397.0	0.6965	110.0	110.0	158.0	0.6962	0.6962	109.0	109.0	152.0	0.7171	0.7171	99.0	99.0	142.0	0.6972	0.6972	79.0	79.0	118.0	0.6695	0.6695	0.0	0.0	0.0	0.0	0.0
0.0	23.0	253	2.9601	0.006	2434.1860	1687.2492	403.0	570.0	0.7070	403.0	0.7070	111.0	111.0	158.0	0.7025	0.7025	112.0	112.0	152.0	0.7368	0.7368	101.0	101.0	142.0	0.7113	0.7113	79.0	79.0	118.0	0.6695	0.6695	0.0	0.0	0.0	0.0	0.0
0.0	24.0	264	2.9746	0.006	2446.1592	1695.5484	400.0	570.0	0.7018	400.0	0.7018	110.0	110.0	158.0	0.6962	0.6962	110.0	110.0	152.0	0.7237	0.7237	101.0	101.0	142.0	0.7113	0.7113	79.0	79.0	118.0	0.6695	0.6695	0.0	0.0	0.0	0.0	0.0
0.0	25.0	275	2.9806	0.006	2451.0232	1698.9198	398.0	570.0	0.6982	398.0	0.6982	109.0	109.0	158.0	0.6899	0.6899	111.0	111.0	152.0	0.7303	0.7303	99.0	99.0	142.0	0.6972	0.6972	79.0	79.0	118.0	0.6695	0.6695	0.0	0.0	0.0	0.0	0.0
0.0	26.0	286	3.0022	0.006	2468.8163	1711.2530	400.0	570.0	0.7018	400.0	0.7018	110.0	110.0	158.0	0.6962	0.6962	110.0	110.0	152.0	0.7237	0.7237	101.0	101.0	142.0	0.7113	0.7113	79.0	79.0	118.0	0.6695	0.6695	0.0	0.0	0.0	0.0	0.0
0.0	27.0	297	2.9606	0.006	2434.6001	1687.5362	405.0	570.0	0.7105	405.0	0.7105	113.0	113.0	158.0	0.7152	0.7152	111.0	111.0	152.0	0.7303	0.7303	102.0	102.0	142.0	0.7183	0.7183	79.0	79.0	118.0	0.6695	0.6695	0.0	0.0	0.0	0.0	0.0
0.0	28.0	308	2.9773	0.006	2448.3431	1697.0621	403.0	570.0	0.7070	403.0	0.7070	112.0	112.0	158.0	0.7089	0.7089	110.0	110.0	152.0	0.7237	0.7237	102.0	102.0	142.0	0.7183	0.7183	79.0	79.0	118.0	0.6695	0.6695	0.0	0.0	0.0	0.0	0.0
0.0	29.0	319	3.0146	0.006	2478.9969	1718.3097	400.0	570.0	0.7018	400.0	0.7018	110.0	110.0	158.0	0.6962	0.6962	112.0	112.0	152.0	0.7368	0.7368	100.0	100.0	142.0	0.7042	0.7042	78.0	78.0	118.0	0.6610	0.6610	0.0	0.0	0.0	0.0	0.0
0.0	30.0	330	2.9987	0.006	2465.9668	1709.2779	399.0	570.0	0.7	399.0	0.7	109.0	109.0	158.0	0.6899	0.6899	111.0	111.0	152.0	0.7303	0.7303	100.0	100.0	142.0	0.7042	0.7042	79.0	79.0	118.0	0.6695	0.6695	0.0	0.0	0.0	0.0	0.0
0.0	31.0	341	3.0034	0.006	2469.7829	1711.9230	399.0	570.0	0.7	399.0	0.7	110.0	110.0	158.0	0.6962	0.6962	110.0	110.0	152.0	0.7237	0.7237	100.0	100.0	142.0	0.7042	0.7042	79.0	79.0	118.0	0.6695	0.6695	0.0	0.0	0.0	0.0	0.0
0.0	32.0	352	3.0066	0.006	2472.4048	1713.7404	402.0	570.0	0.7053	402.0	0.7053	109.0	109.0	158.0	0.6899	0.6899	112.0	112.0	152.0	0.7368	0.7368	102.0	102.0	142.0	0.7183	0.7183	79.0	79.0	118.0	0.6695	0.6695	0.0	0.0	0.0	0.0	0.0
0.0	33.0	363	3.0106	0.006	2475.7556	1716.0630	402.0	570.0	0.7053	402.0	0.7053	112.0	112.0	158.0	0.7089	0.7089	111.0	111.0	152.0	0.7303	0.7303	99.0	99.0	142.0	0.6972	0.6972	80.0	80.0	118.0	0.6780	0.6780	0.0	0.0	0.0	0.0	0.0
0.0	34.0	374	3.0176	0.006	2481.4695	1720.0236	401.0	570.0	0.7035	401.0	0.7035	112.0	112.0	158.0	0.7089	0.7089	111.0	111.0	152.0	0.7303	0.7303	100.0	100.0	142.0	0.7042	0.7042	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