ARC-Easy_Llama-3.2-1B-2vnc0c6d

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: 0.7003
• Model Preparation Time: 0.0056
• Mdl: 575.9203
• Accumulated Loss: 399.1976
• Correct Preds: 451.0
• Total Preds: 570.0
• Accuracy: 0.7912
• Correct Gen Preds: 451.0
• Gen Accuracy: 0.7912
• Correct Gen Preds 32: 134.0
• Correct Preds 32: 134.0
• Total Labels 32: 158.0
• Accuracy 32: 0.8481
• Gen Accuracy 32: 0.8481
• Correct Gen Preds 33: 124.0
• Correct Preds 33: 124.0
• Total Labels 33: 152.0
• Accuracy 33: 0.8158
• Gen Accuracy 33: 0.8158
• Correct Gen Preds 34: 112.0
• Correct Preds 34: 112.0
• Total Labels 34: 142.0
• Accuracy 34: 0.7887
• Gen Accuracy 34: 0.7887
• Correct Gen Preds 35: 81.0
• Correct Preds 35: 81.0
• Total Labels 35: 118.0
• Accuracy 35: 0.6864
• Gen Accuracy 35: 0.6864
• 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: constant
• lr_scheduler_warmup_ratio: 0.001
• 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	0.0056	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
0.7518	1.0	32	0.7326	0.0056	602.4087	417.5579	411.0	570.0	0.7211	410.0	0.7193	86.0	87.0	158.0	0.5506	0.5443	124.0	124.0	152.0	0.8158	0.8158	106.0	106.0	142.0	0.7465	0.7465	94.0	94.0	118.0	0.7966	0.7966	0.0	0.0	0.0	0.0	0.0
0.516	2.0	64	0.7003	0.0056	575.9203	399.1976	451.0	570.0	0.7912	451.0	0.7912	134.0	134.0	158.0	0.8481	0.8481	124.0	124.0	152.0	0.8158	0.8158	112.0	112.0	142.0	0.7887	0.7887	81.0	81.0	118.0	0.6864	0.6864	0.0	0.0	0.0	0.0	0.0
0.0089	3.0	96	1.0818	0.0056	889.5841	616.6127	436.0	570.0	0.7649	435.0	0.7632	119.0	120.0	158.0	0.7595	0.7532	110.0	110.0	152.0	0.7237	0.7237	112.0	112.0	142.0	0.7887	0.7887	94.0	94.0	118.0	0.7966	0.7966	0.0	0.0	0.0	0.0	0.0
0.0002	4.0	128	1.7721	0.0056	1457.2823	1010.1111	435.0	570.0	0.7632	433.0	0.7596	126.0	128.0	158.0	0.8101	0.7975	118.0	118.0	152.0	0.7763	0.7763	115.0	115.0	142.0	0.8099	0.8099	74.0	74.0	118.0	0.6271	0.6271	0.0	0.0	0.0	0.0	0.0
0.0006	5.0	160	1.6350	0.0056	1344.5068	931.9411	438.0	570.0	0.7684	438.0	0.7684	114.0	114.0	158.0	0.7215	0.7215	124.0	124.0	152.0	0.8158	0.8158	115.0	115.0	142.0	0.8099	0.8099	85.0	85.0	118.0	0.7203	0.7203	0.0	0.0	0.0	0.0	0.0
0.0137	6.0	192	1.7262	0.0056	1419.4795	983.9082	451.0	570.0	0.7912	449.0	0.7877	132.0	133.0	158.0	0.8418	0.8354	119.0	120.0	152.0	0.7895	0.7829	114.0	114.0	142.0	0.8028	0.8028	84.0	84.0	118.0	0.7119	0.7119	0.0	0.0	0.0	0.0	0.0
0.0025	7.0	224	2.0853	0.0056	1714.8122	1188.6173	443.0	570.0	0.7772	442.0	0.7754	119.0	120.0	158.0	0.7595	0.7532	121.0	121.0	152.0	0.7961	0.7961	117.0	117.0	142.0	0.8239	0.8239	85.0	85.0	118.0	0.7203	0.7203	0.0	0.0	0.0	0.0	0.0
0.0	8.0	256	2.1702	0.0056	1784.6740	1237.0417	441.0	570.0	0.7737	440.0	0.7719	122.0	123.0	158.0	0.7785	0.7722	120.0	120.0	152.0	0.7895	0.7895	116.0	116.0	142.0	0.8169	0.8169	82.0	82.0	118.0	0.6949	0.6949	0.0	0.0	0.0	0.0	0.0
0.0	9.0	288	2.1716	0.0056	1785.7880	1237.8139	442.0	570.0	0.7754	441.0	0.7737	125.0	126.0	158.0	0.7975	0.7911	120.0	120.0	152.0	0.7895	0.7895	113.0	113.0	142.0	0.7958	0.7958	83.0	83.0	118.0	0.7034	0.7034	0.0	0.0	0.0	0.0	0.0
0.0	10.0	320	2.1731	0.0056	1786.9933	1238.6494	439.0	570.0	0.7702	438.0	0.7684	123.0	124.0	158.0	0.7848	0.7785	120.0	120.0	152.0	0.7895	0.7895	113.0	113.0	142.0	0.7958	0.7958	82.0	82.0	118.0	0.6949	0.6949	0.0	0.0	0.0	0.0	0.0
0.0	11.0	352	2.1918	0.0056	1802.4099	1249.3354	442.0	570.0	0.7754	441.0	0.7737	124.0	125.0	158.0	0.7911	0.7848	121.0	121.0	152.0	0.7961	0.7961	114.0	114.0	142.0	0.8028	0.8028	82.0	82.0	118.0	0.6949	0.6949	0.0	0.0	0.0	0.0	0.0
0.0	12.0	384	2.2028	0.0056	1811.4368	1255.5923	443.0	570.0	0.7772	442.0	0.7754	125.0	126.0	158.0	0.7975	0.7911	120.0	120.0	152.0	0.7895	0.7895	114.0	114.0	142.0	0.8028	0.8028	83.0	83.0	118.0	0.7034	0.7034	0.0	0.0	0.0	0.0	0.0
0.0	13.0	416	2.2258	0.0056	1830.3815	1268.7238	442.0	570.0	0.7754	441.0	0.7737	124.0	125.0	158.0	0.7911	0.7848	120.0	120.0	152.0	0.7895	0.7895	114.0	114.0	142.0	0.8028	0.8028	83.0	83.0	118.0	0.7034	0.7034	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