ARC-Easy_Llama-3.2-1B-p985fyrb

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: 3.1280
• Model Preparation Time: 0.0059
• Mdl: 2572.2448
• Accumulated Loss: 1782.9442
• Correct Preds: 391.0
• Total Preds: 570.0
• Accuracy: 0.6860
• Correct Gen Preds: 391.0
• Gen Accuracy: 0.6860
• Correct Gen Preds 32: 102.0
• Correct Preds 32: 102.0
• Total Labels 32: 158.0
• Accuracy 32: 0.6456
• Gen Accuracy 32: 0.6456
• Correct Gen Preds 33: 112.0
• Correct Preds 33: 112.0
• Total Labels 33: 152.0
• Accuracy 33: 0.7368
• Gen Accuracy 33: 0.7368
• Correct Gen Preds 34: 98.0
• Correct Preds 34: 98.0
• Total Labels 34: 142.0
• Accuracy 34: 0.6901
• Gen Accuracy 34: 0.6901
• Correct Gen Preds 35: 79.0
• Correct Preds 35: 79.0
• Total Labels 35: 118.0
• Accuracy 35: 0.6695
• Gen Accuracy 35: 0.6695
• 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	0.0059	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
0.8001	1.0	4	1.2027	0.0059	989.0572	685.5622	300.0	570.0	0.5263	300.0	0.5263	22.0	22.0	158.0	0.1392	0.1392	115.0	115.0	152.0	0.7566	0.7566	73.0	73.0	142.0	0.5141	0.5141	90.0	90.0	118.0	0.7627	0.7627	0.0	0.0	0.0	0.0	0.0
0.5383	2.0	8	0.9091	0.0059	747.5608	518.1697	383.0	570.0	0.6719	383.0	0.6719	89.0	89.0	158.0	0.5633	0.5633	116.0	116.0	152.0	0.7632	0.7632	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
0.0249	3.0	12	1.9204	0.0059	1579.2329	1094.6408	390.0	570.0	0.6842	390.0	0.6842	124.0	124.0	158.0	0.7848	0.7848	101.0	101.0	152.0	0.6645	0.6645	97.0	97.0	142.0	0.6831	0.6831	68.0	68.0	118.0	0.5763	0.5763	0.0	0.0	0.0	0.0	0.0
0.0007	4.0	16	2.5739	0.0059	2116.5801	1467.1015	386.0	570.0	0.6772	385.0	0.6754	89.0	90.0	158.0	0.5696	0.5633	117.0	117.0	152.0	0.7697	0.7697	98.0	98.0	142.0	0.6901	0.6901	81.0	81.0	118.0	0.6864	0.6864	0.0	0.0	0.0	0.0	0.0
0.0017	5.0	20	3.1280	0.0059	2572.2448	1782.9442	391.0	570.0	0.6860	391.0	0.6860	102.0	102.0	158.0	0.6456	0.6456	112.0	112.0	152.0	0.7368	0.7368	98.0	98.0	142.0	0.6901	0.6901	79.0	79.0	118.0	0.6695	0.6695	0.0	0.0	0.0	0.0	0.0
0.0003	6.0	24	3.8452	0.0059	3162.0871	2191.7918	386.0	570.0	0.6772	386.0	0.6772	110.0	110.0	158.0	0.6962	0.6962	103.0	103.0	152.0	0.6776	0.6776	94.0	94.0	142.0	0.6620	0.6620	79.0	79.0	118.0	0.6695	0.6695	0.0	0.0	0.0	0.0	0.0
0.0	7.0	28	4.4219	0.0059	3636.2606	2520.4638	382.0	570.0	0.6702	382.0	0.6702	113.0	113.0	158.0	0.7152	0.7152	100.0	100.0	152.0	0.6579	0.6579	90.0	90.0	142.0	0.6338	0.6338	79.0	79.0	118.0	0.6695	0.6695	0.0	0.0	0.0	0.0	0.0
0.0	8.0	32	4.6772	0.0059	3846.1986	2665.9817	380.0	570.0	0.6667	380.0	0.6667	114.0	114.0	158.0	0.7215	0.7215	97.0	97.0	152.0	0.6382	0.6382	91.0	91.0	142.0	0.6408	0.6408	78.0	78.0	118.0	0.6610	0.6610	0.0	0.0	0.0	0.0	0.0
0.0	9.0	36	4.8253	0.0059	3968.0250	2750.4254	377.0	570.0	0.6614	377.0	0.6614	113.0	113.0	158.0	0.7152	0.7152	95.0	95.0	152.0	0.625	0.625	92.0	92.0	142.0	0.6479	0.6479	77.0	77.0	118.0	0.6525	0.6525	0.0	0.0	0.0	0.0	0.0
0.0	10.0	40	4.9416	0.0059	4063.6575	2816.7127	378.0	570.0	0.6632	378.0	0.6632	113.0	113.0	158.0	0.7152	0.7152	95.0	95.0	152.0	0.625	0.625	93.0	93.0	142.0	0.6549	0.6549	77.0	77.0	118.0	0.6525	0.6525	0.0	0.0	0.0	0.0	0.0
0.0	11.0	44	4.9382	0.0059	4060.8541	2814.7696	377.0	570.0	0.6614	377.0	0.6614	112.0	112.0	158.0	0.7089	0.7089	95.0	95.0	152.0	0.625	0.625	93.0	93.0	142.0	0.6549	0.6549	77.0	77.0	118.0	0.6525	0.6525	0.0	0.0	0.0	0.0	0.0
0.0	12.0	48	5.0121	0.0059	4121.6647	2856.9202	379.0	570.0	0.6649	379.0	0.6649	114.0	114.0	158.0	0.7215	0.7215	95.0	95.0	152.0	0.625	0.625	93.0	93.0	142.0	0.6549	0.6549	77.0	77.0	118.0	0.6525	0.6525	0.0	0.0	0.0	0.0	0.0
0.0	13.0	52	4.9747	0.0059	4090.8914	2835.5898	380.0	570.0	0.6667	380.0	0.6667	113.0	113.0	158.0	0.7152	0.7152	95.0	95.0	152.0	0.625	0.625	94.0	94.0	142.0	0.6620	0.6620	78.0	78.0	118.0	0.6610	0.6610	0.0	0.0	0.0	0.0	0.0
0.0	14.0	56	5.0345	0.0059	4140.0879	2869.6903	380.0	570.0	0.6667	380.0	0.6667	114.0	114.0	158.0	0.7215	0.7215	95.0	95.0	152.0	0.625	0.625	93.0	93.0	142.0	0.6549	0.6549	78.0	78.0	118.0	0.6610	0.6610	0.0	0.0	0.0	0.0	0.0
0.0	15.0	60	5.0372	0.0059	4142.3083	2871.2294	379.0	570.0	0.6649	379.0	0.6649	113.0	113.0	158.0	0.7152	0.7152	94.0	94.0	152.0	0.6184	0.6184	95.0	95.0	142.0	0.6690	0.6690	77.0	77.0	118.0	0.6525	0.6525	0.0	0.0	0.0	0.0	0.0
0.0	16.0	64	5.0324	0.0059	4138.2840	2868.4399	377.0	570.0	0.6614	377.0	0.6614	112.0	112.0	158.0	0.7089	0.7089	95.0	95.0	152.0	0.625	0.625	93.0	93.0	142.0	0.6549	0.6549	77.0	77.0	118.0	0.6525	0.6525	0.0	0.0	0.0	0.0	0.0
0.0	17.0	68	5.0466	0.0059	4150.0401	2876.5886	378.0	570.0	0.6632	378.0	0.6632	112.0	112.0	158.0	0.7089	0.7089	95.0	95.0	152.0	0.625	0.625	93.0	93.0	142.0	0.6549	0.6549	78.0	78.0	118.0	0.6610	0.6610	0.0	0.0	0.0	0.0	0.0
0.0	18.0	72	5.0758	0.0059	4174.0155	2893.2071	376.0	570.0	0.6596	376.0	0.6596	112.0	112.0	158.0	0.7089	0.7089	94.0	94.0	152.0	0.6184	0.6184	92.0	92.0	142.0	0.6479	0.6479	78.0	78.0	118.0	0.6610	0.6610	0.0	0.0	0.0	0.0	0.0
0.0	19.0	76	5.0498	0.0059	4152.6162	2878.3742	379.0	570.0	0.6649	379.0	0.6649	113.0	113.0	158.0	0.7152	0.7152	96.0	96.0	152.0	0.6316	0.6316	93.0	93.0	142.0	0.6549	0.6549	77.0	77.0	118.0	0.6525	0.6525	0.0	0.0	0.0	0.0	0.0
0.0	20.0	80	5.0611	0.0059	4161.9058	2884.8132	379.0	570.0	0.6649	379.0	0.6649	112.0	112.0	158.0	0.7089	0.7089	95.0	95.0	152.0	0.625	0.625	94.0	94.0	142.0	0.6620	0.6620	78.0	78.0	118.0	0.6610	0.6610	0.0	0.0	0.0	0.0	0.0
0.0	21.0	84	5.0557	0.0059	4157.5229	2881.7753	378.0	570.0	0.6632	378.0	0.6632	112.0	112.0	158.0	0.7089	0.7089	94.0	94.0	152.0	0.6184	0.6184	94.0	94.0	142.0	0.6620	0.6620	78.0	78.0	118.0	0.6610	0.6610	0.0	0.0	0.0	0.0	0.0
0.0	22.0	88	5.0334	0.0059	4139.1444	2869.0363	379.0	570.0	0.6649	379.0	0.6649	113.0	113.0	158.0	0.7152	0.7152	95.0	95.0	152.0	0.625	0.625	94.0	94.0	142.0	0.6620	0.6620	77.0	77.0	118.0	0.6525	0.6525	0.0	0.0	0.0	0.0	0.0
0.0	23.0	92	5.0884	0.0059	4184.3384	2900.3623	379.0	570.0	0.6649	379.0	0.6649	113.0	113.0	158.0	0.7152	0.7152	95.0	95.0	152.0	0.625	0.625	94.0	94.0	142.0	0.6620	0.6620	77.0	77.0	118.0	0.6525	0.6525	0.0	0.0	0.0	0.0	0.0
0.0	24.0	96	5.0960	0.0059	4190.6606	2904.7446	380.0	570.0	0.6667	380.0	0.6667	112.0	112.0	158.0	0.7089	0.7089	96.0	96.0	152.0	0.6316	0.6316	94.0	94.0	142.0	0.6620	0.6620	78.0	78.0	118.0	0.6610	0.6610	0.0	0.0	0.0	0.0	0.0
0.0	25.0	100	5.0158	0.0059	4124.6379	2858.9811	383.0	570.0	0.6719	383.0	0.6719	114.0	114.0	158.0	0.7215	0.7215	96.0	96.0	152.0	0.6316	0.6316	95.0	95.0	142.0	0.6690	0.6690	78.0	78.0	118.0	0.6610	0.6610	0.0	0.0	0.0	0.0	0.0
0.0	26.0	104	5.0697	0.0059	4168.9987	2889.7297	378.0	570.0	0.6632	378.0	0.6632	112.0	112.0	158.0	0.7089	0.7089	95.0	95.0	152.0	0.625	0.625	93.0	93.0	142.0	0.6549	0.6549	78.0	78.0	118.0	0.6610	0.6610	0.0	0.0	0.0	0.0	0.0
0.0	27.0	108	5.0597	0.0059	4160.7773	2884.0311	379.0	570.0	0.6649	379.0	0.6649	112.0	112.0	158.0	0.7089	0.7089	95.0	95.0	152.0	0.625	0.625	94.0	94.0	142.0	0.6620	0.6620	78.0	78.0	118.0	0.6610	0.6610	0.0	0.0	0.0	0.0	0.0
0.0	28.0	112	5.0579	0.0059	4159.2565	2882.9769	379.0	570.0	0.6649	379.0	0.6649	114.0	114.0	158.0	0.7215	0.7215	94.0	94.0	152.0	0.6184	0.6184	94.0	94.0	142.0	0.6620	0.6620	77.0	77.0	118.0	0.6525	0.6525	0.0	0.0	0.0	0.0	0.0
0.0	29.0	116	5.0731	0.0059	4171.8091	2891.6777	378.0	570.0	0.6632	378.0	0.6632	112.0	112.0	158.0	0.7089	0.7089	94.0	94.0	152.0	0.6184	0.6184	94.0	94.0	142.0	0.6620	0.6620	78.0	78.0	118.0	0.6610	0.6610	0.0	0.0	0.0	0.0	0.0
0.0	30.0	120	5.0587	0.0059	4159.9854	2883.4822	377.0	570.0	0.6614	377.0	0.6614	113.0	113.0	158.0	0.7152	0.7152	93.0	93.0	152.0	0.6118	0.6118	94.0	94.0	142.0	0.6620	0.6620	77.0	77.0	118.0	0.6525	0.6525	0.0	0.0	0.0	0.0	0.0
0.0	31.0	124	5.0970	0.0059	4191.4450	2905.2883	376.0	570.0	0.6596	376.0	0.6596	112.0	112.0	158.0	0.7089	0.7089	94.0	94.0	152.0	0.6184	0.6184	93.0	93.0	142.0	0.6549	0.6549	77.0	77.0	118.0	0.6525	0.6525	0.0	0.0	0.0	0.0	0.0
0.0	32.0	128	5.0980	0.0059	4192.2751	2905.8637	375.0	570.0	0.6579	375.0	0.6579	111.0	111.0	158.0	0.7025	0.7025	94.0	94.0	152.0	0.6184	0.6184	93.0	93.0	142.0	0.6549	0.6549	77.0	77.0	118.0	0.6525	0.6525	0.0	0.0	0.0	0.0	0.0
0.0	33.0	132	5.0987	0.0059	4192.8484	2906.2611	377.0	570.0	0.6614	377.0	0.6614	112.0	112.0	158.0	0.7089	0.7089	94.0	94.0	152.0	0.6184	0.6184	94.0	94.0	142.0	0.6620	0.6620	77.0	77.0	118.0	0.6525	0.6525	0.0	0.0	0.0	0.0	0.0
0.0	34.0	136	5.0753	0.0059	4173.6419	2892.9481	379.0	570.0	0.6649	379.0	0.6649	113.0	113.0	158.0	0.7152	0.7152	95.0	95.0	152.0	0.625	0.625	94.0	94.0	142.0	0.6620	0.6620	77.0	77.0	118.0	0.6525	0.6525	0.0	0.0	0.0	0.0	0.0
0.0	35.0	140	5.0517	0.0059	4154.1764	2879.4557	379.0	570.0	0.6649	379.0	0.6649	112.0	112.0	158.0	0.7089	0.7089	96.0	96.0	152.0	0.6316	0.6316	94.0	94.0	142.0	0.6620	0.6620	77.0	77.0	118.0	0.6525	0.6525	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