ARC-Easy_Llama-3.2-1B-m8bnuslx

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: 0.8185
• Model Preparation Time: 0.0057
• Mdl: 673.1057
• Accumulated Loss: 466.5613
• Correct Preds: 439.0
• Total Preds: 570.0
• Accuracy: 0.7702
• Correct Gen Preds: 439.0
• Gen Accuracy: 0.7702
• Correct Gen Preds 32: 116.0
• Correct Preds 32: 116.0
• Total Labels 32: 158.0
• Accuracy 32: 0.7342
• Gen Accuracy 32: 0.7342
• Correct Gen Preds 33: 114.0
• Correct Preds 33: 114.0
• Total Labels 33: 152.0
• Accuracy 33: 0.75
• Gen Accuracy 33: 0.75
• Correct Gen Preds 34: 119.0
• Correct Preds 34: 119.0
• Total Labels 34: 142.0
• Accuracy 34: 0.8380
• Gen Accuracy 34: 0.8380
• Correct Gen Preds 35: 90.0
• Correct Preds 35: 90.0
• Total Labels 35: 118.0
• Accuracy 35: 0.7627
• Gen Accuracy 35: 0.7627
• 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	0.0057	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	0.0	0.0	0.0	0.0	0.0
0.7763	1.0	34	0.7288	0.0057	599.3417	415.4320	419.0	570.0	0.7351	419.0	0.7351	101.0	101.0	158.0	0.6392	0.6392	123.0	123.0	152.0	0.8092	0.8092	116.0	116.0	142.0	0.8169	0.8169	79.0	79.0	118.0	0.6695	0.6695	0.0	0.0	0.0	0.0	0.0
0.1374	2.0	68	0.7563	0.0057	621.9182	431.0809	426.0	570.0	0.7474	426.0	0.7474	120.0	120.0	158.0	0.7595	0.7595	113.0	113.0	152.0	0.7434	0.7434	108.0	108.0	142.0	0.7606	0.7606	85.0	85.0	118.0	0.7203	0.7203	0.0	0.0	0.0	0.0	0.0
0.2715	3.0	102	0.8185	0.0057	673.1057	466.5613	439.0	570.0	0.7702	439.0	0.7702	116.0	116.0	158.0	0.7342	0.7342	114.0	114.0	152.0	0.75	0.75	119.0	119.0	142.0	0.8380	0.8380	90.0	90.0	118.0	0.7627	0.7627	0.0	0.0	0.0	0.0	0.0
0.0253	4.0	136	1.0157	0.0057	835.2714	578.9660	426.0	570.0	0.7474	162.0	0.2842	50.0	120.0	158.0	0.7595	0.3165	87.0	115.0	152.0	0.7566	0.5724	11.0	110.0	142.0	0.7746	0.0775	14.0	81.0	118.0	0.6864	0.1186	0.0	0.0	0.0	0.0	0.0
0.2569	5.0	170	1.2857	0.0057	1057.2586	732.8358	422.0	570.0	0.7404	419.0	0.7351	104.0	107.0	158.0	0.6772	0.6582	114.0	114.0	152.0	0.75	0.75	119.0	119.0	142.0	0.8380	0.8380	82.0	82.0	118.0	0.6949	0.6949	0.0	0.0	0.0	0.0	0.0
0.0001	6.0	204	2.3304	0.0057	1916.3762	1328.3308	425.0	570.0	0.7456	425.0	0.7456	130.0	130.0	158.0	0.8228	0.8228	117.0	117.0	152.0	0.7697	0.7697	106.0	106.0	142.0	0.7465	0.7465	72.0	72.0	118.0	0.6102	0.6102	0.0	0.0	0.0	0.0	0.0
0.0	7.0	238	2.3094	0.0057	1899.0828	1316.3439	420.0	570.0	0.7368	420.0	0.7368	113.0	113.0	158.0	0.7152	0.7152	118.0	118.0	152.0	0.7763	0.7763	112.0	112.0	142.0	0.7887	0.7887	77.0	77.0	118.0	0.6525	0.6525	0.0	0.0	0.0	0.0	0.0
0.0	8.0	272	2.4585	0.0057	2021.7022	1401.3372	423.0	570.0	0.7421	422.0	0.7404	126.0	127.0	158.0	0.8038	0.7975	115.0	115.0	152.0	0.7566	0.7566	107.0	107.0	142.0	0.7535	0.7535	74.0	74.0	118.0	0.6271	0.6271	0.0	0.0	0.0	0.0	0.0
0.0	9.0	306	2.4936	0.0057	2050.5752	1421.3504	421.0	570.0	0.7386	420.0	0.7368	125.0	126.0	158.0	0.7975	0.7911	115.0	115.0	152.0	0.7566	0.7566	106.0	106.0	142.0	0.7465	0.7465	74.0	74.0	118.0	0.6271	0.6271	0.0	0.0	0.0	0.0	0.0
0.0	10.0	340	2.4968	0.0057	2053.2377	1423.1959	421.0	570.0	0.7386	420.0	0.7368	125.0	126.0	158.0	0.7975	0.7911	115.0	115.0	152.0	0.7566	0.7566	106.0	106.0	142.0	0.7465	0.7465	74.0	74.0	118.0	0.6271	0.6271	0.0	0.0	0.0	0.0	0.0
0.0	11.0	374	2.4945	0.0057	2051.3022	1421.8544	421.0	570.0	0.7386	420.0	0.7368	125.0	126.0	158.0	0.7975	0.7911	115.0	115.0	152.0	0.7566	0.7566	106.0	106.0	142.0	0.7465	0.7465	74.0	74.0	118.0	0.6271	0.6271	0.0	0.0	0.0	0.0	0.0
0.0	12.0	408	2.4958	0.0057	2052.4163	1422.6265	421.0	570.0	0.7386	420.0	0.7368	125.0	126.0	158.0	0.7975	0.7911	114.0	114.0	152.0	0.75	0.75	107.0	107.0	142.0	0.7535	0.7535	74.0	74.0	118.0	0.6271	0.6271	0.0	0.0	0.0	0.0	0.0
0.0	13.0	442	2.5105	0.0057	2064.4925	1430.9972	423.0	570.0	0.7421	422.0	0.7404	126.0	127.0	158.0	0.8038	0.7975	116.0	116.0	152.0	0.7632	0.7632	106.0	106.0	142.0	0.7465	0.7465	74.0	74.0	118.0	0.6271	0.6271	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