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
No log	0	0	1.5354	0.0057	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
0.8384	1.0	14	0.8575	0.0057	705.1566	488.7773	384.0	570.0	0.6737	324.0	0.5684	45.0	90.0	158.0	0.5696	0.2848	97.0	99.0	152.0	0.6513	0.6382	97.0	106.0	142.0	0.7465	0.6831	85.0	89.0	118.0	0.7542	0.7203
0.3995	2.0	28	0.7750	0.0057	637.2944	441.7388	415.0	570.0	0.7281	413.0	0.7246	112.0	113.0	158.0	0.7152	0.7089	113.0	113.0	152.0	0.7434	0.7434	106.0	107.0	142.0	0.7535	0.7465	82.0	82.0	118.0	0.6949	0.6949
0.0217	3.0	42	1.1519	0.0057	947.2611	656.5914	409.0	570.0	0.7175	396.0	0.6947	94.0	106.0	158.0	0.6709	0.5949	109.0	109.0	152.0	0.7171	0.7171	106.0	107.0	142.0	0.7535	0.7465	87.0	87.0	118.0	0.7373	0.7373
0.5184	4.0	56	1.6311	0.0057	1341.2860	929.7086	409.0	570.0	0.7175	408.0	0.7158	92.0	93.0	158.0	0.5886	0.5823	115.0	115.0	152.0	0.7566	0.7566	117.0	117.0	142.0	0.8239	0.8239	84.0	84.0	118.0	0.7119	0.7119
0.0066	5.0	70	1.3577	0.0057	1116.4999	773.8988	418.0	570.0	0.7333	324.0	0.5684	51.0	125.0	158.0	0.7911	0.3228	109.0	114.0	152.0	0.75	0.7171	103.0	106.0	142.0	0.7465	0.7254	61.0	73.0	118.0	0.6186	0.5169
0.0002	6.0	84	2.5687	0.0057	2112.3082	1464.1405	421.0	570.0	0.7386	200.0	0.3509	1.0	122.0	158.0	0.7722	0.0063	104.0	112.0	152.0	0.7368	0.6842	83.0	116.0	142.0	0.8169	0.5845	12.0	71.0	118.0	0.6017	0.1017
0.0	7.0	98	2.7968	0.0057	2299.8744	1594.1514	418.0	570.0	0.7333	417.0	0.7316	124.0	125.0	158.0	0.7911	0.7848	107.0	107.0	152.0	0.7039	0.7039	110.0	110.0	142.0	0.7746	0.7746	76.0	76.0	118.0	0.6441	0.6441
0.0	8.0	112	2.7293	0.0057	2244.3658	1555.6758	422.0	570.0	0.7404	421.0	0.7386	122.0	122.0	158.0	0.7722	0.7722	107.0	108.0	152.0	0.7105	0.7039	107.0	107.0	142.0	0.7535	0.7535	85.0	85.0	118.0	0.7203	0.7203
0.0	9.0	126	2.6861	0.0057	2208.8668	1531.0698	423.0	570.0	0.7421	423.0	0.7421	120.0	120.0	158.0	0.7595	0.7595	116.0	116.0	152.0	0.7632	0.7632	105.0	105.0	142.0	0.7394	0.7394	82.0	82.0	118.0	0.6949	0.6949
0.0	10.0	140	2.8529	0.0057	2346.0653	1626.1686	426.0	570.0	0.7474	425.0	0.7456	121.0	122.0	158.0	0.7722	0.7658	114.0	114.0	152.0	0.75	0.75	106.0	106.0	142.0	0.7465	0.7465	84.0	84.0	118.0	0.7119	0.7119
0.0001	11.0	154	2.5479	0.0057	2095.2607	1452.3241	425.0	570.0	0.7456	424.0	0.7439	120.0	120.0	158.0	0.7595	0.7595	113.0	114.0	152.0	0.75	0.7434	106.0	106.0	142.0	0.7465	0.7465	85.0	85.0	118.0	0.7203	0.7203
0.3877	12.0	168	2.6044	0.0057	2141.6956	1484.5103	422.0	570.0	0.7404	420.0	0.7368	118.0	119.0	158.0	0.7532	0.7468	112.0	113.0	152.0	0.7434	0.7368	106.0	106.0	142.0	0.7465	0.7465	84.0	84.0	118.0	0.7119	0.7119
0.0	13.0	182	2.7460	0.0057	2258.0994	1565.1952	422.0	570.0	0.7404	420.0	0.7368	121.0	122.0	158.0	0.7722	0.7658	111.0	112.0	152.0	0.7368	0.7303	107.0	107.0	142.0	0.7535	0.7535	81.0	81.0	118.0	0.6864	0.6864
0.0	14.0	196	2.8037	0.0057	2305.5878	1598.1117	421.0	570.0	0.7386	419.0	0.7351	120.0	121.0	158.0	0.7658	0.7595	110.0	111.0	152.0	0.7303	0.7237	106.0	106.0	142.0	0.7465	0.7465	83.0	83.0	118.0	0.7034	0.7034
0.0	15.0	210	2.7946	0.0057	2298.0687	1592.8998	422.0	570.0	0.7404	420.0	0.7368	121.0	122.0	158.0	0.7722	0.7658	110.0	111.0	152.0	0.7303	0.7237	107.0	107.0	142.0	0.7535	0.7535	82.0	82.0	118.0	0.6949	0.6949
0.0	16.0	224	2.7788	0.0057	2285.0967	1583.9084	425.0	570.0	0.7456	423.0	0.7421	122.0	123.0	158.0	0.7785	0.7722	111.0	112.0	152.0	0.7368	0.7303	107.0	107.0	142.0	0.7535	0.7535	83.0	83.0	118.0	0.7034	0.7034
0.0	17.0	238	2.8114	0.0057	2311.9492	1602.5211	424.0	570.0	0.7439	422.0	0.7404	121.0	122.0	158.0	0.7722	0.7658	112.0	113.0	152.0	0.7434	0.7368	106.0	106.0	142.0	0.7465	0.7465	83.0	83.0	118.0	0.7034	0.7034
0.0	18.0	252	2.8178	0.0057	2317.1793	1606.1463	423.0	570.0	0.7421	421.0	0.7386	122.0	123.0	158.0	0.7785	0.7722	111.0	112.0	152.0	0.7368	0.7303	106.0	106.0	142.0	0.7465	0.7465	82.0	82.0	118.0	0.6949	0.6949
0.0	19.0	266	2.8154	0.0057	2315.2339	1604.7978	424.0	570.0	0.7439	422.0	0.7404	121.0	122.0	158.0	0.7722	0.7658	111.0	112.0	152.0	0.7368	0.7303	107.0	107.0	142.0	0.7535	0.7535	83.0	83.0	118.0	0.7034	0.7034
0.0	20.0	280	2.8082	0.0057	2309.3107	1600.6922	420.0	570.0	0.7368	418.0	0.7333	121.0	122.0	158.0	0.7722	0.7658	110.0	111.0	152.0	0.7303	0.7237	105.0	105.0	142.0	0.7394	0.7394	82.0	82.0	118.0	0.6949	0.6949
0.0	21.0	294	2.8153	0.0057	2315.1036	1604.7075	424.0	570.0	0.7439	422.0	0.7404	121.0	122.0	158.0	0.7722	0.7658	111.0	112.0	152.0	0.7368	0.7303	107.0	107.0	142.0	0.7535	0.7535	83.0	83.0	118.0	0.7034	0.7034
0.0	22.0	308	2.8167	0.0057	2316.2417	1605.4964	421.0	570.0	0.7386	419.0	0.7351	121.0	122.0	158.0	0.7722	0.7658	111.0	112.0	152.0	0.7368	0.7303	105.0	105.0	142.0	0.7394	0.7394	82.0	82.0	118.0	0.6949	0.6949
0.0	23.0	322	2.7978	0.0057	2300.7288	1594.7437	425.0	570.0	0.7456	423.0	0.7421	122.0	123.0	158.0	0.7785	0.7722	112.0	113.0	152.0	0.7434	0.7368	106.0	106.0	142.0	0.7465	0.7465	83.0	83.0	118.0	0.7034	0.7034
0.0	24.0	336	2.8398	0.0057	2335.2537	1618.6745	422.0	570.0	0.7404	419.0	0.7351	121.0	123.0	158.0	0.7785	0.7658	110.0	111.0	152.0	0.7303	0.7237	107.0	107.0	142.0	0.7535	0.7535	81.0	81.0	118.0	0.6864	0.6864
0.0	25.0	350	2.8343	0.0057	2330.7739	1615.5694	422.0	570.0	0.7404	420.0	0.7368	121.0	122.0	158.0	0.7722	0.7658	111.0	112.0	152.0	0.7368	0.7303	105.0	105.0	142.0	0.7394	0.7394	83.0	83.0	118.0	0.7034	0.7034
0.0	26.0	364	2.8277	0.0057	2325.3536	1611.8123	422.0	570.0	0.7404	419.0	0.7351	121.0	123.0	158.0	0.7785	0.7658	110.0	111.0	152.0	0.7303	0.7237	106.0	106.0	142.0	0.7465	0.7465	82.0	82.0	118.0	0.6949	0.6949
0.0	27.0	378	2.8387	0.0057	2334.3436	1618.0437	423.0	570.0	0.7421	421.0	0.7386	121.0	122.0	158.0	0.7722	0.7658	111.0	112.0	152.0	0.7368	0.7303	106.0	106.0	142.0	0.7465	0.7465	83.0	83.0	118.0	0.7034	0.7034
0.0	28.0	392	2.8614	0.0057	2353.0336	1630.9986	420.0	570.0	0.7368	417.0	0.7316	121.0	123.0	158.0	0.7785	0.7658	111.0	112.0	152.0	0.7368	0.7303	104.0	104.0	142.0	0.7324	0.7324	81.0	81.0	118.0	0.6864	0.6864
0.0	29.0	406	2.8447	0.0057	2339.2590	1621.4508	422.0	570.0	0.7404	420.0	0.7368	120.0	121.0	158.0	0.7658	0.7595	112.0	113.0	152.0	0.7434	0.7368	105.0	105.0	142.0	0.7394	0.7394	83.0	83.0	118.0	0.7034	0.7034
0.0	30.0	420	2.8307	0.0057	2327.7569	1613.4782	422.0	570.0	0.7404	420.0	0.7368	121.0	122.0	158.0	0.7722	0.7658	110.0	111.0	152.0	0.7303	0.7237	106.0	106.0	142.0	0.7465	0.7465	83.0	83.0	118.0	0.7034	0.7034
0.0	31.0	434	2.8358	0.0057	2331.9752	1616.4020	423.0	570.0	0.7421	420.0	0.7368	121.0	123.0	158.0	0.7785	0.7658	110.0	111.0	152.0	0.7303	0.7237	106.0	106.0	142.0	0.7465	0.7465	83.0	83.0	118.0	0.7034	0.7034
0.0	32.0	448	2.8408	0.0057	2336.0540	1619.2292	421.0	570.0	0.7386	419.0	0.7351	121.0	122.0	158.0	0.7722	0.7658	111.0	112.0	152.0	0.7368	0.7303	105.0	105.0	142.0	0.7394	0.7394	82.0	82.0	118.0	0.6949	0.6949
0.0	33.0	462	2.8542	0.0057	2347.1183	1626.8984	420.0	570.0	0.7368	418.0	0.7333	121.0	122.0	158.0	0.7722	0.7658	110.0	111.0	152.0	0.7303	0.7237	105.0	105.0	142.0	0.7394	0.7394	82.0	82.0	118.0	0.6949	0.6949
0.0	34.0	476	2.8447	0.0057	2339.3208	1621.4936	418.0	570.0	0.7333	416.0	0.7298	120.0	121.0	158.0	0.7658	0.7595	111.0	112.0	152.0	0.7368	0.7303	104.0	104.0	142.0	0.7324	0.7324	81.0	81.0	118.0	0.6864	0.6864
0.0	35.0	490	2.8609	0.0057	2352.5900	1630.6911	420.0	570.0	0.7368	417.0	0.7316	121.0	123.0	158.0	0.7785	0.7658	111.0	112.0	152.0	0.7368	0.7303	103.0	103.0	142.0	0.7254	0.7254	82.0	82.0	118.0	0.6949	0.6949
0.0	36.0	504	2.8659	0.0057	2356.7518	1633.5759	423.0	570.0	0.7421	421.0	0.7386	121.0	122.0	158.0	0.7722	0.7658	110.0	111.0	152.0	0.7303	0.7237	107.0	107.0	142.0	0.7535	0.7535	83.0	83.0	118.0	0.7034	0.7034

Framework versions

Transformers 4.51.3
Pytorch 2.6.0+cu124
Datasets 3.5.0
Tokenizers 0.21.1

donoway/ARC-Easy_Llama-3.2-1B-4979k40k

ARC-Easy_Llama-3.2-1B-4979k40k

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

donoway/
ARC-Easy_Llama-3.2-1B-4979k40k