ARC-Easy_Llama-3.2-1B-e3y12nob

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: 1.7843
• Model Preparation Time: 0.0058
• Mdl: 1467.3190
• Accumulated Loss: 1017.0681
• Correct Preds: 373.0
• Total Preds: 570.0
• Accuracy: 0.6544
• Correct Gen Preds: 371.0
• Gen Accuracy: 0.6509
• Correct Gen Preds 32: 86.0
• Correct Preds 32: 88.0
• Total Labels 32: 158.0
• Accuracy 32: 0.5570
• Gen Accuracy 32: 0.5443
• Correct Gen Preds 33: 98.0
• Correct Preds 33: 98.0
• Total Labels 33: 152.0
• Accuracy 33: 0.6447
• Gen Accuracy 33: 0.6447
• Correct Gen Preds 34: 106.0
• Correct Preds 34: 106.0
• Total Labels 34: 142.0
• Accuracy 34: 0.7465
• Gen Accuracy 34: 0.7465
• 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: 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	0.0058	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
1.4034	1.0	1	1.5354	0.0058	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
1.4034	2.0	2	2.0016	0.0058	1646.0092	1140.9266	152.0	570.0	0.2667	152.0	0.2667	0.0	0.0	158.0	0.0	0.0	152.0	152.0	152.0	1.0	1.0	0.0	0.0	142.0	0.0	0.0	0.0	0.0	118.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
2.099	3.0	3	1.2303	0.0058	1011.7395	701.2844	269.0	570.0	0.4719	269.0	0.4719	129.0	129.0	158.0	0.8165	0.8165	83.0	83.0	152.0	0.5461	0.5461	12.0	12.0	142.0	0.0845	0.0845	45.0	45.0	118.0	0.3814	0.3814	0.0	0.0	0.0	0.0	0.0
0.8446	4.0	4	2.9659	0.0058	2438.9877	1690.5775	234.0	570.0	0.4105	234.0	0.4105	155.0	155.0	158.0	0.9810	0.9810	10.0	10.0	152.0	0.0658	0.0658	41.0	41.0	142.0	0.2887	0.2887	28.0	28.0	118.0	0.2373	0.2373	0.0	0.0	0.0	0.0	0.0
0.5023	5.0	5	1.5691	0.0058	1290.3219	894.3830	362.0	570.0	0.6351	361.0	0.6333	124.0	125.0	158.0	0.7911	0.7848	72.0	72.0	152.0	0.4737	0.4737	94.0	94.0	142.0	0.6620	0.6620	71.0	71.0	118.0	0.6017	0.6017	0.0	0.0	0.0	0.0	0.0
0.0833	6.0	6	1.7843	0.0058	1467.3190	1017.0681	373.0	570.0	0.6544	371.0	0.6509	86.0	88.0	158.0	0.5570	0.5443	98.0	98.0	152.0	0.6447	0.6447	106.0	106.0	142.0	0.7465	0.7465	81.0	81.0	118.0	0.6864	0.6864	0.0	0.0	0.0	0.0	0.0
0.0242	7.0	7	2.3130	0.0058	1902.0588	1318.4067	369.0	570.0	0.6474	368.0	0.6456	84.0	85.0	158.0	0.5380	0.5316	93.0	93.0	152.0	0.6118	0.6118	105.0	105.0	142.0	0.7394	0.7394	86.0	86.0	118.0	0.7288	0.7288	0.0	0.0	0.0	0.0	0.0
0.0011	8.0	8	3.1593	0.0058	2597.9927	1800.7913	361.0	570.0	0.6333	361.0	0.6333	83.0	83.0	158.0	0.5253	0.5253	91.0	91.0	152.0	0.5987	0.5987	100.0	100.0	142.0	0.7042	0.7042	87.0	87.0	118.0	0.7373	0.7373	0.0	0.0	0.0	0.0	0.0
0.0	9.0	9	3.9066	0.0058	3212.5149	2226.7457	370.0	570.0	0.6491	370.0	0.6491	94.0	94.0	158.0	0.5949	0.5949	91.0	91.0	152.0	0.5987	0.5987	98.0	98.0	142.0	0.6901	0.6901	87.0	87.0	118.0	0.7373	0.7373	0.0	0.0	0.0	0.0	0.0
0.0	10.0	10	4.5443	0.0058	3736.9375	2590.2477	372.0	570.0	0.6526	372.0	0.6526	98.0	98.0	158.0	0.6203	0.6203	89.0	89.0	152.0	0.5855	0.5855	98.0	98.0	142.0	0.6901	0.6901	87.0	87.0	118.0	0.7373	0.7373	0.0	0.0	0.0	0.0	0.0
0.0	11.0	11	5.0526	0.0058	4154.9691	2880.0051	367.0	570.0	0.6439	367.0	0.6439	97.0	97.0	158.0	0.6139	0.6139	88.0	88.0	152.0	0.5789	0.5789	96.0	96.0	142.0	0.6761	0.6761	86.0	86.0	118.0	0.7288	0.7288	0.0	0.0	0.0	0.0	0.0
0.0	12.0	12	5.4403	0.0058	4473.7260	3100.9506	360.0	570.0	0.6316	360.0	0.6316	98.0	98.0	158.0	0.6203	0.6203	84.0	84.0	152.0	0.5526	0.5526	94.0	94.0	142.0	0.6620	0.6620	84.0	84.0	118.0	0.7119	0.7119	0.0	0.0	0.0	0.0	0.0
0.0	13.0	13	5.7488	0.0058	4727.4811	3276.8402	360.0	570.0	0.6316	360.0	0.6316	99.0	99.0	158.0	0.6266	0.6266	84.0	84.0	152.0	0.5526	0.5526	93.0	93.0	142.0	0.6549	0.6549	84.0	84.0	118.0	0.7119	0.7119	0.0	0.0	0.0	0.0	0.0
0.0	14.0	14	5.9176	0.0058	4866.2342	3373.0165	358.0	570.0	0.6281	358.0	0.6281	98.0	98.0	158.0	0.6203	0.6203	84.0	84.0	152.0	0.5526	0.5526	92.0	92.0	142.0	0.6479	0.6479	84.0	84.0	118.0	0.7119	0.7119	0.0	0.0	0.0	0.0	0.0
0.0	15.0	15	6.0544	0.0058	4978.7857	3451.0313	359.0	570.0	0.6298	359.0	0.6298	100.0	100.0	158.0	0.6329	0.6329	84.0	84.0	152.0	0.5526	0.5526	91.0	91.0	142.0	0.6408	0.6408	84.0	84.0	118.0	0.7119	0.7119	0.0	0.0	0.0	0.0	0.0
0.0	16.0	16	6.1369	0.0058	5046.5733	3498.0180	357.0	570.0	0.6263	357.0	0.6263	99.0	99.0	158.0	0.6266	0.6266	83.0	83.0	152.0	0.5461	0.5461	91.0	91.0	142.0	0.6408	0.6408	84.0	84.0	118.0	0.7119	0.7119	0.0	0.0	0.0	0.0	0.0
0.0	17.0	17	6.1920	0.0058	5091.8671	3529.4133	359.0	570.0	0.6298	359.0	0.6298	100.0	100.0	158.0	0.6329	0.6329	83.0	83.0	152.0	0.5461	0.5461	92.0	92.0	142.0	0.6479	0.6479	84.0	84.0	118.0	0.7119	0.7119	0.0	0.0	0.0	0.0	0.0
0.0	18.0	18	6.2775	0.0058	5162.1998	3578.1642	358.0	570.0	0.6281	358.0	0.6281	101.0	101.0	158.0	0.6392	0.6392	82.0	82.0	152.0	0.5395	0.5395	91.0	91.0	142.0	0.6408	0.6408	84.0	84.0	118.0	0.7119	0.7119	0.0	0.0	0.0	0.0	0.0
0.0	19.0	19	6.3184	0.0058	5195.8145	3601.4642	358.0	570.0	0.6281	358.0	0.6281	101.0	101.0	158.0	0.6392	0.6392	82.0	82.0	152.0	0.5395	0.5395	91.0	91.0	142.0	0.6408	0.6408	84.0	84.0	118.0	0.7119	0.7119	0.0	0.0	0.0	0.0	0.0
0.0	20.0	20	6.3640	0.0058	5233.3637	3627.4913	359.0	570.0	0.6298	359.0	0.6298	102.0	102.0	158.0	0.6456	0.6456	82.0	82.0	152.0	0.5395	0.5395	91.0	91.0	142.0	0.6408	0.6408	84.0	84.0	118.0	0.7119	0.7119	0.0	0.0	0.0	0.0	0.0
0.0	21.0	21	6.3601	0.0058	5230.1444	3625.2599	359.0	570.0	0.6298	359.0	0.6298	102.0	102.0	158.0	0.6456	0.6456	82.0	82.0	152.0	0.5395	0.5395	91.0	91.0	142.0	0.6408	0.6408	84.0	84.0	118.0	0.7119	0.7119	0.0	0.0	0.0	0.0	0.0
0.0	22.0	22	6.3605	0.0058	5230.4748	3625.4888	360.0	570.0	0.6316	360.0	0.6316	103.0	103.0	158.0	0.6519	0.6519	82.0	82.0	152.0	0.5395	0.5395	91.0	91.0	142.0	0.6408	0.6408	84.0	84.0	118.0	0.7119	0.7119	0.0	0.0	0.0	0.0	0.0
0.0	23.0	23	6.4136	0.0058	5274.1374	3655.7535	359.0	570.0	0.6298	359.0	0.6298	103.0	103.0	158.0	0.6519	0.6519	82.0	82.0	152.0	0.5395	0.5395	90.0	90.0	142.0	0.6338	0.6338	84.0	84.0	118.0	0.7119	0.7119	0.0	0.0	0.0	0.0	0.0
0.0	24.0	24	6.4393	0.0058	5295.2320	3670.3751	359.0	570.0	0.6298	359.0	0.6298	103.0	103.0	158.0	0.6519	0.6519	82.0	82.0	152.0	0.5395	0.5395	91.0	91.0	142.0	0.6408	0.6408	83.0	83.0	118.0	0.7034	0.7034	0.0	0.0	0.0	0.0	0.0
0.0	25.0	25	6.4741	0.0058	5323.8611	3690.2193	357.0	570.0	0.6263	357.0	0.6263	102.0	102.0	158.0	0.6456	0.6456	82.0	82.0	152.0	0.5395	0.5395	90.0	90.0	142.0	0.6338	0.6338	83.0	83.0	118.0	0.7034	0.7034	0.0	0.0	0.0	0.0	0.0
0.0	26.0	26	6.4570	0.0058	5309.7895	3680.4656	359.0	570.0	0.6298	359.0	0.6298	103.0	103.0	158.0	0.6519	0.6519	82.0	82.0	152.0	0.5395	0.5395	91.0	91.0	142.0	0.6408	0.6408	83.0	83.0	118.0	0.7034	0.7034	0.0	0.0	0.0	0.0	0.0
0.0	27.0	27	6.4664	0.0058	5317.5521	3685.8463	359.0	570.0	0.6298	359.0	0.6298	104.0	104.0	158.0	0.6582	0.6582	82.0	82.0	152.0	0.5395	0.5395	90.0	90.0	142.0	0.6338	0.6338	83.0	83.0	118.0	0.7034	0.7034	0.0	0.0	0.0	0.0	0.0
0.0	28.0	28	6.4505	0.0058	5304.4878	3676.7908	360.0	570.0	0.6316	360.0	0.6316	103.0	103.0	158.0	0.6519	0.6519	82.0	82.0	152.0	0.5395	0.5395	91.0	91.0	142.0	0.6408	0.6408	84.0	84.0	118.0	0.7119	0.7119	0.0	0.0	0.0	0.0	0.0
0.0	29.0	29	6.4675	0.0058	5318.4425	3686.4634	358.0	570.0	0.6281	358.0	0.6281	103.0	103.0	158.0	0.6519	0.6519	82.0	82.0	152.0	0.5395	0.5395	89.0	89.0	142.0	0.6268	0.6268	84.0	84.0	118.0	0.7119	0.7119	0.0	0.0	0.0	0.0	0.0
0.0	30.0	30	6.4893	0.0058	5336.4153	3698.9212	355.0	570.0	0.6228	355.0	0.6228	101.0	101.0	158.0	0.6392	0.6392	82.0	82.0	152.0	0.5395	0.5395	89.0	89.0	142.0	0.6268	0.6268	83.0	83.0	118.0	0.7034	0.7034	0.0	0.0	0.0	0.0	0.0
0.0	31.0	31	6.4985	0.0058	5343.9332	3704.1322	358.0	570.0	0.6281	358.0	0.6281	103.0	103.0	158.0	0.6519	0.6519	82.0	82.0	152.0	0.5395	0.5395	90.0	90.0	142.0	0.6338	0.6338	83.0	83.0	118.0	0.7034	0.7034	0.0	0.0	0.0	0.0	0.0
0.0	32.0	32	6.4701	0.0058	5320.6317	3687.9809	358.0	570.0	0.6281	358.0	0.6281	104.0	104.0	158.0	0.6582	0.6582	81.0	81.0	152.0	0.5329	0.5329	90.0	90.0	142.0	0.6338	0.6338	83.0	83.0	118.0	0.7034	0.7034	0.0	0.0	0.0	0.0	0.0
0.0	33.0	33	6.5003	0.0058	5345.4584	3705.1894	358.0	570.0	0.6281	358.0	0.6281	104.0	104.0	158.0	0.6582	0.6582	82.0	82.0	152.0	0.5395	0.5395	89.0	89.0	142.0	0.6268	0.6268	83.0	83.0	118.0	0.7034	0.7034	0.0	0.0	0.0	0.0	0.0
0.0	34.0	34	6.4873	0.0058	5334.7482	3697.7657	358.0	570.0	0.6281	358.0	0.6281	103.0	103.0	158.0	0.6519	0.6519	82.0	82.0	152.0	0.5395	0.5395	90.0	90.0	142.0	0.6338	0.6338	83.0	83.0	118.0	0.7034	0.7034	0.0	0.0	0.0	0.0	0.0
0.0	35.0	35	6.4845	0.0058	5332.4770	3696.1914	358.0	570.0	0.6281	358.0	0.6281	103.0	103.0	158.0	0.6519	0.6519	82.0	82.0	152.0	0.5395	0.5395	90.0	90.0	142.0	0.6338	0.6338	83.0	83.0	118.0	0.7034	0.7034	0.0	0.0	0.0	0.0	0.0
0.0	36.0	36	6.4977	0.0058	5343.2788	3703.6786	357.0	570.0	0.6263	357.0	0.6263	104.0	104.0	158.0	0.6582	0.6582	82.0	82.0	152.0	0.5395	0.5395	89.0	89.0	142.0	0.6268	0.6268	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