ARC-Easy_Llama-3.2-1B-u93ubukh

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: 2.4577
• Model Preparation Time: 0.0059
• Mdl: 2021.0226
• Accumulated Loss: 1400.8661
• Correct Preds: 400.0
• Total Preds: 570.0
• Accuracy: 0.7018
• Correct Gen Preds: 391.0
• Gen Accuracy: 0.6860
• Correct Gen Preds 32: 113.0
• Correct Preds 32: 115.0
• Total Labels 32: 158.0
• Accuracy 32: 0.7278
• Gen Accuracy 32: 0.7152
• Correct Gen Preds 33: 103.0
• Correct Preds 33: 109.0
• Total Labels 33: 152.0
• Accuracy 33: 0.7171
• Gen Accuracy 33: 0.6776
• Correct Gen Preds 34: 96.0
• Correct Preds 34: 96.0
• Total Labels 34: 142.0
• Accuracy 34: 0.6761
• Gen Accuracy 34: 0.6761
• Correct Gen Preds 35: 79.0
• Correct Preds 35: 80.0
• Total Labels 35: 118.0
• Accuracy 35: 0.6780
• 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
1.1645	1.0	8	1.2427	0.0059	1021.9553	708.3654	358.0	570.0	0.6281	354.0	0.6211	104.0	106.0	158.0	0.6709	0.6582	63.0	64.0	152.0	0.4211	0.4145	111.0	111.0	142.0	0.7817	0.7817	76.0	77.0	118.0	0.6525	0.6441	0.0	0.0	0.0	0.0	0.0
0.5365	2.0	16	0.9998	0.0059	822.1312	569.8579	398.0	570.0	0.6982	398.0	0.6982	112.0	112.0	158.0	0.7089	0.7089	109.0	109.0	152.0	0.7171	0.7171	104.0	104.0	142.0	0.7324	0.7324	73.0	73.0	118.0	0.6186	0.6186	0.0	0.0	0.0	0.0	0.0
0.2407	3.0	24	1.7101	0.0059	1406.2791	974.7584	393.0	570.0	0.6895	392.0	0.6877	98.0	99.0	158.0	0.6266	0.6203	104.0	104.0	152.0	0.6842	0.6842	109.0	109.0	142.0	0.7676	0.7676	81.0	81.0	118.0	0.6864	0.6864	0.0	0.0	0.0	0.0	0.0
0.0882	4.0	32	1.5139	0.0059	1244.9139	862.9086	384.0	570.0	0.6737	380.0	0.6667	108.0	110.0	158.0	0.6962	0.6835	116.0	117.0	152.0	0.7697	0.7632	93.0	94.0	142.0	0.6620	0.6549	63.0	63.0	118.0	0.5339	0.5339	0.0	0.0	0.0	0.0	0.0
0.0068	5.0	40	2.4577	0.0059	2021.0226	1400.8661	400.0	570.0	0.7018	391.0	0.6860	113.0	115.0	158.0	0.7278	0.7152	103.0	109.0	152.0	0.7171	0.6776	96.0	96.0	142.0	0.6761	0.6761	79.0	80.0	118.0	0.6780	0.6695	0.0	0.0	0.0	0.0	0.0
0.0002	6.0	48	2.9360	0.0059	2414.3942	1673.5305	394.0	570.0	0.6912	383.0	0.6719	119.0	120.0	158.0	0.7595	0.7532	101.0	108.0	152.0	0.7105	0.6645	94.0	96.0	142.0	0.6761	0.6620	69.0	70.0	118.0	0.5932	0.5847	0.0	0.0	0.0	0.0	0.0
0.0001	7.0	56	2.9475	0.0059	2423.8483	1680.0836	394.0	570.0	0.6912	382.0	0.6702	112.0	113.0	158.0	0.7152	0.7089	105.0	115.0	152.0	0.7566	0.6908	99.0	99.0	142.0	0.6972	0.6972	66.0	67.0	118.0	0.5678	0.5593	0.0	0.0	0.0	0.0	0.0
0.0001	8.0	64	2.8929	0.0059	2378.9126	1648.9366	395.0	570.0	0.6930	376.0	0.6596	109.0	110.0	158.0	0.6962	0.6899	97.0	111.0	152.0	0.7303	0.6382	99.0	100.0	142.0	0.7042	0.6972	71.0	74.0	118.0	0.6271	0.6017	0.0	0.0	0.0	0.0	0.0
0.0004	9.0	72	3.4450	0.0059	2832.9687	1963.6643	390.0	570.0	0.6842	389.0	0.6825	111.0	112.0	158.0	0.7089	0.7025	115.0	115.0	152.0	0.7566	0.7566	99.0	99.0	142.0	0.6972	0.6972	64.0	64.0	118.0	0.5424	0.5424	0.0	0.0	0.0	0.0	0.0
0.0015	10.0	80	3.5763	0.0059	2940.9268	2038.4952	388.0	570.0	0.6807	387.0	0.6789	111.0	112.0	158.0	0.7089	0.7025	111.0	111.0	152.0	0.7303	0.7303	102.0	102.0	142.0	0.7183	0.7183	63.0	63.0	118.0	0.5339	0.5339	0.0	0.0	0.0	0.0	0.0
0.0	11.0	88	3.5949	0.0059	2956.1769	2049.0657	386.0	570.0	0.6772	383.0	0.6719	116.0	117.0	158.0	0.7405	0.7342	103.0	104.0	152.0	0.6842	0.6776	100.0	100.0	142.0	0.7042	0.7042	64.0	65.0	118.0	0.5508	0.5424	0.0	0.0	0.0	0.0	0.0
0.0	12.0	96	3.7333	0.0059	3070.0089	2127.9680	383.0	570.0	0.6719	382.0	0.6702	117.0	118.0	158.0	0.7468	0.7405	105.0	105.0	152.0	0.6908	0.6908	97.0	97.0	142.0	0.6831	0.6831	63.0	63.0	118.0	0.5339	0.5339	0.0	0.0	0.0	0.0	0.0
0.0	13.0	104	3.8287	0.0059	3148.4799	2182.3600	381.0	570.0	0.6684	379.0	0.6649	116.0	117.0	158.0	0.7405	0.7342	105.0	105.0	152.0	0.6908	0.6908	95.0	96.0	142.0	0.6761	0.6690	63.0	63.0	118.0	0.5339	0.5339	0.0	0.0	0.0	0.0	0.0
0.0	14.0	112	3.8543	0.0059	3169.5456	2196.9616	382.0	570.0	0.6702	381.0	0.6684	115.0	116.0	158.0	0.7342	0.7278	106.0	106.0	152.0	0.6974	0.6974	97.0	97.0	142.0	0.6831	0.6831	63.0	63.0	118.0	0.5339	0.5339	0.0	0.0	0.0	0.0	0.0
0.0	15.0	120	3.8314	0.0059	3150.6978	2183.8973	384.0	570.0	0.6737	382.0	0.6702	117.0	118.0	158.0	0.7468	0.7405	106.0	106.0	152.0	0.6974	0.6974	96.0	97.0	142.0	0.6831	0.6761	63.0	63.0	118.0	0.5339	0.5339	0.0	0.0	0.0	0.0	0.0
0.0	16.0	128	3.8530	0.0059	3168.4544	2196.2053	380.0	570.0	0.6667	379.0	0.6649	115.0	116.0	158.0	0.7342	0.7278	106.0	106.0	152.0	0.6974	0.6974	95.0	95.0	142.0	0.6690	0.6690	63.0	63.0	118.0	0.5339	0.5339	0.0	0.0	0.0	0.0	0.0
0.0	17.0	136	3.8511	0.0059	3166.8983	2195.1266	386.0	570.0	0.6772	385.0	0.6754	116.0	117.0	158.0	0.7405	0.7342	108.0	108.0	152.0	0.7105	0.7105	98.0	98.0	142.0	0.6901	0.6901	63.0	63.0	118.0	0.5339	0.5339	0.0	0.0	0.0	0.0	0.0
0.0	18.0	144	3.8753	0.0059	3186.7729	2208.9026	382.0	570.0	0.6702	381.0	0.6684	116.0	117.0	158.0	0.7405	0.7342	106.0	106.0	152.0	0.6974	0.6974	96.0	96.0	142.0	0.6761	0.6761	63.0	63.0	118.0	0.5339	0.5339	0.0	0.0	0.0	0.0	0.0
0.0	19.0	152	3.8596	0.0059	3173.9030	2199.9819	382.0	570.0	0.6702	381.0	0.6684	116.0	117.0	158.0	0.7405	0.7342	106.0	106.0	152.0	0.6974	0.6974	96.0	96.0	142.0	0.6761	0.6761	63.0	63.0	118.0	0.5339	0.5339	0.0	0.0	0.0	0.0	0.0
0.0	20.0	160	3.8491	0.0059	3165.2186	2193.9624	384.0	570.0	0.6737	382.0	0.6702	116.0	117.0	158.0	0.7405	0.7342	106.0	106.0	152.0	0.6974	0.6974	97.0	98.0	142.0	0.6901	0.6831	63.0	63.0	118.0	0.5339	0.5339	0.0	0.0	0.0	0.0	0.0
0.0	21.0	168	3.8606	0.0059	3174.6803	2200.5207	383.0	570.0	0.6719	382.0	0.6702	116.0	117.0	158.0	0.7405	0.7342	106.0	106.0	152.0	0.6974	0.6974	97.0	97.0	142.0	0.6831	0.6831	63.0	63.0	118.0	0.5339	0.5339	0.0	0.0	0.0	0.0	0.0
0.0	22.0	176	3.8513	0.0059	3167.0872	2195.2576	382.0	570.0	0.6702	381.0	0.6684	116.0	117.0	158.0	0.7405	0.7342	106.0	106.0	152.0	0.6974	0.6974	96.0	96.0	142.0	0.6761	0.6761	63.0	63.0	118.0	0.5339	0.5339	0.0	0.0	0.0	0.0	0.0
0.0	23.0	184	3.8473	0.0059	3163.7476	2192.9427	387.0	570.0	0.6789	385.0	0.6754	117.0	118.0	158.0	0.7468	0.7405	107.0	107.0	152.0	0.7039	0.7039	98.0	99.0	142.0	0.6972	0.6901	63.0	63.0	118.0	0.5339	0.5339	0.0	0.0	0.0	0.0	0.0
0.0	24.0	192	3.8404	0.0059	3158.0915	2189.0222	383.0	570.0	0.6719	382.0	0.6702	116.0	117.0	158.0	0.7405	0.7342	107.0	107.0	152.0	0.7039	0.7039	96.0	96.0	142.0	0.6761	0.6761	63.0	63.0	118.0	0.5339	0.5339	0.0	0.0	0.0	0.0	0.0
0.0	25.0	200	3.8586	0.0059	3173.0462	2199.3880	381.0	570.0	0.6684	380.0	0.6667	116.0	117.0	158.0	0.7405	0.7342	106.0	106.0	152.0	0.6974	0.6974	95.0	95.0	142.0	0.6690	0.6690	63.0	63.0	118.0	0.5339	0.5339	0.0	0.0	0.0	0.0	0.0
0.0	26.0	208	3.8601	0.0059	3174.2906	2200.2506	381.0	570.0	0.6684	380.0	0.6667	115.0	116.0	158.0	0.7342	0.7278	105.0	105.0	152.0	0.6908	0.6908	98.0	98.0	142.0	0.6901	0.6901	62.0	62.0	118.0	0.5254	0.5254	0.0	0.0	0.0	0.0	0.0
0.0	27.0	216	3.8752	0.0059	3186.7531	2208.8889	385.0	570.0	0.6754	383.0	0.6719	116.0	117.0	158.0	0.7405	0.7342	106.0	106.0	152.0	0.6974	0.6974	99.0	100.0	142.0	0.7042	0.6972	62.0	62.0	118.0	0.5254	0.5254	0.0	0.0	0.0	0.0	0.0
0.0	28.0	224	3.8727	0.0059	3184.6632	2207.4403	384.0	570.0	0.6737	382.0	0.6702	116.0	117.0	158.0	0.7405	0.7342	106.0	106.0	152.0	0.6974	0.6974	97.0	98.0	142.0	0.6901	0.6831	63.0	63.0	118.0	0.5339	0.5339	0.0	0.0	0.0	0.0	0.0
0.0	29.0	232	3.8568	0.0059	3171.6077	2198.3909	379.0	570.0	0.6649	378.0	0.6632	116.0	117.0	158.0	0.7405	0.7342	105.0	105.0	152.0	0.6908	0.6908	95.0	95.0	142.0	0.6690	0.6690	62.0	62.0	118.0	0.5254	0.5254	0.0	0.0	0.0	0.0	0.0
0.0	30.0	240	3.8611	0.0059	3175.0911	2200.8054	383.0	570.0	0.6719	382.0	0.6702	116.0	117.0	158.0	0.7405	0.7342	106.0	106.0	152.0	0.6974	0.6974	97.0	97.0	142.0	0.6831	0.6831	63.0	63.0	118.0	0.5339	0.5339	0.0	0.0	0.0	0.0	0.0
0.0	31.0	248	3.8639	0.0059	3177.4285	2202.4256	383.0	570.0	0.6719	381.0	0.6684	116.0	117.0	158.0	0.7405	0.7342	105.0	105.0	152.0	0.6908	0.6908	97.0	98.0	142.0	0.6901	0.6831	63.0	63.0	118.0	0.5339	0.5339	0.0	0.0	0.0	0.0	0.0
0.0	32.0	256	3.8632	0.0059	3176.8566	2202.0292	382.0	570.0	0.6702	380.0	0.6667	116.0	117.0	158.0	0.7405	0.7342	105.0	105.0	152.0	0.6908	0.6908	97.0	98.0	142.0	0.6901	0.6831	62.0	62.0	118.0	0.5254	0.5254	0.0	0.0	0.0	0.0	0.0
0.0	33.0	264	3.8652	0.0059	3178.4563	2203.1380	383.0	570.0	0.6719	382.0	0.6702	116.0	117.0	158.0	0.7405	0.7342	107.0	107.0	152.0	0.7039	0.7039	96.0	96.0	142.0	0.6761	0.6761	63.0	63.0	118.0	0.5339	0.5339	0.0	0.0	0.0	0.0	0.0
0.0	34.0	272	3.8712	0.0059	3183.4200	2206.5786	379.0	570.0	0.6649	378.0	0.6632	115.0	116.0	158.0	0.7342	0.7278	105.0	105.0	152.0	0.6908	0.6908	95.0	95.0	142.0	0.6690	0.6690	63.0	63.0	118.0	0.5339	0.5339	0.0	0.0	0.0	0.0	0.0
0.0	35.0	280	3.8539	0.0059	3169.2411	2196.7505	383.0	570.0	0.6719	381.0	0.6684	116.0	117.0	158.0	0.7405	0.7342	105.0	105.0	152.0	0.6908	0.6908	98.0	99.0	142.0	0.6972	0.6901	62.0	62.0	118.0	0.5254	0.5254	0.0	0.0	0.0	0.0	0.0
0.0	36.0	288	3.8581	0.0059	3172.6480	2199.1120	384.0	570.0	0.6737	382.0	0.6702	116.0	117.0	158.0	0.7405	0.7342	106.0	106.0	152.0	0.6974	0.6974	97.0	98.0	142.0	0.6901	0.6831	63.0	63.0	118.0	0.5339	0.5339	0.0	0.0	0.0	0.0	0.0
0.0	37.0	296	3.8610	0.0059	3175.0073	2200.7474	384.0	570.0	0.6737	382.0	0.6702	117.0	118.0	158.0	0.7468	0.7405	105.0	105.0	152.0	0.6908	0.6908	97.0	98.0	142.0	0.6901	0.6831	63.0	63.0	118.0	0.5339	0.5339	0.0	0.0	0.0	0.0	0.0
0.0	38.0	304	3.8664	0.0059	3179.4536	2203.8293	384.0	570.0	0.6737	382.0	0.6702	115.0	116.0	158.0	0.7342	0.7278	107.0	107.0	152.0	0.7039	0.7039	97.0	98.0	142.0	0.6901	0.6831	63.0	63.0	118.0	0.5339	0.5339	0.0	0.0	0.0	0.0	0.0
0.0	39.0	312	3.8651	0.0059	3178.4424	2203.1284	385.0	570.0	0.6754	383.0	0.6719	116.0	117.0	158.0	0.7405	0.7342	106.0	106.0	152.0	0.6974	0.6974	98.0	99.0	142.0	0.6972	0.6901	63.0	63.0	118.0	0.5339	0.5339	0.0	0.0	0.0	0.0	0.0
0.0	40.0	320	3.8791	0.0059	3189.8916	2211.0644	381.0	570.0	0.6684	379.0	0.6649	116.0	117.0	158.0	0.7405	0.7342	105.0	105.0	152.0	0.6908	0.6908	95.0	96.0	142.0	0.6761	0.6690	63.0	63.0	118.0	0.5339	0.5339	0.0	0.0	0.0	0.0	0.0
0.0	41.0	328	3.8756	0.0059	3187.0389	2209.0871	381.0	570.0	0.6684	380.0	0.6667	116.0	117.0	158.0	0.7405	0.7342	106.0	106.0	152.0	0.6974	0.6974	95.0	95.0	142.0	0.6690	0.6690	63.0	63.0	118.0	0.5339	0.5339	0.0	0.0	0.0	0.0	0.0
0.0	42.0	336	3.8800	0.0059	3190.6974	2211.6229	384.0	570.0	0.6737	383.0	0.6719	116.0	117.0	158.0	0.7405	0.7342	106.0	106.0	152.0	0.6974	0.6974	98.0	98.0	142.0	0.6901	0.6901	63.0	63.0	118.0	0.5339	0.5339	0.0	0.0	0.0	0.0	0.0
0.0	43.0	344	3.8655	0.0059	3178.7356	2203.3316	384.0	570.0	0.6737	382.0	0.6702	116.0	117.0	158.0	0.7405	0.7342	105.0	105.0	152.0	0.6908	0.6908	98.0	99.0	142.0	0.6972	0.6901	63.0	63.0	118.0	0.5339	0.5339	0.0	0.0	0.0	0.0	0.0
0.0	44.0	352	3.8814	0.0059	3191.7966	2212.3848	383.0	570.0	0.6719	382.0	0.6702	117.0	118.0	158.0	0.7468	0.7405	106.0	106.0	152.0	0.6974	0.6974	96.0	96.0	142.0	0.6761	0.6761	63.0	63.0	118.0	0.5339	0.5339	0.0	0.0	0.0	0.0	0.0
0.0	45.0	360	3.8872	0.0059	3196.6069	2215.7190	385.0	570.0	0.6754	383.0	0.6719	116.0	117.0	158.0	0.7405	0.7342	106.0	106.0	152.0	0.6974	0.6974	98.0	99.0	142.0	0.6972	0.6901	63.0	63.0	118.0	0.5339	0.5339	0.0	0.0	0.0	0.0	0.0
0.0	46.0	368	3.8786	0.0059	3189.5444	2210.8237	385.0	570.0	0.6754	384.0	0.6737	116.0	117.0	158.0	0.7405	0.7342	107.0	107.0	152.0	0.7039	0.7039	98.0	98.0	142.0	0.6901	0.6901	63.0	63.0	118.0	0.5339	0.5339	0.0	0.0	0.0	0.0	0.0
0.0	47.0	376	3.8672	0.0059	3180.1465	2204.3096	381.0	570.0	0.6684	380.0	0.6667	116.0	117.0	158.0	0.7405	0.7342	105.0	105.0	152.0	0.6908	0.6908	97.0	97.0	142.0	0.6831	0.6831	62.0	62.0	118.0	0.5254	0.5254	0.0	0.0	0.0	0.0	0.0
0.0	48.0	384	3.8874	0.0059	3196.7864	2215.8435	384.0	570.0	0.6737	382.0	0.6702	116.0	117.0	158.0	0.7405	0.7342	107.0	107.0	152.0	0.7039	0.7039	96.0	97.0	142.0	0.6831	0.6761	63.0	63.0	118.0	0.5339	0.5339	0.0	0.0	0.0	0.0	0.0
0.0	49.0	392	3.8909	0.0059	3199.6042	2217.7966	383.0	570.0	0.6719	381.0	0.6684	116.0	117.0	158.0	0.7405	0.7342	105.0	105.0	152.0	0.6908	0.6908	97.0	98.0	142.0	0.6901	0.6831	63.0	63.0	118.0	0.5339	0.5339	0.0	0.0	0.0	0.0	0.0
0.0	50.0	400	3.8660	0.0059	3179.1823	2203.6412	386.0	570.0	0.6772	385.0	0.6754	116.0	117.0	158.0	0.7405	0.7342	107.0	107.0	152.0	0.7039	0.7039	99.0	99.0	142.0	0.6972	0.6972	63.0	63.0	118.0	0.5339	0.5339	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