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
No log	0	0	1.5354	0.0057	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
1.3531	1.0	1	1.5354	0.0057	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
1.3531	2.0	2	2.3144	0.0057	1903.2267	1319.2162	152.0	570.0	0.2667	152.0	0.2667	0.0	0.0	158.0	0.0	0.0	152.0	152.0	152.0	1.0	1.0	0.0	0.0	142.0	0.0	0.0	0.0	0.0	118.0	0.0	0.0
1.8233	3.0	3	1.4965	0.0057	1230.6575	853.0268	159.0	570.0	0.2789	159.0	0.2789	158.0	158.0	158.0	1.0	1.0	0.0	0.0	152.0	0.0	0.0	1.0	1.0	142.0	0.0070	0.0070	0.0	0.0	118.0	0.0	0.0
0.8791	4.0	4	1.0754	0.0057	884.3810	613.0062	307.0	570.0	0.5386	307.0	0.5386	114.0	114.0	158.0	0.7215	0.7215	11.0	11.0	152.0	0.0724	0.0724	98.0	98.0	142.0	0.6901	0.6901	84.0	84.0	118.0	0.7119	0.7119
0.4849	5.0	5	1.9580	0.0057	1610.1108	1116.0437	292.0	570.0	0.5123	292.0	0.5123	149.0	149.0	158.0	0.9430	0.9430	30.0	30.0	152.0	0.1974	0.1974	66.0	66.0	142.0	0.4648	0.4648	47.0	47.0	118.0	0.3983	0.3983
0.2217	6.0	6	1.7848	0.0057	1467.6739	1017.3141	339.0	570.0	0.5947	296.0	0.5193	98.0	116.0	158.0	0.7342	0.6203	95.0	110.0	152.0	0.7237	0.625	65.0	74.0	142.0	0.5211	0.4577	38.0	39.0	118.0	0.3305	0.3220
0.0696	7.0	7	2.3773	0.0057	1954.8994	1355.0330	356.0	570.0	0.6246	351.0	0.6158	124.0	125.0	158.0	0.7911	0.7848	107.0	109.0	152.0	0.7171	0.7039	79.0	81.0	142.0	0.5704	0.5563	41.0	41.0	118.0	0.3475	0.3475
0.0037	8.0	8	4.1178	0.0057	3386.1975	2347.1332	351.0	570.0	0.6158	351.0	0.6158	137.0	137.0	158.0	0.8671	0.8671	100.0	100.0	152.0	0.6579	0.6579	73.0	73.0	142.0	0.5141	0.5141	41.0	41.0	118.0	0.3475	0.3475
0.0	9.0	9	5.4025	0.0057	4442.6583	3079.4161	336.0	570.0	0.5895	331.0	0.5807	133.0	138.0	158.0	0.8734	0.8418	92.0	92.0	152.0	0.6053	0.6053	67.0	67.0	142.0	0.4718	0.4718	39.0	39.0	118.0	0.3305	0.3305
0.0	10.0	10	6.2570	0.0057	5145.3854	3566.5094	330.0	570.0	0.5789	315.0	0.5526	126.0	141.0	158.0	0.8924	0.7975	92.0	92.0	152.0	0.6053	0.6053	64.0	64.0	142.0	0.4507	0.4507	33.0	33.0	118.0	0.2797	0.2797
0.0	11.0	11	6.8353	0.0057	5620.9324	3896.1334	329.0	570.0	0.5772	314.0	0.5509	128.0	143.0	158.0	0.9051	0.8101	91.0	91.0	152.0	0.5987	0.5987	62.0	62.0	142.0	0.4366	0.4366	33.0	33.0	118.0	0.2797	0.2797
0.0	12.0	12	7.2254	0.0057	5941.6769	4118.4566	326.0	570.0	0.5719	314.0	0.5509	131.0	143.0	158.0	0.9051	0.8291	91.0	91.0	152.0	0.5987	0.5987	59.0	59.0	142.0	0.4155	0.4155	33.0	33.0	118.0	0.2797	0.2797
0.0	13.0	13	7.4730	0.0057	6145.3165	4259.6088	322.0	570.0	0.5649	312.0	0.5474	134.0	144.0	158.0	0.9114	0.8481	91.0	91.0	152.0	0.5987	0.5987	55.0	55.0	142.0	0.3873	0.3873	32.0	32.0	118.0	0.2712	0.2712
0.0	14.0	14	7.6164	0.0057	6263.2805	4341.3752	321.0	570.0	0.5632	313.0	0.5491	137.0	145.0	158.0	0.9177	0.8671	89.0	89.0	152.0	0.5855	0.5855	55.0	55.0	142.0	0.3873	0.3873	32.0	32.0	118.0	0.2712	0.2712
0.0	15.0	15	7.7243	0.0057	6351.9819	4402.8583	318.0	570.0	0.5579	313.0	0.5491	140.0	145.0	158.0	0.9177	0.8861	88.0	88.0	152.0	0.5789	0.5789	53.0	53.0	142.0	0.3732	0.3732	32.0	32.0	118.0	0.2712	0.2712
0.0	16.0	16	7.7785	0.0057	6396.5780	4433.7700	317.0	570.0	0.5561	313.0	0.5491	141.0	145.0	158.0	0.9177	0.8924	87.0	87.0	152.0	0.5724	0.5724	53.0	53.0	142.0	0.3732	0.3732	32.0	32.0	118.0	0.2712	0.2712
0.0	17.0	17	7.8645	0.0057	6467.2957	4482.7878	315.0	570.0	0.5526	312.0	0.5474	142.0	145.0	158.0	0.9177	0.8987	88.0	88.0	152.0	0.5789	0.5789	51.0	51.0	142.0	0.3592	0.3592	31.0	31.0	118.0	0.2627	0.2627
0.0	18.0	18	7.9027	0.0057	6498.6900	4504.5487	316.0	570.0	0.5544	312.0	0.5474	141.0	145.0	158.0	0.9177	0.8924	87.0	87.0	152.0	0.5724	0.5724	53.0	53.0	142.0	0.3732	0.3732	31.0	31.0	118.0	0.2627	0.2627
0.0	19.0	19	7.9998	0.0057	6578.5635	4559.9128	313.0	570.0	0.5491	310.0	0.5439	142.0	145.0	158.0	0.9177	0.8987	86.0	86.0	152.0	0.5658	0.5658	52.0	52.0	142.0	0.3662	0.3662	30.0	30.0	118.0	0.2542	0.2542
0.0	20.0	20	8.0042	0.0057	6582.1226	4562.3797	314.0	570.0	0.5509	311.0	0.5456	142.0	145.0	158.0	0.9177	0.8987	85.0	85.0	152.0	0.5592	0.5592	54.0	54.0	142.0	0.3803	0.3803	30.0	30.0	118.0	0.2542	0.2542
0.0	21.0	21	8.0503	0.0057	6620.0897	4588.6965	311.0	570.0	0.5456	308.0	0.5404	142.0	145.0	158.0	0.9177	0.8987	84.0	84.0	152.0	0.5526	0.5526	52.0	52.0	142.0	0.3662	0.3662	30.0	30.0	118.0	0.2542	0.2542
0.0	22.0	22	8.0228	0.0057	6597.4578	4573.0092	315.0	570.0	0.5526	313.0	0.5491	143.0	145.0	158.0	0.9177	0.9051	85.0	85.0	152.0	0.5592	0.5592	53.0	53.0	142.0	0.3732	0.3732	32.0	32.0	118.0	0.2712	0.2712
0.0	23.0	23	8.1360	0.0057	6690.5589	4637.5420	312.0	570.0	0.5474	309.0	0.5421	142.0	145.0	158.0	0.9177	0.8987	85.0	85.0	152.0	0.5592	0.5592	52.0	52.0	142.0	0.3662	0.3662	30.0	30.0	118.0	0.2542	0.2542
0.0	24.0	24	8.1110	0.0057	6669.9872	4623.2829	315.0	570.0	0.5526	314.0	0.5509	145.0	146.0	158.0	0.9241	0.9177	85.0	85.0	152.0	0.5592	0.5592	54.0	54.0	142.0	0.3803	0.3803	30.0	30.0	118.0	0.2542	0.2542
0.0	25.0	25	8.0899	0.0057	6652.6387	4611.2577	313.0	570.0	0.5491	312.0	0.5474	145.0	146.0	158.0	0.9241	0.9177	83.0	83.0	152.0	0.5461	0.5461	54.0	54.0	142.0	0.3803	0.3803	30.0	30.0	118.0	0.2542	0.2542
0.0	26.0	26	8.0958	0.0057	6657.4563	4614.5971	315.0	570.0	0.5526	313.0	0.5491	143.0	145.0	158.0	0.9177	0.9051	83.0	83.0	152.0	0.5461	0.5461	55.0	55.0	142.0	0.3873	0.3873	32.0	32.0	118.0	0.2712	0.2712
0.0	27.0	27	8.1194	0.0057	6676.9034	4628.0768	314.0	570.0	0.5509	312.0	0.5474	143.0	145.0	158.0	0.9177	0.9051	85.0	85.0	152.0	0.5592	0.5592	54.0	54.0	142.0	0.3803	0.3803	30.0	30.0	118.0	0.2542	0.2542
0.0	28.0	28	8.1511	0.0057	6702.9764	4646.1492	314.0	570.0	0.5509	313.0	0.5491	144.0	145.0	158.0	0.9177	0.9114	84.0	84.0	152.0	0.5526	0.5526	55.0	55.0	142.0	0.3873	0.3873	30.0	30.0	118.0	0.2542	0.2542
0.0	29.0	29	8.1586	0.0057	6709.1201	4650.4077	313.0	570.0	0.5491	313.0	0.5491	145.0	145.0	158.0	0.9177	0.9177	83.0	83.0	152.0	0.5461	0.5461	54.0	54.0	142.0	0.3803	0.3803	31.0	31.0	118.0	0.2627	0.2627
0.0	30.0	30	8.1033	0.0057	6663.6069	4618.8603	312.0	570.0	0.5474	310.0	0.5439	143.0	145.0	158.0	0.9177	0.9051	84.0	84.0	152.0	0.5526	0.5526	53.0	53.0	142.0	0.3732	0.3732	30.0	30.0	118.0	0.2542	0.2542
0.0	31.0	31	8.1388	0.0057	6692.8394	4639.1227	317.0	570.0	0.5561	316.0	0.5544	144.0	145.0	158.0	0.9177	0.9114	85.0	85.0	152.0	0.5592	0.5592	56.0	56.0	142.0	0.3944	0.3944	31.0	31.0	118.0	0.2627	0.2627
0.0	32.0	32	8.1790	0.0057	6725.8530	4662.0061	312.0	570.0	0.5474	311.0	0.5456	144.0	145.0	158.0	0.9177	0.9114	83.0	83.0	152.0	0.5461	0.5461	53.0	53.0	142.0	0.3732	0.3732	31.0	31.0	118.0	0.2627	0.2627
0.0	33.0	33	8.1788	0.0057	6725.7129	4661.9089	314.0	570.0	0.5509	314.0	0.5509	145.0	145.0	158.0	0.9177	0.9177	83.0	83.0	152.0	0.5461	0.5461	56.0	56.0	142.0	0.3944	0.3944	30.0	30.0	118.0	0.2542	0.2542
0.0	34.0	34	8.1461	0.0057	6698.7991	4643.2537	315.0	570.0	0.5526	315.0	0.5526	145.0	145.0	158.0	0.9177	0.9177	84.0	84.0	152.0	0.5526	0.5526	56.0	56.0	142.0	0.3944	0.3944	30.0	30.0	118.0	0.2542	0.2542
0.0	35.0	35	8.1543	0.0057	6705.5694	4647.9465	315.0	570.0	0.5526	314.0	0.5509	144.0	145.0	158.0	0.9177	0.9114	85.0	85.0	152.0	0.5592	0.5592	54.0	54.0	142.0	0.3803	0.3803	31.0	31.0	118.0	0.2627	0.2627
0.0	36.0	36	8.1585	0.0057	6709.0706	4650.3734	315.0	570.0	0.5526	315.0	0.5526	145.0	145.0	158.0	0.9177	0.9177	83.0	83.0	152.0	0.5461	0.5461	56.0	56.0	142.0	0.3944	0.3944	31.0	31.0	118.0	0.2627	0.2627
0.0	37.0	37	8.1496	0.0057	6701.7266	4645.2829	314.0	570.0	0.5509	313.0	0.5491	144.0	145.0	158.0	0.9177	0.9114	85.0	85.0	152.0	0.5592	0.5592	55.0	55.0	142.0	0.3873	0.3873	29.0	29.0	118.0	0.2458	0.2458

Framework versions

Transformers 4.51.3
Pytorch 2.6.0+cu124
Datasets 3.5.0
Tokenizers 0.21.1

donoway/ARC-Easy_Llama-3.2-1B-eecazfmn

ARC-Easy_Llama-3.2-1B-eecazfmn

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

donoway/
ARC-Easy_Llama-3.2-1B-eecazfmn