ARC-Easy_Llama-3.2-1B-blvojtf1

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: 2.6796
• Model Preparation Time: 0.006
• Mdl: 2203.5395
• Accumulated Loss: 1527.3772
• Correct Preds: 389.0
• Total Preds: 570.0
• Accuracy: 0.6825
• Correct Gen Preds: 327.0
• Gen Accuracy: 0.5737
• Correct Gen Preds 32: 62.0
• Correct Preds 32: 98.0
• Total Labels 32: 158.0
• Accuracy 32: 0.6203
• Gen Accuracy 32: 0.3924
• Correct Gen Preds 33: 95.0
• Correct Preds 33: 105.0
• Total Labels 33: 152.0
• Accuracy 33: 0.6908
• Gen Accuracy 33: 0.625
• Correct Gen Preds 34: 100.0
• Correct Preds 34: 108.0
• Total Labels 34: 142.0
• Accuracy 34: 0.7606
• Gen Accuracy 34: 0.7042
• Correct Gen Preds 35: 70.0
• Correct Preds 35: 78.0
• Total Labels 35: 118.0
• Accuracy 35: 0.6610
• Gen Accuracy 35: 0.5932
• 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	0.006	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
1.3822	1.0	1	1.5354	0.006	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
1.3877	2.0	2	2.3794	0.006	1956.6844	1356.2703	153.0	570.0	0.2684	153.0	0.2684	0.0	0.0	158.0	0.0	0.0	152.0	152.0	152.0	1.0	1.0	1.0	1.0	142.0	0.0070	0.0070	0.0	0.0	118.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
2.1356	3.0	3	1.4237	0.006	1170.7407	811.4956	203.0	570.0	0.3561	203.0	0.3561	89.0	89.0	158.0	0.5633	0.5633	114.0	114.0	152.0	0.75	0.75	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
0.8074	4.0	4	1.6417	0.006	1350.0078	935.7541	216.0	570.0	0.3789	216.0	0.3789	155.0	155.0	158.0	0.9810	0.9810	6.0	6.0	152.0	0.0395	0.0395	38.0	38.0	142.0	0.2676	0.2676	17.0	17.0	118.0	0.1441	0.1441	0.0	0.0	0.0	0.0	0.0
0.3151	5.0	5	1.1711	0.006	963.0449	667.5319	358.0	570.0	0.6281	351.0	0.6158	105.0	108.0	158.0	0.6835	0.6646	68.0	71.0	152.0	0.4671	0.4474	100.0	100.0	142.0	0.7042	0.7042	78.0	79.0	118.0	0.6695	0.6610	0.0	0.0	0.0	0.0	0.0
0.0313	6.0	6	1.8008	0.006	1480.8473	1026.4451	384.0	570.0	0.6737	333.0	0.5842	68.0	99.0	158.0	0.6266	0.4304	91.0	101.0	152.0	0.6645	0.5987	101.0	108.0	142.0	0.7606	0.7113	73.0	76.0	118.0	0.6441	0.6186	0.0	0.0	0.0	0.0	0.0
0.0007	7.0	7	2.6796	0.006	2203.5395	1527.3772	389.0	570.0	0.6825	327.0	0.5737	62.0	98.0	158.0	0.6203	0.3924	95.0	105.0	152.0	0.6908	0.625	100.0	108.0	142.0	0.7606	0.7042	70.0	78.0	118.0	0.6610	0.5932	0.0	0.0	0.0	0.0	0.0
0.0	8.0	8	3.2573	0.006	2678.6154	1856.6747	382.0	570.0	0.6702	317.0	0.5561	56.0	98.0	158.0	0.6203	0.3544	99.0	106.0	152.0	0.6974	0.6513	95.0	104.0	142.0	0.7324	0.6690	67.0	74.0	118.0	0.6271	0.5678	0.0	0.0	0.0	0.0	0.0
0.0	9.0	9	3.6049	0.006	2964.4270	2054.7842	378.0	570.0	0.6632	308.0	0.5404	51.0	95.0	158.0	0.6013	0.3228	101.0	106.0	152.0	0.6974	0.6645	91.0	103.0	142.0	0.7254	0.6408	65.0	74.0	118.0	0.6271	0.5508	0.0	0.0	0.0	0.0	0.0
0.0	10.0	10	3.8133	0.006	3135.8227	2173.5867	376.0	570.0	0.6596	300.0	0.5263	46.0	94.0	158.0	0.5949	0.2911	102.0	108.0	152.0	0.7105	0.6711	88.0	103.0	142.0	0.7254	0.6197	64.0	71.0	118.0	0.6017	0.5424	0.0	0.0	0.0	0.0	0.0
0.0	11.0	11	3.9897	0.006	3280.8607	2274.1194	374.0	570.0	0.6561	292.0	0.5123	43.0	94.0	158.0	0.5949	0.2722	103.0	109.0	152.0	0.7171	0.6776	85.0	102.0	142.0	0.7183	0.5986	61.0	69.0	118.0	0.5847	0.5169	0.0	0.0	0.0	0.0	0.0
0.0	12.0	12	4.0979	0.006	3369.8531	2335.8042	371.0	570.0	0.6509	283.0	0.4965	39.0	93.0	158.0	0.5886	0.2468	101.0	107.0	152.0	0.7039	0.6645	81.0	103.0	142.0	0.7254	0.5704	62.0	68.0	118.0	0.5763	0.5254	0.0	0.0	0.0	0.0	0.0
0.0	13.0	13	4.1798	0.006	3437.2348	2382.5096	374.0	570.0	0.6561	284.0	0.4982	39.0	94.0	158.0	0.5949	0.2468	101.0	109.0	152.0	0.7171	0.6645	82.0	103.0	142.0	0.7254	0.5775	62.0	68.0	118.0	0.5763	0.5254	0.0	0.0	0.0	0.0	0.0
0.0	14.0	14	4.2962	0.006	3532.8950	2448.8162	370.0	570.0	0.6491	281.0	0.4930	38.0	91.0	158.0	0.5759	0.2405	100.0	109.0	152.0	0.7171	0.6579	81.0	102.0	142.0	0.7183	0.5704	62.0	68.0	118.0	0.5763	0.5254	0.0	0.0	0.0	0.0	0.0
0.0	15.0	15	4.3080	0.006	3542.6472	2455.5760	371.0	570.0	0.6509	281.0	0.4930	37.0	91.0	158.0	0.5759	0.2342	99.0	110.0	152.0	0.7237	0.6513	83.0	102.0	142.0	0.7183	0.5845	62.0	68.0	118.0	0.5763	0.5254	0.0	0.0	0.0	0.0	0.0
0.0	16.0	16	4.3532	0.006	3579.7884	2481.3203	372.0	570.0	0.6526	281.0	0.4930	36.0	92.0	158.0	0.5823	0.2278	100.0	109.0	152.0	0.7171	0.6579	83.0	103.0	142.0	0.7254	0.5845	62.0	68.0	118.0	0.5763	0.5254	0.0	0.0	0.0	0.0	0.0
0.0	17.0	17	4.3767	0.006	3599.1354	2494.7306	372.0	570.0	0.6526	280.0	0.4912	35.0	92.0	158.0	0.5823	0.2215	100.0	110.0	152.0	0.7237	0.6579	83.0	102.0	142.0	0.7183	0.5845	62.0	68.0	118.0	0.5763	0.5254	0.0	0.0	0.0	0.0	0.0
0.0	18.0	18	4.4285	0.006	3641.7234	2524.2503	371.0	570.0	0.6509	278.0	0.4877	36.0	91.0	158.0	0.5759	0.2278	99.0	110.0	152.0	0.7237	0.6513	81.0	102.0	142.0	0.7183	0.5704	62.0	68.0	118.0	0.5763	0.5254	0.0	0.0	0.0	0.0	0.0
0.0	19.0	19	4.4736	0.006	3678.7926	2549.9447	371.0	570.0	0.6509	277.0	0.4860	35.0	91.0	158.0	0.5759	0.2215	100.0	110.0	152.0	0.7237	0.6579	80.0	102.0	142.0	0.7183	0.5634	62.0	68.0	118.0	0.5763	0.5254	0.0	0.0	0.0	0.0	0.0
0.0	20.0	20	4.4894	0.006	3691.8132	2558.9699	370.0	570.0	0.6491	276.0	0.4842	35.0	90.0	158.0	0.5696	0.2215	98.0	110.0	152.0	0.7237	0.6447	81.0	102.0	142.0	0.7183	0.5704	62.0	68.0	118.0	0.5763	0.5254	0.0	0.0	0.0	0.0	0.0
0.0	21.0	21	4.4970	0.006	3698.0504	2563.2932	371.0	570.0	0.6509	277.0	0.4860	35.0	91.0	158.0	0.5759	0.2215	98.0	110.0	152.0	0.7237	0.6447	82.0	102.0	142.0	0.7183	0.5775	62.0	68.0	118.0	0.5763	0.5254	0.0	0.0	0.0	0.0	0.0
0.0	22.0	22	4.5302	0.006	3725.3769	2582.2345	369.0	570.0	0.6474	276.0	0.4842	34.0	88.0	158.0	0.5570	0.2152	99.0	110.0	152.0	0.7237	0.6513	82.0	103.0	142.0	0.7254	0.5775	61.0	68.0	118.0	0.5763	0.5169	0.0	0.0	0.0	0.0	0.0
0.0	23.0	23	4.5179	0.006	3715.2392	2575.2076	367.0	570.0	0.6439	277.0	0.4860	35.0	89.0	158.0	0.5633	0.2215	100.0	110.0	152.0	0.7237	0.6579	82.0	102.0	142.0	0.7183	0.5775	60.0	66.0	118.0	0.5593	0.5085	0.0	0.0	0.0	0.0	0.0
0.0	24.0	24	4.5215	0.006	3718.1928	2577.2548	371.0	570.0	0.6509	277.0	0.4860	34.0	91.0	158.0	0.5759	0.2152	98.0	110.0	152.0	0.7237	0.6447	84.0	102.0	142.0	0.7183	0.5915	61.0	68.0	118.0	0.5763	0.5169	0.0	0.0	0.0	0.0	0.0
0.0	25.0	25	4.5525	0.006	3743.7136	2594.9445	367.0	570.0	0.6439	278.0	0.4877	34.0	89.0	158.0	0.5633	0.2152	99.0	110.0	152.0	0.7237	0.6513	84.0	102.0	142.0	0.7183	0.5915	61.0	66.0	118.0	0.5593	0.5169	0.0	0.0	0.0	0.0	0.0
0.0	26.0	26	4.5539	0.006	3744.8645	2595.7423	369.0	570.0	0.6474	278.0	0.4877	35.0	90.0	158.0	0.5696	0.2215	99.0	110.0	152.0	0.7237	0.6513	83.0	102.0	142.0	0.7183	0.5845	61.0	67.0	118.0	0.5678	0.5169	0.0	0.0	0.0	0.0	0.0
0.0	27.0	27	4.5578	0.006	3748.0692	2597.9636	368.0	570.0	0.6456	277.0	0.4860	35.0	89.0	158.0	0.5633	0.2215	98.0	110.0	152.0	0.7237	0.6447	84.0	103.0	142.0	0.7254	0.5915	60.0	66.0	118.0	0.5593	0.5085	0.0	0.0	0.0	0.0	0.0
0.0	28.0	28	4.5636	0.006	3752.8202	2601.2567	368.0	570.0	0.6456	279.0	0.4895	36.0	90.0	158.0	0.5696	0.2278	99.0	110.0	152.0	0.7237	0.6513	83.0	102.0	142.0	0.7183	0.5845	61.0	66.0	118.0	0.5593	0.5169	0.0	0.0	0.0	0.0	0.0
0.0	29.0	29	4.5600	0.006	3749.8841	2599.2216	369.0	570.0	0.6474	278.0	0.4877	34.0	89.0	158.0	0.5633	0.2152	99.0	110.0	152.0	0.7237	0.6513	84.0	103.0	142.0	0.7254	0.5915	61.0	67.0	118.0	0.5678	0.5169	0.0	0.0	0.0	0.0	0.0
0.0	30.0	30	4.5483	0.006	3740.2175	2592.5212	370.0	570.0	0.6491	277.0	0.4860	34.0	90.0	158.0	0.5696	0.2152	98.0	110.0	152.0	0.7237	0.6447	85.0	103.0	142.0	0.7254	0.5986	60.0	67.0	118.0	0.5678	0.5085	0.0	0.0	0.0	0.0	0.0
0.0	31.0	31	4.5859	0.006	3771.1699	2613.9758	369.0	570.0	0.6474	277.0	0.4860	35.0	90.0	158.0	0.5696	0.2215	99.0	110.0	152.0	0.7237	0.6513	82.0	103.0	142.0	0.7254	0.5775	61.0	66.0	118.0	0.5593	0.5169	0.0	0.0	0.0	0.0	0.0
0.0	32.0	32	4.5906	0.006	3775.0048	2616.6339	367.0	570.0	0.6439	278.0	0.4877	34.0	89.0	158.0	0.5633	0.2152	99.0	110.0	152.0	0.7237	0.6513	86.0	103.0	142.0	0.7254	0.6056	59.0	65.0	118.0	0.5508	0.5	0.0	0.0	0.0	0.0	0.0
0.0	33.0	33	4.5403	0.006	3733.6585	2587.9749	373.0	570.0	0.6544	282.0	0.4947	35.0	92.0	158.0	0.5823	0.2215	99.0	110.0	152.0	0.7237	0.6513	86.0	103.0	142.0	0.7254	0.6056	62.0	68.0	118.0	0.5763	0.5254	0.0	0.0	0.0	0.0	0.0
0.0	34.0	34	4.5964	0.006	3779.8051	2619.9612	368.0	570.0	0.6456	278.0	0.4877	35.0	90.0	158.0	0.5696	0.2215	99.0	110.0	152.0	0.7237	0.6513	84.0	101.0	142.0	0.7113	0.5915	60.0	67.0	118.0	0.5678	0.5085	0.0	0.0	0.0	0.0	0.0
0.0	35.0	35	4.5792	0.006	3765.6775	2610.1687	369.0	570.0	0.6474	279.0	0.4895	34.0	91.0	158.0	0.5759	0.2152	100.0	110.0	152.0	0.7237	0.6579	85.0	103.0	142.0	0.7254	0.5986	60.0	65.0	118.0	0.5508	0.5085	0.0	0.0	0.0	0.0	0.0
0.0	36.0	36	4.5957	0.006	3779.2287	2619.5617	371.0	570.0	0.6509	283.0	0.4965	36.0	91.0	158.0	0.5759	0.2278	100.0	110.0	152.0	0.7237	0.6579	85.0	102.0	142.0	0.7183	0.5986	62.0	68.0	118.0	0.5763	0.5254	0.0	0.0	0.0	0.0	0.0
0.0	37.0	37	4.5726	0.006	3760.2223	2606.3875	370.0	570.0	0.6491	283.0	0.4965	36.0	90.0	158.0	0.5696	0.2278	101.0	110.0	152.0	0.7237	0.6645	85.0	103.0	142.0	0.7254	0.5986	61.0	67.0	118.0	0.5678	0.5169	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