ARC-Challenge_Llama-3.2-1B-xiv8i84s

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: 1.3993
• Model Preparation Time: 0.0061
• Mdl: 603.5987
• Accumulated Loss: 418.3828
• Correct Preds: 117.0
• Total Preds: 299.0
• Accuracy: 0.3913
• Correct Gen Preds: 117.0
• Gen Accuracy: 0.3913
• Correct Gen Preds 32: 22.0
• Correct Preds 32: 22.0
• Total Labels 32: 64.0
• Accuracy 32: 0.3438
• Gen Accuracy 32: 0.3438
• Correct Gen Preds 33: 31.0
• Correct Preds 33: 31.0
• Total Labels 33: 73.0
• Accuracy 33: 0.4247
• Gen Accuracy 33: 0.4247
• Correct Gen Preds 34: 31.0
• Correct Preds 34: 31.0
• Total Labels 34: 78.0
• Accuracy 34: 0.3974
• Gen Accuracy 34: 0.3974
• Correct Gen Preds 35: 33.0
• Correct Preds 35: 33.0
• Total Labels 35: 83.0
• Accuracy 35: 0.3976
• Gen Accuracy 35: 0.3976
• Correct Gen Preds 36: 0.0
• Correct Preds 36: 0.0
• Total Labels 36: 1.0
• 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-06
• 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	1.6389	0.0061	706.9523	490.0220	66.0	299.0	0.2207	66.0	0.2207	62.0	62.0	64.0	0.9688	0.9688	0.0	0.0	73.0	0.0	0.0	4.0	4.0	78.0	0.0513	0.0513	0.0	0.0	83.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0
1.3934	1.0	18	1.4315	0.0061	617.4876	428.0098	95.0	299.0	0.3177	95.0	0.3177	22.0	22.0	64.0	0.3438	0.3438	7.0	7.0	73.0	0.0959	0.0959	63.0	63.0	78.0	0.8077	0.8077	3.0	3.0	83.0	0.0361	0.0361	0.0	0.0	1.0	0.0	0.0
1.3114	2.0	36	1.3692	0.0061	590.6380	409.3991	93.0	299.0	0.3110	93.0	0.3110	11.0	11.0	64.0	0.1719	0.1719	44.0	44.0	73.0	0.6027	0.6027	19.0	19.0	78.0	0.2436	0.2436	19.0	19.0	83.0	0.2289	0.2289	0.0	0.0	1.0	0.0	0.0
1.2988	3.0	54	1.3415	0.0061	578.6677	401.1019	100.0	299.0	0.3344	100.0	0.3344	15.0	15.0	64.0	0.2344	0.2344	31.0	31.0	73.0	0.4247	0.4247	33.0	33.0	78.0	0.4231	0.4231	21.0	21.0	83.0	0.2530	0.2530	0.0	0.0	1.0	0.0	0.0
1.2075	4.0	72	1.3369	0.0061	576.7045	399.7411	109.0	299.0	0.3645	109.0	0.3645	15.0	15.0	64.0	0.2344	0.2344	31.0	31.0	73.0	0.4247	0.4247	41.0	41.0	78.0	0.5256	0.5256	22.0	22.0	83.0	0.2651	0.2651	0.0	0.0	1.0	0.0	0.0
1.2872	5.0	90	1.3448	0.0061	580.0996	402.0944	109.0	299.0	0.3645	109.0	0.3645	21.0	21.0	64.0	0.3281	0.3281	31.0	31.0	73.0	0.4247	0.4247	31.0	31.0	78.0	0.3974	0.3974	26.0	26.0	83.0	0.3133	0.3133	0.0	0.0	1.0	0.0	0.0
1.1451	6.0	108	1.3456	0.0061	580.4255	402.3203	113.0	299.0	0.3779	113.0	0.3779	22.0	22.0	64.0	0.3438	0.3438	29.0	29.0	73.0	0.3973	0.3973	33.0	33.0	78.0	0.4231	0.4231	29.0	29.0	83.0	0.3494	0.3494	0.0	0.0	1.0	0.0	0.0
1.1946	7.0	126	1.3591	0.0061	586.2816	406.3794	114.0	299.0	0.3813	114.0	0.3813	23.0	23.0	64.0	0.3594	0.3594	31.0	31.0	73.0	0.4247	0.4247	34.0	34.0	78.0	0.4359	0.4359	26.0	26.0	83.0	0.3133	0.3133	0.0	0.0	1.0	0.0	0.0
0.9206	8.0	144	1.3674	0.0061	589.8591	408.8592	113.0	299.0	0.3779	113.0	0.3779	21.0	21.0	64.0	0.3281	0.3281	30.0	30.0	73.0	0.4110	0.4110	33.0	33.0	78.0	0.4231	0.4231	29.0	29.0	83.0	0.3494	0.3494	0.0	0.0	1.0	0.0	0.0
0.9886	9.0	162	1.3719	0.0061	591.7926	410.1994	115.0	299.0	0.3846	115.0	0.3846	22.0	22.0	64.0	0.3438	0.3438	29.0	29.0	73.0	0.3973	0.3973	33.0	33.0	78.0	0.4231	0.4231	31.0	31.0	83.0	0.3735	0.3735	0.0	0.0	1.0	0.0	0.0
1.2581	10.0	180	1.3810	0.0061	595.7011	412.9085	115.0	299.0	0.3846	115.0	0.3846	21.0	21.0	64.0	0.3281	0.3281	30.0	30.0	73.0	0.4110	0.4110	32.0	32.0	78.0	0.4103	0.4103	32.0	32.0	83.0	0.3855	0.3855	0.0	0.0	1.0	0.0	0.0
1.1873	11.0	198	1.3873	0.0061	598.4510	414.8146	114.0	299.0	0.3813	114.0	0.3813	20.0	20.0	64.0	0.3125	0.3125	30.0	30.0	73.0	0.4110	0.4110	32.0	32.0	78.0	0.4103	0.4103	32.0	32.0	83.0	0.3855	0.3855	0.0	0.0	1.0	0.0	0.0
0.9558	12.0	216	1.3993	0.0061	603.5987	418.3828	117.0	299.0	0.3913	117.0	0.3913	22.0	22.0	64.0	0.3438	0.3438	31.0	31.0	73.0	0.4247	0.4247	31.0	31.0	78.0	0.3974	0.3974	33.0	33.0	83.0	0.3976	0.3976	0.0	0.0	1.0	0.0	0.0
1.188	13.0	234	1.4141	0.0061	609.9838	422.8085	114.0	299.0	0.3813	114.0	0.3813	24.0	24.0	64.0	0.375	0.375	27.0	27.0	73.0	0.3699	0.3699	30.0	30.0	78.0	0.3846	0.3846	33.0	33.0	83.0	0.3976	0.3976	0.0	0.0	1.0	0.0	0.0
1.157	14.0	252	1.4277	0.0061	615.8563	426.8791	115.0	299.0	0.3846	115.0	0.3846	23.0	23.0	64.0	0.3594	0.3594	29.0	29.0	73.0	0.3973	0.3973	30.0	30.0	78.0	0.3846	0.3846	33.0	33.0	83.0	0.3976	0.3976	0.0	0.0	1.0	0.0	0.0
1.2143	15.0	270	1.4394	0.0061	620.8877	430.3666	114.0	299.0	0.3813	114.0	0.3813	25.0	25.0	64.0	0.3906	0.3906	27.0	27.0	73.0	0.3699	0.3699	29.0	29.0	78.0	0.3718	0.3718	33.0	33.0	83.0	0.3976	0.3976	0.0	0.0	1.0	0.0	0.0
0.7737	16.0	288	1.4445	0.0061	623.0950	431.8965	114.0	299.0	0.3813	114.0	0.3813	24.0	24.0	64.0	0.375	0.375	29.0	29.0	73.0	0.3973	0.3973	28.0	28.0	78.0	0.3590	0.3590	33.0	33.0	83.0	0.3976	0.3976	0.0	0.0	1.0	0.0	0.0
1.1038	17.0	306	1.4587	0.0061	629.2456	436.1598	113.0	299.0	0.3779	113.0	0.3779	24.0	24.0	64.0	0.375	0.375	28.0	28.0	73.0	0.3836	0.3836	28.0	28.0	78.0	0.3590	0.3590	33.0	33.0	83.0	0.3976	0.3976	0.0	0.0	1.0	0.0	0.0
1.0035	18.0	324	1.4778	0.0061	637.4820	441.8688	113.0	299.0	0.3779	113.0	0.3779	25.0	25.0	64.0	0.3906	0.3906	28.0	28.0	73.0	0.3836	0.3836	27.0	27.0	78.0	0.3462	0.3462	33.0	33.0	83.0	0.3976	0.3976	0.0	0.0	1.0	0.0	0.0
1.0202	19.0	342	1.4850	0.0061	640.5834	444.0186	114.0	299.0	0.3813	114.0	0.3813	25.0	25.0	64.0	0.3906	0.3906	29.0	29.0	73.0	0.3973	0.3973	27.0	27.0	78.0	0.3462	0.3462	33.0	33.0	83.0	0.3976	0.3976	0.0	0.0	1.0	0.0	0.0
0.9705	20.0	360	1.4979	0.0061	646.1607	447.8845	114.0	299.0	0.3813	114.0	0.3813	24.0	24.0	64.0	0.375	0.375	29.0	29.0	73.0	0.3973	0.3973	28.0	28.0	78.0	0.3590	0.3590	33.0	33.0	83.0	0.3976	0.3976	0.0	0.0	1.0	0.0	0.0
0.964	21.0	378	1.5103	0.0061	651.4758	451.5686	116.0	299.0	0.3880	116.0	0.3880	25.0	25.0	64.0	0.3906	0.3906	29.0	29.0	73.0	0.3973	0.3973	28.0	28.0	78.0	0.3590	0.3590	34.0	34.0	83.0	0.4096	0.4096	0.0	0.0	1.0	0.0	0.0
1.1157	22.0	396	1.5239	0.0061	657.3471	455.6383	115.0	299.0	0.3846	115.0	0.3846	25.0	25.0	64.0	0.3906	0.3906	28.0	28.0	73.0	0.3836	0.3836	28.0	28.0	78.0	0.3590	0.3590	34.0	34.0	83.0	0.4096	0.4096	0.0	0.0	1.0	0.0	0.0

Framework versions

• Transformers 4.51.3
• Pytorch 2.6.0+cu124
• Datasets 3.5.0
• Tokenizers 0.21.1