ARC-Easy_Llama-3.2-1B-jrm0jamn

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: 1.8504
• Model Preparation Time: 0.0059
• Mdl: 1521.6846
• Accumulated Loss: 1054.7514
• Correct Preds: 380.0
• Total Preds: 570.0
• Accuracy: 0.6667
• Correct Gen Preds: 380.0
• Gen Accuracy: 0.6667
• Correct Gen Preds 32: 122.0
• Correct Preds 32: 122.0
• Total Labels 32: 158.0
• Accuracy 32: 0.7722
• Gen Accuracy 32: 0.7722
• Correct Gen Preds 33: 115.0
• Correct Preds 33: 115.0
• Total Labels 33: 152.0
• Accuracy 33: 0.7566
• Gen Accuracy 33: 0.7566
• Correct Gen Preds 34: 78.0
• Correct Preds 34: 78.0
• Total Labels 34: 142.0
• Accuracy 34: 0.5493
• Gen Accuracy 34: 0.5493
• Correct Gen Preds 35: 65.0
• Correct Preds 35: 65.0
• Total Labels 35: 118.0
• Accuracy 35: 0.5508
• Gen Accuracy 35: 0.5508
• 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	0.0059	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
1.4967	1.0	2	1.4528	0.0059	1194.6871	828.0940	186.0	570.0	0.3263	186.0	0.3263	0.0	0.0	158.0	0.0	0.0	148.0	148.0	152.0	0.9737	0.9737	19.0	19.0	142.0	0.1338	0.1338	19.0	19.0	118.0	0.1610	0.1610	0.0	0.0	0.0	0.0	0.0
1.0859	2.0	4	1.0903	0.0059	896.5652	621.4516	326.0	570.0	0.5719	326.0	0.5719	69.0	69.0	158.0	0.4367	0.4367	79.0	79.0	152.0	0.5197	0.5197	76.0	76.0	142.0	0.5352	0.5352	102.0	102.0	118.0	0.8644	0.8644	0.0	0.0	0.0	0.0	0.0
0.3795	3.0	6	1.8504	0.0059	1521.6846	1054.7514	380.0	570.0	0.6667	380.0	0.6667	122.0	122.0	158.0	0.7722	0.7722	115.0	115.0	152.0	0.7566	0.7566	78.0	78.0	142.0	0.5493	0.5493	65.0	65.0	118.0	0.5508	0.5508	0.0	0.0	0.0	0.0	0.0
0.1292	4.0	8	2.0181	0.0059	1659.5713	1150.3272	377.0	570.0	0.6614	377.0	0.6614	100.0	100.0	158.0	0.6329	0.6329	126.0	126.0	152.0	0.8289	0.8289	77.0	77.0	142.0	0.5423	0.5423	74.0	74.0	118.0	0.6271	0.6271	0.0	0.0	0.0	0.0	0.0
0.0126	5.0	10	2.7711	0.0059	2278.7844	1579.5330	377.0	570.0	0.6614	377.0	0.6614	121.0	121.0	158.0	0.7658	0.7658	121.0	121.0	152.0	0.7961	0.7961	68.0	68.0	142.0	0.4789	0.4789	67.0	67.0	118.0	0.5678	0.5678	0.0	0.0	0.0	0.0	0.0
0.0001	6.0	12	3.9026	0.0059	3209.2439	2224.4784	366.0	570.0	0.6421	366.0	0.6421	100.0	100.0	158.0	0.6329	0.6329	130.0	130.0	152.0	0.8553	0.8553	65.0	65.0	142.0	0.4577	0.4577	71.0	71.0	118.0	0.6017	0.6017	0.0	0.0	0.0	0.0	0.0
0.0	7.0	14	4.6672	0.0059	3838.0176	2660.3111	366.0	570.0	0.6421	366.0	0.6421	105.0	105.0	158.0	0.6646	0.6646	123.0	123.0	152.0	0.8092	0.8092	64.0	64.0	142.0	0.4507	0.4507	74.0	74.0	118.0	0.6271	0.6271	0.0	0.0	0.0	0.0	0.0
0.0	8.0	16	5.1196	0.0059	4210.0438	2918.1800	364.0	570.0	0.6386	364.0	0.6386	105.0	105.0	158.0	0.6646	0.6646	123.0	123.0	152.0	0.8092	0.8092	60.0	60.0	142.0	0.4225	0.4225	76.0	76.0	118.0	0.6441	0.6441	0.0	0.0	0.0	0.0	0.0
0.0	9.0	18	5.3345	0.0059	4386.7564	3040.6678	361.0	570.0	0.6333	361.0	0.6333	114.0	114.0	158.0	0.7215	0.7215	127.0	127.0	152.0	0.8355	0.8355	59.0	59.0	142.0	0.4155	0.4155	61.0	61.0	118.0	0.5169	0.5169	0.0	0.0	0.0	0.0	0.0
0.0	10.0	20	5.4206	0.0059	4457.5178	3089.7159	359.0	570.0	0.6298	359.0	0.6298	117.0	117.0	158.0	0.7405	0.7405	128.0	128.0	152.0	0.8421	0.8421	59.0	59.0	142.0	0.4155	0.4155	55.0	55.0	118.0	0.4661	0.4661	0.0	0.0	0.0	0.0	0.0
0.1576	11.0	22	5.4392	0.0059	4472.8407	3100.3370	361.0	570.0	0.6333	361.0	0.6333	118.0	118.0	158.0	0.7468	0.7468	127.0	127.0	152.0	0.8355	0.8355	60.0	60.0	142.0	0.4225	0.4225	56.0	56.0	118.0	0.4746	0.4746	0.0	0.0	0.0	0.0	0.0
0.0	12.0	24	5.3259	0.0059	4379.7011	3035.7775	365.0	570.0	0.6404	365.0	0.6404	117.0	117.0	158.0	0.7405	0.7405	127.0	127.0	152.0	0.8355	0.8355	63.0	63.0	142.0	0.4437	0.4437	58.0	58.0	118.0	0.4915	0.4915	0.0	0.0	0.0	0.0	0.0
0.0	13.0	26	5.2342	0.0059	4304.2851	2983.5031	365.0	570.0	0.6404	365.0	0.6404	116.0	116.0	158.0	0.7342	0.7342	126.0	126.0	152.0	0.8289	0.8289	64.0	64.0	142.0	0.4507	0.4507	59.0	59.0	118.0	0.5	0.5	0.0	0.0	0.0	0.0	0.0
0.0	14.0	28	5.2364	0.0059	4306.0688	2984.7395	363.0	570.0	0.6368	363.0	0.6368	116.0	116.0	158.0	0.7342	0.7342	123.0	123.0	152.0	0.8092	0.8092	64.0	64.0	142.0	0.4507	0.4507	60.0	60.0	118.0	0.5085	0.5085	0.0	0.0	0.0	0.0	0.0
0.0	15.0	30	5.2256	0.0059	4297.1966	2978.5897	366.0	570.0	0.6421	366.0	0.6421	118.0	118.0	158.0	0.7468	0.7468	123.0	123.0	152.0	0.8092	0.8092	65.0	65.0	142.0	0.4577	0.4577	60.0	60.0	118.0	0.5085	0.5085	0.0	0.0	0.0	0.0	0.0
0.0	16.0	32	5.2122	0.0059	4286.1574	2970.9379	363.0	570.0	0.6368	363.0	0.6368	117.0	117.0	158.0	0.7405	0.7405	122.0	122.0	152.0	0.8026	0.8026	63.0	63.0	142.0	0.4437	0.4437	61.0	61.0	118.0	0.5169	0.5169	0.0	0.0	0.0	0.0	0.0
0.0	17.0	34	5.1878	0.0059	4266.1053	2957.0389	363.0	570.0	0.6368	363.0	0.6368	118.0	118.0	158.0	0.7468	0.7468	121.0	121.0	152.0	0.7961	0.7961	64.0	64.0	142.0	0.4507	0.4507	60.0	60.0	118.0	0.5085	0.5085	0.0	0.0	0.0	0.0	0.0
0.0	18.0	36	5.1925	0.0059	4269.9907	2959.7320	363.0	570.0	0.6368	363.0	0.6368	116.0	116.0	158.0	0.7342	0.7342	122.0	122.0	152.0	0.8026	0.8026	65.0	65.0	142.0	0.4577	0.4577	60.0	60.0	118.0	0.5085	0.5085	0.0	0.0	0.0	0.0	0.0
0.0	19.0	38	5.2069	0.0059	4281.8194	2967.9310	363.0	570.0	0.6368	363.0	0.6368	117.0	117.0	158.0	0.7405	0.7405	121.0	121.0	152.0	0.7961	0.7961	64.0	64.0	142.0	0.4507	0.4507	61.0	61.0	118.0	0.5169	0.5169	0.0	0.0	0.0	0.0	0.0
0.0	20.0	40	5.2136	0.0059	4287.3168	2971.7415	364.0	570.0	0.6386	364.0	0.6386	117.0	117.0	158.0	0.7405	0.7405	121.0	121.0	152.0	0.7961	0.7961	65.0	65.0	142.0	0.4577	0.4577	61.0	61.0	118.0	0.5169	0.5169	0.0	0.0	0.0	0.0	0.0
0.0	21.0	42	5.1806	0.0059	4260.2052	2952.9493	363.0	570.0	0.6368	363.0	0.6368	118.0	118.0	158.0	0.7468	0.7468	119.0	119.0	152.0	0.7829	0.7829	65.0	65.0	142.0	0.4577	0.4577	61.0	61.0	118.0	0.5169	0.5169	0.0	0.0	0.0	0.0	0.0
0.0	22.0	44	5.2170	0.0059	4290.1195	2973.6842	362.0	570.0	0.6351	362.0	0.6351	117.0	117.0	158.0	0.7405	0.7405	119.0	119.0	152.0	0.7829	0.7829	65.0	65.0	142.0	0.4577	0.4577	61.0	61.0	118.0	0.5169	0.5169	0.0	0.0	0.0	0.0	0.0
0.0	23.0	46	5.1888	0.0059	4266.9097	2957.5964	360.0	570.0	0.6316	360.0	0.6316	115.0	115.0	158.0	0.7278	0.7278	121.0	121.0	152.0	0.7961	0.7961	64.0	64.0	142.0	0.4507	0.4507	60.0	60.0	118.0	0.5085	0.5085	0.0	0.0	0.0	0.0	0.0
0.0	24.0	48	5.1787	0.0059	4258.6330	2951.8595	363.0	570.0	0.6368	363.0	0.6368	117.0	117.0	158.0	0.7405	0.7405	121.0	121.0	152.0	0.7961	0.7961	64.0	64.0	142.0	0.4507	0.4507	61.0	61.0	118.0	0.5169	0.5169	0.0	0.0	0.0	0.0	0.0
0.0	25.0	50	5.1830	0.0059	4262.1355	2954.2872	364.0	570.0	0.6386	364.0	0.6386	117.0	117.0	158.0	0.7405	0.7405	120.0	120.0	152.0	0.7895	0.7895	66.0	66.0	142.0	0.4648	0.4648	61.0	61.0	118.0	0.5169	0.5169	0.0	0.0	0.0	0.0	0.0
0.0	26.0	52	5.2134	0.0059	4287.1840	2971.6495	366.0	570.0	0.6421	366.0	0.6421	117.0	117.0	158.0	0.7405	0.7405	121.0	121.0	152.0	0.7961	0.7961	66.0	66.0	142.0	0.4648	0.4648	62.0	62.0	118.0	0.5254	0.5254	0.0	0.0	0.0	0.0	0.0
0.0	27.0	54	5.2234	0.0059	4295.3572	2977.3147	363.0	570.0	0.6368	363.0	0.6368	117.0	117.0	158.0	0.7405	0.7405	121.0	121.0	152.0	0.7961	0.7961	64.0	64.0	142.0	0.4507	0.4507	61.0	61.0	118.0	0.5169	0.5169	0.0	0.0	0.0	0.0	0.0
0.0	28.0	56	5.2081	0.0059	4282.7799	2968.5968	363.0	570.0	0.6368	363.0	0.6368	116.0	116.0	158.0	0.7342	0.7342	121.0	121.0	152.0	0.7961	0.7961	66.0	66.0	142.0	0.4648	0.4648	60.0	60.0	118.0	0.5085	0.5085	0.0	0.0	0.0	0.0	0.0
0.0	29.0	58	5.2147	0.0059	4288.2770	2972.4071	363.0	570.0	0.6368	363.0	0.6368	117.0	117.0	158.0	0.7405	0.7405	120.0	120.0	152.0	0.7895	0.7895	65.0	65.0	142.0	0.4577	0.4577	61.0	61.0	118.0	0.5169	0.5169	0.0	0.0	0.0	0.0	0.0
0.0	30.0	60	5.1978	0.0059	4274.3588	2962.7597	364.0	570.0	0.6386	364.0	0.6386	116.0	116.0	158.0	0.7342	0.7342	121.0	121.0	152.0	0.7961	0.7961	65.0	65.0	142.0	0.4577	0.4577	62.0	62.0	118.0	0.5254	0.5254	0.0	0.0	0.0	0.0	0.0
0.0	31.0	62	5.2467	0.0059	4314.5237	2990.5999	362.0	570.0	0.6351	362.0	0.6351	115.0	115.0	158.0	0.7278	0.7278	121.0	121.0	152.0	0.7961	0.7961	65.0	65.0	142.0	0.4577	0.4577	61.0	61.0	118.0	0.5169	0.5169	0.0	0.0	0.0	0.0	0.0
0.0	32.0	64	5.1926	0.0059	4270.0613	2959.7810	364.0	570.0	0.6386	364.0	0.6386	116.0	116.0	158.0	0.7342	0.7342	121.0	121.0	152.0	0.7961	0.7961	67.0	67.0	142.0	0.4718	0.4718	60.0	60.0	118.0	0.5085	0.5085	0.0	0.0	0.0	0.0	0.0
0.0	33.0	66	5.2157	0.0059	4289.0287	2972.9282	362.0	570.0	0.6351	362.0	0.6351	117.0	117.0	158.0	0.7405	0.7405	120.0	120.0	152.0	0.7895	0.7895	64.0	64.0	142.0	0.4507	0.4507	61.0	61.0	118.0	0.5169	0.5169	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