ARC-Easy_Llama-3.2-1B-qba6fe5a

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: 2.1998
• Model Preparation Time: 0.006
• Mdl: 1808.9895
• Accumulated Loss: 1253.8960
• Correct Preds: 346.0
• Total Preds: 570.0
• Accuracy: 0.6070
• Correct Gen Preds: 337.0
• Gen Accuracy: 0.5912
• Correct Gen Preds 32: 123.0
• Correct Preds 32: 131.0
• Total Labels 32: 158.0
• Accuracy 32: 0.8291
• Gen Accuracy 32: 0.7785
• Correct Gen Preds 33: 106.0
• Correct Preds 33: 106.0
• Total Labels 33: 152.0
• Accuracy 33: 0.6974
• Gen Accuracy 33: 0.6974
• Correct Gen Preds 34: 74.0
• Correct Preds 34: 75.0
• Total Labels 34: 142.0
• Accuracy 34: 0.5282
• Gen Accuracy 34: 0.5211
• Correct Gen Preds 35: 34.0
• Correct Preds 35: 34.0
• Total Labels 35: 118.0
• Accuracy 35: 0.2881
• Gen Accuracy 35: 0.2881
• 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
1.4642	1.0	1	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
1.4642	2.0	2	2.4299	0.006	1998.1608	1385.0195	210.0	570.0	0.3684	210.0	0.3684	0.0	0.0	158.0	0.0	0.0	144.0	144.0	152.0	0.9474	0.9474	66.0	66.0	142.0	0.4648	0.4648	0.0	0.0	118.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
1.7757	3.0	3	1.2974	0.006	1066.9296	739.5393	185.0	570.0	0.3246	185.0	0.3246	6.0	6.0	158.0	0.0380	0.0380	152.0	152.0	152.0	1.0	1.0	27.0	27.0	142.0	0.1901	0.1901	0.0	0.0	118.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
0.6892	4.0	4	2.0158	0.006	1657.6402	1148.9886	279.0	570.0	0.4895	279.0	0.4895	148.0	148.0	158.0	0.9367	0.9367	48.0	48.0	152.0	0.3158	0.3158	57.0	57.0	142.0	0.4014	0.4014	26.0	26.0	118.0	0.2203	0.2203	0.0	0.0	0.0	0.0	0.0
0.1661	5.0	5	2.1998	0.006	1808.9895	1253.8960	346.0	570.0	0.6070	337.0	0.5912	123.0	131.0	158.0	0.8291	0.7785	106.0	106.0	152.0	0.6974	0.6974	74.0	75.0	142.0	0.5282	0.5211	34.0	34.0	118.0	0.2881	0.2881	0.0	0.0	0.0	0.0	0.0
0.0079	6.0	6	2.8282	0.006	2325.6988	1612.0516	343.0	570.0	0.6018	296.0	0.5193	84.0	123.0	158.0	0.7785	0.5316	105.0	109.0	152.0	0.7171	0.6908	72.0	76.0	142.0	0.5352	0.5070	35.0	35.0	118.0	0.2966	0.2966	0.0	0.0	0.0	0.0	0.0
0.0001	7.0	7	3.1565	0.006	2595.6829	1799.1903	339.0	570.0	0.5947	264.0	0.4632	60.0	117.0	158.0	0.7405	0.3797	104.0	111.0	152.0	0.7303	0.6842	69.0	76.0	142.0	0.5352	0.4859	31.0	35.0	118.0	0.2966	0.2627	0.0	0.0	0.0	0.0	0.0
0.0	8.0	8	3.3429	0.006	2749.0232	1905.4777	331.0	570.0	0.5807	236.0	0.4140	40.0	112.0	158.0	0.7089	0.2532	101.0	110.0	152.0	0.7237	0.6645	68.0	77.0	142.0	0.5423	0.4789	27.0	32.0	118.0	0.2712	0.2288	0.0	0.0	0.0	0.0	0.0
0.0	9.0	9	3.5286	0.006	2901.6844	2011.2944	327.0	570.0	0.5737	228.0	0.4	41.0	110.0	158.0	0.6962	0.2595	99.0	111.0	152.0	0.7303	0.6513	61.0	74.0	142.0	0.5211	0.4296	27.0	32.0	118.0	0.2712	0.2288	0.0	0.0	0.0	0.0	0.0
0.0	10.0	10	3.6900	0.006	3034.4363	2103.3110	323.0	570.0	0.5667	227.0	0.3982	41.0	111.0	158.0	0.7025	0.2595	97.0	107.0	152.0	0.7039	0.6382	62.0	73.0	142.0	0.5141	0.4366	27.0	32.0	118.0	0.2712	0.2288	0.0	0.0	0.0	0.0	0.0
0.0	11.0	11	3.7945	0.006	3120.3216	2162.8421	323.0	570.0	0.5667	230.0	0.4035	43.0	112.0	158.0	0.7089	0.2722	98.0	107.0	152.0	0.7039	0.6447	63.0	73.0	142.0	0.5141	0.4437	26.0	31.0	118.0	0.2627	0.2203	0.0	0.0	0.0	0.0	0.0
0.0	12.0	12	3.8860	0.006	3195.5829	2215.0093	321.0	570.0	0.5632	227.0	0.3982	46.0	111.0	158.0	0.7025	0.2911	94.0	105.0	152.0	0.6908	0.6184	62.0	74.0	142.0	0.5211	0.4366	25.0	31.0	118.0	0.2627	0.2119	0.0	0.0	0.0	0.0	0.0
0.0	13.0	13	3.9627	0.006	3258.6448	2258.7204	321.0	570.0	0.5632	226.0	0.3965	45.0	110.0	158.0	0.6962	0.2848	94.0	106.0	152.0	0.6974	0.6184	62.0	74.0	142.0	0.5211	0.4366	25.0	31.0	118.0	0.2627	0.2119	0.0	0.0	0.0	0.0	0.0
0.0	14.0	14	4.0387	0.006	3321.1484	2302.0447	319.0	570.0	0.5596	227.0	0.3982	48.0	109.0	158.0	0.6899	0.3038	93.0	105.0	152.0	0.6908	0.6118	61.0	74.0	142.0	0.5211	0.4296	25.0	31.0	118.0	0.2627	0.2119	0.0	0.0	0.0	0.0	0.0
0.0	15.0	15	4.0577	0.006	3336.7945	2312.8897	319.0	570.0	0.5596	226.0	0.3965	48.0	109.0	158.0	0.6899	0.3038	91.0	106.0	152.0	0.6974	0.5987	60.0	74.0	142.0	0.5211	0.4225	27.0	30.0	118.0	0.2542	0.2288	0.0	0.0	0.0	0.0	0.0
0.0	16.0	16	4.0975	0.006	3369.4997	2335.5592	317.0	570.0	0.5561	224.0	0.3930	50.0	109.0	158.0	0.6899	0.3165	88.0	104.0	152.0	0.6842	0.5789	60.0	73.0	142.0	0.5141	0.4225	26.0	31.0	118.0	0.2627	0.2203	0.0	0.0	0.0	0.0	0.0
0.0	17.0	17	4.1230	0.006	3390.5230	2350.1314	316.0	570.0	0.5544	229.0	0.4018	51.0	108.0	158.0	0.6835	0.3228	91.0	104.0	152.0	0.6842	0.5987	60.0	74.0	142.0	0.5211	0.4225	27.0	30.0	118.0	0.2542	0.2288	0.0	0.0	0.0	0.0	0.0
0.0	18.0	18	4.1552	0.006	3416.9873	2368.4751	318.0	570.0	0.5579	229.0	0.4018	51.0	108.0	158.0	0.6835	0.3228	89.0	103.0	152.0	0.6776	0.5855	62.0	76.0	142.0	0.5352	0.4366	27.0	31.0	118.0	0.2627	0.2288	0.0	0.0	0.0	0.0	0.0
0.0	19.0	19	4.1977	0.006	3451.8923	2392.6694	316.0	570.0	0.5544	227.0	0.3982	50.0	108.0	158.0	0.6835	0.3165	89.0	103.0	152.0	0.6776	0.5855	62.0	75.0	142.0	0.5282	0.4366	26.0	30.0	118.0	0.2542	0.2203	0.0	0.0	0.0	0.0	0.0
0.0	20.0	20	4.1922	0.006	3447.4190	2389.5688	317.0	570.0	0.5561	228.0	0.4	51.0	109.0	158.0	0.6899	0.3228	89.0	104.0	152.0	0.6842	0.5855	60.0	74.0	142.0	0.5211	0.4225	28.0	30.0	118.0	0.2542	0.2373	0.0	0.0	0.0	0.0	0.0
0.0	21.0	21	4.2154	0.006	3466.4538	2402.7627	317.0	570.0	0.5561	231.0	0.4053	53.0	109.0	158.0	0.6899	0.3354	89.0	102.0	152.0	0.6711	0.5855	62.0	76.0	142.0	0.5352	0.4366	27.0	30.0	118.0	0.2542	0.2288	0.0	0.0	0.0	0.0	0.0
0.0	22.0	22	4.2255	0.006	3474.8213	2408.5626	319.0	570.0	0.5596	231.0	0.4053	51.0	108.0	158.0	0.6835	0.3228	90.0	103.0	152.0	0.6776	0.5921	63.0	78.0	142.0	0.5493	0.4437	27.0	30.0	118.0	0.2542	0.2288	0.0	0.0	0.0	0.0	0.0
0.0	23.0	23	4.2222	0.006	3472.0563	2406.6461	323.0	570.0	0.5667	234.0	0.4105	53.0	111.0	158.0	0.7025	0.3354	89.0	104.0	152.0	0.6842	0.5855	64.0	77.0	142.0	0.5423	0.4507	28.0	31.0	118.0	0.2627	0.2373	0.0	0.0	0.0	0.0	0.0
0.0	24.0	24	4.2449	0.006	3490.7282	2419.5884	318.0	570.0	0.5579	233.0	0.4088	53.0	108.0	158.0	0.6835	0.3354	89.0	103.0	152.0	0.6776	0.5855	63.0	76.0	142.0	0.5352	0.4437	28.0	31.0	118.0	0.2627	0.2373	0.0	0.0	0.0	0.0	0.0
0.0	25.0	25	4.2439	0.006	3489.9021	2419.0158	317.0	570.0	0.5561	234.0	0.4105	53.0	107.0	158.0	0.6772	0.3354	89.0	103.0	152.0	0.6776	0.5855	64.0	76.0	142.0	0.5352	0.4507	28.0	31.0	118.0	0.2627	0.2373	0.0	0.0	0.0	0.0	0.0
0.0	26.0	26	4.2465	0.006	3492.0437	2420.5002	316.0	570.0	0.5544	233.0	0.4088	55.0	109.0	158.0	0.6899	0.3481	89.0	101.0	152.0	0.6645	0.5855	62.0	76.0	142.0	0.5352	0.4366	27.0	30.0	118.0	0.2542	0.2288	0.0	0.0	0.0	0.0	0.0
0.0	27.0	27	4.2626	0.006	3505.3292	2429.7091	317.0	570.0	0.5561	233.0	0.4088	54.0	109.0	158.0	0.6899	0.3418	88.0	102.0	152.0	0.6711	0.5789	62.0	75.0	142.0	0.5282	0.4366	29.0	31.0	118.0	0.2627	0.2458	0.0	0.0	0.0	0.0	0.0
0.0	28.0	28	4.2468	0.006	3492.3048	2420.6812	320.0	570.0	0.5614	234.0	0.4105	53.0	108.0	158.0	0.6835	0.3354	89.0	103.0	152.0	0.6776	0.5855	63.0	78.0	142.0	0.5493	0.4437	29.0	31.0	118.0	0.2627	0.2458	0.0	0.0	0.0	0.0	0.0
0.0	29.0	29	4.2713	0.006	3512.4807	2434.6661	318.0	570.0	0.5579	233.0	0.4088	54.0	109.0	158.0	0.6899	0.3418	89.0	102.0	152.0	0.6711	0.5855	62.0	76.0	142.0	0.5352	0.4366	28.0	31.0	118.0	0.2627	0.2373	0.0	0.0	0.0	0.0	0.0
0.0	30.0	30	4.2732	0.006	3513.9739	2435.7011	317.0	570.0	0.5561	234.0	0.4105	54.0	108.0	158.0	0.6835	0.3418	89.0	102.0	152.0	0.6711	0.5855	62.0	76.0	142.0	0.5352	0.4366	29.0	31.0	118.0	0.2627	0.2458	0.0	0.0	0.0	0.0	0.0
0.0	31.0	31	4.2507	0.006	3495.4848	2422.8854	319.0	570.0	0.5596	232.0	0.4070	53.0	109.0	158.0	0.6899	0.3354	89.0	102.0	152.0	0.6711	0.5855	62.0	77.0	142.0	0.5423	0.4366	28.0	31.0	118.0	0.2627	0.2373	0.0	0.0	0.0	0.0	0.0
0.0	32.0	32	4.2647	0.006	3507.0566	2430.9064	321.0	570.0	0.5632	235.0	0.4123	54.0	109.0	158.0	0.6899	0.3418	89.0	104.0	152.0	0.6842	0.5855	64.0	78.0	142.0	0.5493	0.4507	28.0	30.0	118.0	0.2542	0.2373	0.0	0.0	0.0	0.0	0.0
0.0	33.0	33	4.2689	0.006	3510.5114	2433.3011	315.0	570.0	0.5526	230.0	0.4035	52.0	106.0	158.0	0.6709	0.3291	88.0	102.0	152.0	0.6711	0.5789	63.0	77.0	142.0	0.5423	0.4437	27.0	30.0	118.0	0.2542	0.2288	0.0	0.0	0.0	0.0	0.0
0.0	34.0	34	4.2978	0.006	3534.2027	2449.7226	318.0	570.0	0.5579	233.0	0.4088	55.0	109.0	158.0	0.6899	0.3481	89.0	103.0	152.0	0.6776	0.5855	62.0	76.0	142.0	0.5352	0.4366	27.0	30.0	118.0	0.2542	0.2288	0.0	0.0	0.0	0.0	0.0
0.0	35.0	35	4.2874	0.006	3525.6484	2443.7932	319.0	570.0	0.5596	233.0	0.4088	53.0	110.0	158.0	0.6962	0.3354	89.0	102.0	152.0	0.6711	0.5855	62.0	76.0	142.0	0.5352	0.4366	29.0	31.0	118.0	0.2627	0.2458	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