ARC-Easy_Llama-3.2-1B-l3w1y2gt

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: 0.7675
• Model Preparation Time: 0.0056
• Mdl: 631.1500
• Accumulated Loss: 437.4799
• Correct Preds: 430.0
• Total Preds: 570.0
• Accuracy: 0.7544
• Correct Gen Preds: 430.0
• Gen Accuracy: 0.7544
• Correct Gen Preds 32: 120.0
• Correct Preds 32: 120.0
• Total Labels 32: 158.0
• Accuracy 32: 0.7595
• Gen Accuracy 32: 0.7595
• Correct Gen Preds 33: 118.0
• Correct Preds 33: 118.0
• Total Labels 33: 152.0
• Accuracy 33: 0.7763
• Gen Accuracy 33: 0.7763
• Correct Gen Preds 34: 110.0
• Correct Preds 34: 110.0
• Total Labels 34: 142.0
• Accuracy 34: 0.7746
• Gen Accuracy 34: 0.7746
• Correct Gen Preds 35: 82.0
• Correct Preds 35: 82.0
• Total Labels 35: 118.0
• Accuracy 35: 0.6949
• Gen Accuracy 35: 0.6949
• 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
0.8193	1.0	17	0.8399	0.0056	690.6870	478.7477	401.0	570.0	0.7035	401.0	0.7035	106.0	106.0	158.0	0.6709	0.6709	106.0	106.0	152.0	0.6974	0.6974	103.0	103.0	142.0	0.7254	0.7254	86.0	86.0	118.0	0.7288	0.7288	0.0	0.0	0.0	0.0	0.0
0.3755	2.0	34	0.7675	0.0056	631.1500	437.4799	430.0	570.0	0.7544	430.0	0.7544	120.0	120.0	158.0	0.7595	0.7595	118.0	118.0	152.0	0.7763	0.7763	110.0	110.0	142.0	0.7746	0.7746	82.0	82.0	118.0	0.6949	0.6949	0.0	0.0	0.0	0.0	0.0
0.0673	3.0	51	0.9258	0.0056	761.2801	527.6791	425.0	570.0	0.7456	424.0	0.7439	113.0	114.0	158.0	0.7215	0.7152	123.0	123.0	152.0	0.8092	0.8092	113.0	113.0	142.0	0.7958	0.7958	75.0	75.0	118.0	0.6356	0.6356	0.0	0.0	0.0	0.0	0.0
0.0163	4.0	68	1.1686	0.0056	961.0022	666.1160	410.0	570.0	0.7193	410.0	0.7193	125.0	125.0	158.0	0.7911	0.7911	113.0	113.0	152.0	0.7434	0.7434	105.0	105.0	142.0	0.7394	0.7394	67.0	67.0	118.0	0.5678	0.5678	0.0	0.0	0.0	0.0	0.0
0.0	5.0	85	2.5405	0.0056	2089.1473	1448.0865	406.0	570.0	0.7123	406.0	0.7123	99.0	99.0	158.0	0.6266	0.6266	129.0	129.0	152.0	0.8487	0.8487	102.0	102.0	142.0	0.7183	0.7183	76.0	76.0	118.0	0.6441	0.6441	0.0	0.0	0.0	0.0	0.0
0.0219	6.0	102	2.1967	0.0056	1806.4444	1252.1318	418.0	570.0	0.7333	418.0	0.7333	127.0	127.0	158.0	0.8038	0.8038	105.0	105.0	152.0	0.6908	0.6908	110.0	110.0	142.0	0.7746	0.7746	76.0	76.0	118.0	0.6441	0.6441	0.0	0.0	0.0	0.0	0.0
0.0	7.0	119	2.6483	0.0056	2177.7596	1509.5079	414.0	570.0	0.7263	410.0	0.7193	101.0	103.0	158.0	0.6519	0.6392	125.0	125.0	152.0	0.8224	0.8224	106.0	107.0	142.0	0.7535	0.7465	78.0	79.0	118.0	0.6695	0.6610	0.0	0.0	0.0	0.0	0.0
0.185	8.0	136	2.2471	0.0056	1847.8903	1280.8600	415.0	570.0	0.7281	415.0	0.7281	129.0	129.0	158.0	0.8165	0.8165	123.0	123.0	152.0	0.8092	0.8092	101.0	101.0	142.0	0.7113	0.7113	62.0	62.0	118.0	0.5254	0.5254	0.0	0.0	0.0	0.0	0.0
0.0	9.0	153	2.7019	0.0056	2221.8581	1540.0747	418.0	570.0	0.7333	417.0	0.7316	112.0	113.0	158.0	0.7152	0.7089	131.0	131.0	152.0	0.8618	0.8618	103.0	103.0	142.0	0.7254	0.7254	71.0	71.0	118.0	0.6017	0.6017	0.0	0.0	0.0	0.0	0.0
0.0	10.0	170	2.7311	0.0056	2245.8859	1556.7295	418.0	570.0	0.7333	418.0	0.7333	116.0	116.0	158.0	0.7342	0.7342	122.0	122.0	152.0	0.8026	0.8026	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	187	2.7509	0.0056	2262.1718	1568.0180	419.0	570.0	0.7351	419.0	0.7351	116.0	116.0	158.0	0.7342	0.7342	121.0	121.0	152.0	0.7961	0.7961	108.0	108.0	142.0	0.7606	0.7606	74.0	74.0	118.0	0.6271	0.6271	0.0	0.0	0.0	0.0	0.0
0.0	12.0	204	2.7518	0.0056	2262.9076	1568.5280	419.0	570.0	0.7351	419.0	0.7351	116.0	116.0	158.0	0.7342	0.7342	121.0	121.0	152.0	0.7961	0.7961	108.0	108.0	142.0	0.7606	0.7606	74.0	74.0	118.0	0.6271	0.6271	0.0	0.0	0.0	0.0	0.0
0.0	13.0	221	2.7606	0.0056	2270.1255	1573.5311	419.0	570.0	0.7351	419.0	0.7351	116.0	116.0	158.0	0.7342	0.7342	121.0	121.0	152.0	0.7961	0.7961	108.0	108.0	142.0	0.7606	0.7606	74.0	74.0	118.0	0.6271	0.6271	0.0	0.0	0.0	0.0	0.0
0.0	14.0	238	2.7827	0.0056	2288.3338	1586.1521	420.0	570.0	0.7368	420.0	0.7368	116.0	116.0	158.0	0.7342	0.7342	122.0	122.0	152.0	0.8026	0.8026	108.0	108.0	142.0	0.7606	0.7606	74.0	74.0	118.0	0.6271	0.6271	0.0	0.0	0.0	0.0	0.0
0.0	15.0	255	2.7809	0.0056	2286.8709	1585.1381	419.0	570.0	0.7351	418.0	0.7333	115.0	116.0	158.0	0.7342	0.7278	121.0	121.0	152.0	0.7961	0.7961	108.0	108.0	142.0	0.7606	0.7606	74.0	74.0	118.0	0.6271	0.6271	0.0	0.0	0.0	0.0	0.0
0.0	16.0	272	2.7799	0.0056	2286.0110	1584.5421	419.0	570.0	0.7351	419.0	0.7351	116.0	116.0	158.0	0.7342	0.7342	121.0	121.0	152.0	0.7961	0.7961	108.0	108.0	142.0	0.7606	0.7606	74.0	74.0	118.0	0.6271	0.6271	0.0	0.0	0.0	0.0	0.0
0.0	17.0	289	2.7880	0.0056	2292.6855	1589.1685	420.0	570.0	0.7368	420.0	0.7368	116.0	116.0	158.0	0.7342	0.7342	122.0	122.0	152.0	0.8026	0.8026	108.0	108.0	142.0	0.7606	0.7606	74.0	74.0	118.0	0.6271	0.6271	0.0	0.0	0.0	0.0	0.0
0.0	18.0	306	2.8077	0.0056	2308.8692	1600.3861	420.0	570.0	0.7368	420.0	0.7368	116.0	116.0	158.0	0.7342	0.7342	122.0	122.0	152.0	0.8026	0.8026	108.0	108.0	142.0	0.7606	0.7606	74.0	74.0	118.0	0.6271	0.6271	0.0	0.0	0.0	0.0	0.0
0.0	19.0	323	2.8043	0.0056	2306.1110	1598.4744	419.0	570.0	0.7351	419.0	0.7351	116.0	116.0	158.0	0.7342	0.7342	121.0	121.0	152.0	0.7961	0.7961	108.0	108.0	142.0	0.7606	0.7606	74.0	74.0	118.0	0.6271	0.6271	0.0	0.0	0.0	0.0	0.0
0.0	20.0	340	2.8029	0.0056	2304.8923	1597.6296	419.0	570.0	0.7351	418.0	0.7333	115.0	116.0	158.0	0.7342	0.7278	121.0	121.0	152.0	0.7961	0.7961	108.0	108.0	142.0	0.7606	0.7606	74.0	74.0	118.0	0.6271	0.6271	0.0	0.0	0.0	0.0	0.0
0.0	21.0	357	2.8202	0.0056	2319.1354	1607.5022	419.0	570.0	0.7351	419.0	0.7351	116.0	116.0	158.0	0.7342	0.7342	121.0	121.0	152.0	0.7961	0.7961	108.0	108.0	142.0	0.7606	0.7606	74.0	74.0	118.0	0.6271	0.6271	0.0	0.0	0.0	0.0	0.0
0.0	22.0	374	2.8085	0.0056	2309.5375	1600.8494	420.0	570.0	0.7368	419.0	0.7351	115.0	116.0	158.0	0.7342	0.7278	122.0	122.0	152.0	0.8026	0.8026	108.0	108.0	142.0	0.7606	0.7606	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