ARC-Easy_Llama-3.2-1B-nwtxni4g

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: 3.1932
• Model Preparation Time: 0.0059
• Mdl: 2625.8782
• Accumulated Loss: 1820.1201
• Correct Preds: 400.0
• Total Preds: 570.0
• Accuracy: 0.7018
• Correct Gen Preds: 395.0
• Gen Accuracy: 0.6930
• Correct Gen Preds 32: 107.0
• Correct Preds 32: 110.0
• Total Labels 32: 158.0
• Accuracy 32: 0.6962
• Gen Accuracy 32: 0.6772
• Correct Gen Preds 33: 102.0
• Correct Preds 33: 104.0
• Total Labels 33: 152.0
• Accuracy 33: 0.6842
• Gen Accuracy 33: 0.6711
• Correct Gen Preds 34: 107.0
• Correct Preds 34: 107.0
• Total Labels 34: 142.0
• Accuracy 34: 0.7535
• Gen Accuracy 34: 0.7535
• 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: 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	0.0059	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
0.8304	1.0	5	1.6613	0.0059	1366.1795	946.9635	158.0	570.0	0.2772	158.0	0.2772	158.0	158.0	158.0	1.0	1.0	0.0	0.0	152.0	0.0	0.0	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
0.1331	2.0	10	1.6711	0.0059	1374.2163	952.5341	366.0	570.0	0.6421	327.0	0.5737	55.0	85.0	158.0	0.5380	0.3481	97.0	100.0	152.0	0.6579	0.6382	92.0	97.0	142.0	0.6831	0.6479	83.0	84.0	118.0	0.7119	0.7034	0.0	0.0	0.0	0.0	0.0
1.8083	3.0	15	1.4665	0.0059	1205.9506	835.9012	374.0	570.0	0.6561	374.0	0.6561	84.0	84.0	158.0	0.5316	0.5316	115.0	115.0	152.0	0.7566	0.7566	86.0	86.0	142.0	0.6056	0.6056	89.0	89.0	118.0	0.7542	0.7542	0.0	0.0	0.0	0.0	0.0
0.0009	4.0	20	1.8073	0.0059	1486.1779	1030.1400	392.0	570.0	0.6877	391.0	0.6860	120.0	121.0	158.0	0.7658	0.7595	92.0	92.0	152.0	0.6053	0.6053	97.0	97.0	142.0	0.6831	0.6831	82.0	82.0	118.0	0.6949	0.6949	0.0	0.0	0.0	0.0	0.0
0.0011	5.0	25	2.0931	0.0059	1721.2519	1193.0809	398.0	570.0	0.6982	384.0	0.6737	107.0	115.0	158.0	0.7278	0.6772	103.0	108.0	152.0	0.7105	0.6776	98.0	98.0	142.0	0.6901	0.6901	76.0	77.0	118.0	0.6525	0.6441	0.0	0.0	0.0	0.0	0.0
0.0	6.0	30	3.1932	0.0059	2625.8782	1820.1201	400.0	570.0	0.7018	395.0	0.6930	107.0	110.0	158.0	0.6962	0.6772	102.0	104.0	152.0	0.6842	0.6711	107.0	107.0	142.0	0.7535	0.7535	79.0	79.0	118.0	0.6695	0.6695	0.0	0.0	0.0	0.0	0.0
0.0	7.0	35	3.5309	0.0059	2903.6243	2012.6390	390.0	570.0	0.6842	380.0	0.6667	113.0	119.0	158.0	0.7532	0.7152	98.0	100.0	152.0	0.6579	0.6447	96.0	97.0	142.0	0.6831	0.6761	73.0	74.0	118.0	0.6271	0.6186	0.0	0.0	0.0	0.0	0.0
0.0	8.0	40	3.6311	0.0059	2986.0010	2069.7382	391.0	570.0	0.6860	379.0	0.6649	113.0	121.0	158.0	0.7658	0.7152	101.0	104.0	152.0	0.6842	0.6645	92.0	93.0	142.0	0.6549	0.6479	73.0	73.0	118.0	0.6186	0.6186	0.0	0.0	0.0	0.0	0.0
0.0	9.0	45	3.6229	0.0059	2979.2623	2065.0673	387.0	570.0	0.6789	376.0	0.6596	110.0	116.0	158.0	0.7342	0.6962	103.0	106.0	152.0	0.6974	0.6776	92.0	94.0	142.0	0.6620	0.6479	71.0	71.0	118.0	0.6017	0.6017	0.0	0.0	0.0	0.0	0.0
0.0001	10.0	50	3.7087	0.0059	3049.7654	2113.9363	386.0	570.0	0.6772	374.0	0.6561	108.0	114.0	158.0	0.7215	0.6835	104.0	107.0	152.0	0.7039	0.6842	91.0	93.0	142.0	0.6549	0.6408	71.0	72.0	118.0	0.6102	0.6017	0.0	0.0	0.0	0.0	0.0
0.0	11.0	55	3.7644	0.0059	3095.5786	2145.6916	390.0	570.0	0.6842	381.0	0.6684	108.0	113.0	158.0	0.7152	0.6835	106.0	108.0	152.0	0.7105	0.6974	94.0	95.0	142.0	0.6690	0.6620	73.0	74.0	118.0	0.6271	0.6186	0.0	0.0	0.0	0.0	0.0
0.0	12.0	60	3.8077	0.0059	3131.2368	2170.4080	388.0	570.0	0.6807	378.0	0.6632	108.0	113.0	158.0	0.7152	0.6835	105.0	107.0	152.0	0.7039	0.6908	94.0	96.0	142.0	0.6761	0.6620	71.0	72.0	118.0	0.6102	0.6017	0.0	0.0	0.0	0.0	0.0
0.0	13.0	65	3.8259	0.0059	3146.2115	2180.7876	390.0	570.0	0.6842	377.0	0.6614	108.0	115.0	158.0	0.7278	0.6835	104.0	107.0	152.0	0.7039	0.6842	94.0	96.0	142.0	0.6761	0.6620	71.0	72.0	118.0	0.6102	0.6017	0.0	0.0	0.0	0.0	0.0
0.0	14.0	70	3.8349	0.0059	3153.6059	2185.9130	390.0	570.0	0.6842	378.0	0.6632	108.0	114.0	158.0	0.7215	0.6835	104.0	107.0	152.0	0.7039	0.6842	94.0	96.0	142.0	0.6761	0.6620	72.0	73.0	118.0	0.6186	0.6102	0.0	0.0	0.0	0.0	0.0
0.0	15.0	75	3.8388	0.0059	3156.8245	2188.1440	390.0	570.0	0.6842	379.0	0.6649	108.0	114.0	158.0	0.7215	0.6835	105.0	107.0	152.0	0.7039	0.6908	94.0	96.0	142.0	0.6761	0.6620	72.0	73.0	118.0	0.6186	0.6102	0.0	0.0	0.0	0.0	0.0
0.0	16.0	80	3.8554	0.0059	3170.4579	2197.5939	389.0	570.0	0.6825	378.0	0.6632	108.0	114.0	158.0	0.7215	0.6835	104.0	107.0	152.0	0.7039	0.6842	94.0	95.0	142.0	0.6690	0.6620	72.0	73.0	118.0	0.6186	0.6102	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