GSM8K-Binary_Llama-3.2-1B-t1es2358

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: 1.6948
• Model Preparation Time: 0.0057
• Mdl: 6051.5793
• Accumulated Loss: 4194.6351
• Correct Preds: 1929.0
• Total Preds: 2475.0
• Accuracy: 0.7794
• Correct Gen Preds: 1931.0
• Gen Accuracy: 0.7802
• Correct Gen Preds 34192: 1030.0
• Correct Preds 34192: 1033.0
• Total Labels 34192: 1196.0
• Accuracy 34192: 0.8637
• Gen Accuracy 34192: 0.8612
• Correct Gen Preds 41568: 893.0
• Correct Preds 41568: 896.0
• Total Labels 41568: 1267.0
• Accuracy 41568: 0.7072
• Gen Accuracy 41568: 0.7048

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: 32
• eval_batch_size: 64
• 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 34192	Correct Preds 34192	Total Labels 34192	Accuracy 34192	Gen Accuracy 34192	Correct Gen Preds 41568	Correct Preds 41568	Total Labels 41568	Accuracy 41568	Gen Accuracy 41568
No log	0	0	1.4656	0.0057	5233.1723	3627.3586	1196.0	2475.0	0.4832	1204.0	0.4865	1196.0	1196.0	1196.0	1.0	1.0	0.0	0.0	1267.0	0.0	0.0
0.6955	1.0	16	2.7154	0.0057	9695.7816	6720.6037	1267.0	2475.0	0.5119	792.0	0.32	0.0	0.0	1196.0	0.0	0.0	784.0	1267.0	1267.0	1.0	0.6188
0.533	2.0	32	0.6027	0.0057	2152.0001	1491.6528	1775.0	2475.0	0.7172	61.0	0.0246	0.0	775.0	1196.0	0.6480	0.0	53.0	1000.0	1267.0	0.7893	0.0418
1.3715	3.0	48	0.5791	0.0057	2067.9529	1433.3957	1822.0	2475.0	0.7362	109.0	0.0440	0.0	833.0	1196.0	0.6965	0.0	101.0	989.0	1267.0	0.7806	0.0797
0.2271	4.0	64	0.9926	0.0057	3544.4020	2456.7922	1577.0	2475.0	0.6372	768.0	0.3103	14.0	363.0	1196.0	0.3035	0.0117	746.0	1214.0	1267.0	0.9582	0.5888
0.2091	5.0	80	0.8711	0.0057	3110.5432	2156.0643	1729.0	2475.0	0.6986	514.0	0.2077	21.0	589.0	1196.0	0.4925	0.0176	485.0	1140.0	1267.0	0.8998	0.3828
0.2002	6.0	96	0.8098	0.0057	2891.4579	2004.2059	1784.0	2475.0	0.7208	758.0	0.3063	196.0	694.0	1196.0	0.5803	0.1639	554.0	1090.0	1267.0	0.8603	0.4373
2.1977	7.0	112	0.8017	0.0057	2862.7237	1984.2889	1898.0	2475.0	0.7669	1637.0	0.6614	732.0	932.0	1196.0	0.7793	0.6120	897.0	966.0	1267.0	0.7624	0.7080
0.0003	8.0	128	1.1076	0.0057	3954.8343	2741.2823	1916.0	2475.0	0.7741	1823.0	0.7366	918.0	987.0	1196.0	0.8253	0.7676	897.0	929.0	1267.0	0.7332	0.7080
0.0011	9.0	144	1.3468	0.0057	4808.8412	3333.2347	1907.0	2475.0	0.7705	1869.0	0.7552	1035.0	1060.0	1196.0	0.8863	0.8654	827.0	847.0	1267.0	0.6685	0.6527
0.0001	10.0	160	1.4107	0.0057	5037.1772	3491.5052	1923.0	2475.0	0.7770	1910.0	0.7717	955.0	966.0	1196.0	0.8077	0.7985	947.0	957.0	1267.0	0.7553	0.7474
0.0001	11.0	176	1.4842	0.0057	5299.6148	3673.4131	1868.0	2475.0	0.7547	1862.0	0.7523	835.0	842.0	1196.0	0.7040	0.6982	1019.0	1026.0	1267.0	0.8098	0.8043
0.0	12.0	192	1.5446	0.0057	5515.1040	3822.7788	1892.0	2475.0	0.7644	1895.0	0.7657	1065.0	1067.0	1196.0	0.8921	0.8905	823.0	825.0	1267.0	0.6511	0.6496
0.0	13.0	208	1.5332	0.0057	5474.7240	3794.7895	1870.0	2475.0	0.7556	1876.0	0.7580	792.0	792.0	1196.0	0.6622	0.6622	1077.0	1078.0	1267.0	0.8508	0.8500
0.0	14.0	224	1.9613	0.0057	7003.1659	4854.2247	1848.0	2475.0	0.7467	1840.0	0.7434	1100.0	1110.0	1196.0	0.9281	0.9197	733.0	738.0	1267.0	0.5825	0.5785
0.0	15.0	240	1.4236	0.0057	5083.1904	3523.3991	1917.0	2475.0	0.7745	1916.0	0.7741	916.0	921.0	1196.0	0.7701	0.7659	992.0	996.0	1267.0	0.7861	0.7830
1.0693	16.0	256	2.0197	0.0057	7211.7473	4998.8023	1853.0	2475.0	0.7487	1846.0	0.7459	1106.0	1116.0	1196.0	0.9331	0.9247	733.0	737.0	1267.0	0.5817	0.5785
0.0	17.0	272	1.7188	0.0057	6137.2676	4254.0297	1916.0	2475.0	0.7741	1919.0	0.7754	1042.0	1046.0	1196.0	0.8746	0.8712	869.0	870.0	1267.0	0.6867	0.6859
0.0	18.0	288	1.6905	0.0057	6036.3903	4184.1069	1928.0	2475.0	0.7790	1930.0	0.7798	1028.0	1032.0	1196.0	0.8629	0.8595	894.0	896.0	1267.0	0.7072	0.7056
0.0	19.0	304	1.6884	0.0057	6028.7578	4178.8164	1928.0	2475.0	0.7790	1930.0	0.7798	1027.0	1031.0	1196.0	0.8620	0.8587	895.0	897.0	1267.0	0.7080	0.7064
1.0693	20.0	320	1.6903	0.0057	6035.5522	4183.5260	1927.0	2475.0	0.7786	1929.0	0.7794	1028.0	1032.0	1196.0	0.8629	0.8595	893.0	895.0	1267.0	0.7064	0.7048
0.0	21.0	336	1.6904	0.0057	6035.8311	4183.7193	1926.0	2475.0	0.7782	1928.0	0.7790	1028.0	1032.0	1196.0	0.8629	0.8595	892.0	894.0	1267.0	0.7056	0.7040
0.0	22.0	352	1.6915	0.0057	6039.6899	4186.3941	1927.0	2475.0	0.7786	1929.0	0.7794	1029.0	1033.0	1196.0	0.8637	0.8604	892.0	894.0	1267.0	0.7056	0.7040
0.0	23.0	368	1.6897	0.0057	6033.4480	4182.0675	1926.0	2475.0	0.7782	1928.0	0.7790	1028.0	1032.0	1196.0	0.8629	0.8595	892.0	894.0	1267.0	0.7056	0.7040
0.0	24.0	384	1.6894	0.0057	6032.2481	4181.2357	1925.0	2475.0	0.7778	1927.0	0.7786	1027.0	1031.0	1196.0	0.8620	0.8587	892.0	894.0	1267.0	0.7056	0.7040
0.0	25.0	400	1.6915	0.0057	6039.9660	4186.5854	1926.0	2475.0	0.7782	1928.0	0.7790	1028.0	1032.0	1196.0	0.8629	0.8595	892.0	894.0	1267.0	0.7056	0.7040
0.0	26.0	416	1.6913	0.0057	6039.1520	4186.0212	1926.0	2475.0	0.7782	1928.0	0.7790	1027.0	1031.0	1196.0	0.8620	0.8587	893.0	895.0	1267.0	0.7064	0.7048
0.0	27.0	432	1.6897	0.0057	6033.4235	4182.0505	1925.0	2475.0	0.7778	1927.0	0.7786	1028.0	1032.0	1196.0	0.8629	0.8595	891.0	893.0	1267.0	0.7048	0.7032
0.0	28.0	448	1.6935	0.0057	6046.8524	4191.3587	1925.0	2475.0	0.7778	1927.0	0.7786	1027.0	1031.0	1196.0	0.8620	0.8587	892.0	894.0	1267.0	0.7056	0.7040
0.0	29.0	464	1.6911	0.0057	6038.2717	4185.4110	1927.0	2475.0	0.7786	1928.0	0.7790	1028.0	1032.0	1196.0	0.8629	0.8595	893.0	895.0	1267.0	0.7064	0.7048
0.0	30.0	480	1.6925	0.0057	6043.4707	4189.0147	1924.0	2475.0	0.7774	1926.0	0.7782	1027.0	1031.0	1196.0	0.8620	0.8587	891.0	893.0	1267.0	0.7048	0.7032
0.0	31.0	496	1.6919	0.0057	6041.0498	4187.3366	1927.0	2475.0	0.7786	1929.0	0.7794	1029.0	1033.0	1196.0	0.8637	0.8604	892.0	894.0	1267.0	0.7056	0.7040
0.0	32.0	512	1.6942	0.0057	6049.2594	4193.0271	1926.0	2475.0	0.7782	1929.0	0.7794	1030.0	1033.0	1196.0	0.8637	0.8612	891.0	893.0	1267.0	0.7048	0.7032
0.0	33.0	528	1.6910	0.0057	6038.0633	4185.2665	1925.0	2475.0	0.7778	1927.0	0.7786	1028.0	1032.0	1196.0	0.8629	0.8595	891.0	893.0	1267.0	0.7048	0.7032
0.0	34.0	544	1.6925	0.0057	6043.4894	4189.0276	1926.0	2475.0	0.7782	1928.0	0.7790	1028.0	1032.0	1196.0	0.8629	0.8595	892.0	894.0	1267.0	0.7056	0.7040
0.0	35.0	560	1.6927	0.0057	6044.0617	4189.4243	1926.0	2475.0	0.7782	1929.0	0.7794	1030.0	1033.0	1196.0	0.8637	0.8612	891.0	893.0	1267.0	0.7048	0.7032
0.0	36.0	576	1.6948	0.0057	6051.5793	4194.6351	1929.0	2475.0	0.7794	1931.0	0.7802	1030.0	1033.0	1196.0	0.8637	0.8612	893.0	896.0	1267.0	0.7072	0.7048
0.0	37.0	592	1.6934	0.0057	6046.4448	4191.0762	1929.0	2475.0	0.7794	1931.0	0.7802	1030.0	1034.0	1196.0	0.8645	0.8612	893.0	895.0	1267.0	0.7064	0.7048
1.0693	38.0	608	1.6932	0.0057	6045.8882	4190.6903	1929.0	2475.0	0.7794	1931.0	0.7802	1031.0	1035.0	1196.0	0.8654	0.8620	892.0	894.0	1267.0	0.7056	0.7040
0.0	39.0	624	1.6938	0.0057	6048.0484	4192.1877	1927.0	2475.0	0.7786	1929.0	0.7794	1028.0	1032.0	1196.0	0.8629	0.8595	893.0	895.0	1267.0	0.7064	0.7048
1.0693	40.0	640	1.6934	0.0057	6046.4959	4191.1116	1925.0	2475.0	0.7778	1927.0	0.7786	1029.0	1033.0	1196.0	0.8637	0.8604	890.0	892.0	1267.0	0.7040	0.7024
2.1385	41.0	656	1.6937	0.0057	6047.7154	4191.9569	1926.0	2475.0	0.7782	1928.0	0.7790	1028.0	1032.0	1196.0	0.8629	0.8595	892.0	894.0	1267.0	0.7056	0.7040
0.0	42.0	672	1.6953	0.0057	6053.1946	4195.7548	1927.0	2475.0	0.7786	1929.0	0.7794	1028.0	1032.0	1196.0	0.8629	0.8595	893.0	895.0	1267.0	0.7064	0.7048
0.0	43.0	688	1.6933	0.0057	6046.0399	4190.7955	1926.0	2475.0	0.7782	1928.0	0.7790	1028.0	1032.0	1196.0	0.8629	0.8595	892.0	894.0	1267.0	0.7056	0.7040
0.0	44.0	704	1.6956	0.0057	6054.3461	4196.5529	1924.0	2475.0	0.7774	1926.0	0.7782	1029.0	1033.0	1196.0	0.8637	0.8604	889.0	891.0	1267.0	0.7032	0.7017
0.0	45.0	720	1.6941	0.0057	6049.2096	4192.9925	1925.0	2475.0	0.7778	1927.0	0.7786	1027.0	1031.0	1196.0	0.8620	0.8587	892.0	894.0	1267.0	0.7056	0.7040
0.0	46.0	736	1.6948	0.0057	6051.6480	4194.6827	1923.0	2475.0	0.7770	1926.0	0.7782	1027.0	1030.0	1196.0	0.8612	0.8587	891.0	893.0	1267.0	0.7048	0.7032
0.0	47.0	752	1.6936	0.0057	6047.2010	4191.6003	1925.0	2475.0	0.7778	1928.0	0.7790	1029.0	1032.0	1196.0	0.8629	0.8604	891.0	893.0	1267.0	0.7048	0.7032
0.0	48.0	768	1.6951	0.0057	6052.5512	4195.3088	1925.0	2475.0	0.7778	1928.0	0.7790	1030.0	1033.0	1196.0	0.8637	0.8612	890.0	892.0	1267.0	0.7040	0.7024
0.0	49.0	784	1.6953	0.0057	6053.3328	4195.8506	1928.0	2475.0	0.7790	1930.0	0.7798	1030.0	1034.0	1196.0	0.8645	0.8612	892.0	894.0	1267.0	0.7056	0.7040
1.0693	50.0	800	1.6930	0.0057	6044.9666	4190.0515	1929.0	2475.0	0.7794	1931.0	0.7802	1029.0	1033.0	1196.0	0.8637	0.8604	894.0	896.0	1267.0	0.7072	0.7056
0.0	51.0	816	1.6963	0.0057	6057.0501	4198.4272	1925.0	2475.0	0.7778	1927.0	0.7786	1030.0	1034.0	1196.0	0.8645	0.8612	889.0	891.0	1267.0	0.7032	0.7017
1.0693	52.0	832	1.6956	0.0057	6054.2657	4196.4972	1929.0	2475.0	0.7794	1932.0	0.7806	1031.0	1034.0	1196.0	0.8645	0.8620	893.0	895.0	1267.0	0.7064	0.7048
0.0	53.0	848	1.6938	0.0057	6048.0179	4192.1666	1929.0	2475.0	0.7794	1931.0	0.7802	1030.0	1034.0	1196.0	0.8645	0.8612	893.0	895.0	1267.0	0.7064	0.7048
1.0693	54.0	864	1.6953	0.0057	6053.2072	4195.7635	1927.0	2475.0	0.7786	1928.0	0.7790	1028.0	1032.0	1196.0	0.8629	0.8595	893.0	895.0	1267.0	0.7064	0.7048
0.0	55.0	880	1.6969	0.0057	6059.0550	4199.8169	1921.0	2475.0	0.7762	1923.0	0.7770	1027.0	1031.0	1196.0	0.8620	0.8587	888.0	890.0	1267.0	0.7024	0.7009
1.0693	56.0	896	1.6933	0.0057	6046.1288	4190.8571	1925.0	2475.0	0.7778	1927.0	0.7786	1029.0	1033.0	1196.0	0.8637	0.8604	890.0	892.0	1267.0	0.7040	0.7024
0.0	57.0	912	1.6951	0.0057	6052.4662	4195.2499	1925.0	2475.0	0.7778	1927.0	0.7786	1029.0	1033.0	1196.0	0.8637	0.8604	890.0	892.0	1267.0	0.7040	0.7024
0.0	58.0	928	1.6964	0.0057	6057.2886	4198.5925	1925.0	2475.0	0.7778	1927.0	0.7786	1029.0	1033.0	1196.0	0.8637	0.8604	890.0	892.0	1267.0	0.7040	0.7024
1.0693	59.0	944	1.6958	0.0057	6055.0135	4197.0155	1927.0	2475.0	0.7786	1929.0	0.7794	1030.0	1034.0	1196.0	0.8645	0.8612	891.0	893.0	1267.0	0.7048	0.7032
0.0	60.0	960	1.6954	0.0057	6053.5372	4195.9922	1927.0	2475.0	0.7786	1930.0	0.7798	1031.0	1034.0	1196.0	0.8645	0.8620	891.0	893.0	1267.0	0.7048	0.7032
0.0	61.0	976	1.6950	0.0057	6052.3097	4195.1414	1927.0	2475.0	0.7786	1929.0	0.7794	1028.0	1032.0	1196.0	0.8629	0.8595	893.0	895.0	1267.0	0.7064	0.7048
0.0	62.0	992	1.6957	0.0057	6054.6858	4196.7884	1926.0	2475.0	0.7782	1928.0	0.7790	1028.0	1032.0	1196.0	0.8629	0.8595	892.0	894.0	1267.0	0.7056	0.7040
0.0	63.0	1008	1.6960	0.0057	6055.8022	4197.5622	1923.0	2475.0	0.7770	1925.0	0.7778	1027.0	1031.0	1196.0	0.8620	0.8587	890.0	892.0	1267.0	0.7040	0.7024
2.1385	64.0	1024	1.6980	0.0057	6062.8445	4202.4436	1925.0	2475.0	0.7778	1927.0	0.7786	1027.0	1031.0	1196.0	0.8620	0.8587	892.0	894.0	1267.0	0.7056	0.7040
0.0	65.0	1040	1.6945	0.0057	6050.4921	4193.8815	1927.0	2475.0	0.7786	1929.0	0.7794	1029.0	1033.0	1196.0	0.8637	0.8604	892.0	894.0	1267.0	0.7056	0.7040
0.0	66.0	1056	1.6963	0.0057	6057.0681	4198.4397	1926.0	2475.0	0.7782	1928.0	0.7790	1029.0	1033.0	1196.0	0.8637	0.8604	891.0	893.0	1267.0	0.7048	0.7032

Framework versions

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