ARC-Easy_Llama-3.2-1B-hd3zn430

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: 1.1489
• Model Preparation Time: 0.0061
• Mdl: 944.7868
• Accumulated Loss: 654.8763
• Correct Preds: 430.0
• Total Preds: 570.0
• Accuracy: 0.7544
• Correct Gen Preds: 430.0
• Gen Accuracy: 0.7544
• Correct Gen Preds 32: 115.0
• Correct Preds 32: 115.0
• Total Labels 32: 158.0
• Accuracy 32: 0.7278
• Gen Accuracy 32: 0.7278
• Correct Gen Preds 33: 116.0
• Correct Preds 33: 116.0
• Total Labels 33: 152.0
• Accuracy 33: 0.7632
• Gen Accuracy 33: 0.7632
• Correct Gen Preds 34: 116.0
• Correct Preds 34: 116.0
• Total Labels 34: 142.0
• Accuracy 34: 0.8169
• Gen Accuracy 34: 0.8169
• Correct Gen Preds 35: 83.0
• Correct Preds 35: 83.0
• Total Labels 35: 118.0
• Accuracy 35: 0.7034
• Gen Accuracy 35: 0.7034
• 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	0.0061	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
0.8351	1.0	28	0.7875	0.0061	647.6048	448.8855	405.0	570.0	0.7105	405.0	0.7105	94.0	94.0	158.0	0.5949	0.5949	126.0	126.0	152.0	0.8289	0.8289	111.0	111.0	142.0	0.7817	0.7817	74.0	74.0	118.0	0.6271	0.6271	0.0	0.0	0.0	0.0	0.0
0.392	2.0	56	0.7881	0.0061	648.0490	449.1933	425.0	570.0	0.7456	424.0	0.7439	105.0	106.0	158.0	0.6709	0.6646	112.0	112.0	152.0	0.7368	0.7368	115.0	115.0	142.0	0.8099	0.8099	92.0	92.0	118.0	0.7797	0.7797	0.0	0.0	0.0	0.0	0.0
0.2634	3.0	84	1.1489	0.0061	944.7868	654.8763	430.0	570.0	0.7544	430.0	0.7544	115.0	115.0	158.0	0.7278	0.7278	116.0	116.0	152.0	0.7632	0.7632	116.0	116.0	142.0	0.8169	0.8169	83.0	83.0	118.0	0.7034	0.7034	0.0	0.0	0.0	0.0	0.0
0.0013	4.0	112	1.4643	0.0061	1204.1842	834.6769	423.0	570.0	0.7421	417.0	0.7316	122.0	126.0	158.0	0.7975	0.7722	106.0	108.0	152.0	0.7105	0.6974	110.0	110.0	142.0	0.7746	0.7746	79.0	79.0	118.0	0.6695	0.6695	0.0	0.0	0.0	0.0	0.0
0.004	5.0	140	1.7882	0.0061	1470.4741	1019.2550	422.0	570.0	0.7404	422.0	0.7404	105.0	105.0	158.0	0.6646	0.6646	119.0	119.0	152.0	0.7829	0.7829	109.0	109.0	142.0	0.7676	0.7676	89.0	89.0	118.0	0.7542	0.7542	0.0	0.0	0.0	0.0	0.0
0.0001	6.0	168	2.2048	0.0061	1813.0828	1256.7333	410.0	570.0	0.7193	406.0	0.7123	115.0	119.0	158.0	0.7532	0.7278	105.0	105.0	152.0	0.6908	0.6908	103.0	103.0	142.0	0.7254	0.7254	83.0	83.0	118.0	0.7034	0.7034	0.0	0.0	0.0	0.0	0.0
0.0005	7.0	196	1.8019	0.0061	1481.7281	1027.0557	420.0	570.0	0.7368	420.0	0.7368	131.0	131.0	158.0	0.8291	0.8291	105.0	105.0	152.0	0.6908	0.6908	110.0	110.0	142.0	0.7746	0.7746	74.0	74.0	118.0	0.6271	0.6271	0.0	0.0	0.0	0.0	0.0
0.0001	8.0	224	2.3824	0.0061	1959.1709	1357.9938	425.0	570.0	0.7456	425.0	0.7456	111.0	111.0	158.0	0.7025	0.7025	118.0	118.0	152.0	0.7763	0.7763	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	9.0	252	2.5785	0.0061	2120.3940	1469.7451	416.0	570.0	0.7298	416.0	0.7298	127.0	127.0	158.0	0.8038	0.8038	113.0	113.0	152.0	0.7434	0.7434	99.0	99.0	142.0	0.6972	0.6972	77.0	77.0	118.0	0.6525	0.6525	0.0	0.0	0.0	0.0	0.0
0.0002	10.0	280	2.1760	0.0061	1789.4440	1240.3481	421.0	570.0	0.7386	421.0	0.7386	119.0	119.0	158.0	0.7532	0.7532	109.0	109.0	152.0	0.7171	0.7171	109.0	109.0	142.0	0.7676	0.7676	84.0	84.0	118.0	0.7119	0.7119	0.0	0.0	0.0	0.0	0.0
0.0	11.0	308	2.4159	0.0061	1986.6644	1377.0508	425.0	570.0	0.7456	425.0	0.7456	123.0	123.0	158.0	0.7785	0.7785	110.0	110.0	152.0	0.7237	0.7237	109.0	109.0	142.0	0.7676	0.7676	83.0	83.0	118.0	0.7034	0.7034	0.0	0.0	0.0	0.0	0.0
0.0	12.0	336	2.5305	0.0061	2080.8912	1442.3639	421.0	570.0	0.7386	421.0	0.7386	125.0	125.0	158.0	0.7911	0.7911	109.0	109.0	152.0	0.7171	0.7171	104.0	104.0	142.0	0.7324	0.7324	83.0	83.0	118.0	0.7034	0.7034	0.0	0.0	0.0	0.0	0.0
0.0	13.0	364	2.5658	0.0061	2109.9708	1462.5203	423.0	570.0	0.7421	423.0	0.7421	125.0	125.0	158.0	0.7911	0.7911	109.0	109.0	152.0	0.7171	0.7171	106.0	106.0	142.0	0.7465	0.7465	83.0	83.0	118.0	0.7034	0.7034	0.0	0.0	0.0	0.0	0.0
0.0	14.0	392	2.5842	0.0061	2125.0564	1472.9769	422.0	570.0	0.7404	422.0	0.7404	125.0	125.0	158.0	0.7911	0.7911	110.0	110.0	152.0	0.7237	0.7237	105.0	105.0	142.0	0.7394	0.7394	82.0	82.0	118.0	0.6949	0.6949	0.0	0.0	0.0	0.0	0.0
0.0	15.0	420	2.6001	0.0061	2138.1608	1482.0601	422.0	570.0	0.7404	422.0	0.7404	125.0	125.0	158.0	0.7911	0.7911	111.0	111.0	152.0	0.7303	0.7303	104.0	104.0	142.0	0.7324	0.7324	82.0	82.0	118.0	0.6949	0.6949	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