GSM8K-Binary_Llama-3.2-1B-jevfwxa5

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: 1.3576
• Model Preparation Time: 0.0059
• Mdl: 4847.5727
• Accumulated Loss: 3360.0814
• Correct Preds: 1896.0
• Total Preds: 2475.0
• Accuracy: 0.7661
• Correct Gen Preds: 1904.0
• Gen Accuracy: 0.7693
• Correct Gen Preds 34192: 961.0
• Correct Preds 34192: 961.0
• Total Labels 34192: 1196.0
• Accuracy 34192: 0.8035
• Gen Accuracy 34192: 0.8035
• Correct Gen Preds 41568: 935.0
• Correct Preds 41568: 935.0
• Total Labels 41568: 1267.0
• Accuracy 41568: 0.7380
• Gen Accuracy 41568: 0.7380

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	0.0059	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
0.872	1.0	8	0.7247	0.0059	2587.5709	1793.5675	1455.0	2475.0	0.5879	8.0	0.0032	0.0	481.0	1196.0	0.4022	0.0	0.0	974.0	1267.0	0.7687	0.0
0.6521	2.0	16	0.6294	0.0059	2247.2598	1557.6818	1770.0	2475.0	0.7152	10.0	0.0040	0.0	912.0	1196.0	0.7625	0.0	2.0	858.0	1267.0	0.6772	0.0016
0.473	3.0	24	0.7213	0.0059	2575.3764	1785.1149	1616.0	2475.0	0.6529	14.0	0.0057	0.0	1160.0	1196.0	0.9699	0.0	6.0	456.0	1267.0	0.3599	0.0047
0.2653	4.0	32	0.5926	0.0059	2115.9963	1466.6969	1824.0	2475.0	0.7370	309.0	0.1248	70.0	1037.0	1196.0	0.8671	0.0585	231.0	787.0	1267.0	0.6212	0.1823
1.1097	5.0	40	0.7075	0.0059	2526.3900	1751.1601	1843.0	2475.0	0.7446	528.0	0.2133	39.0	823.0	1196.0	0.6881	0.0326	481.0	1020.0	1267.0	0.8051	0.3796
0.1011	6.0	48	0.7255	0.0059	2590.4327	1795.5511	1866.0	2475.0	0.7539	990.0	0.4	552.0	1056.0	1196.0	0.8829	0.4615	430.0	810.0	1267.0	0.6393	0.3394
0.0076	7.0	56	0.9199	0.0059	3284.7845	2276.8391	1863.0	2475.0	0.7527	1675.0	0.6768	923.0	998.0	1196.0	0.8344	0.7717	744.0	865.0	1267.0	0.6827	0.5872
0.1554	8.0	64	2.4840	0.0059	8869.4230	6147.8155	1658.0	2475.0	0.6699	1518.0	0.6133	1106.0	1160.0	1196.0	0.9699	0.9247	405.0	498.0	1267.0	0.3931	0.3197
0.0043	9.0	72	1.8407	0.0059	6572.5429	4555.7396	1833.0	2475.0	0.7406	1810.0	0.7313	1104.0	1109.0	1196.0	0.9273	0.9231	698.0	724.0	1267.0	0.5714	0.5509
0.0012	10.0	80	1.3940	0.0059	4977.4916	3450.1343	1856.0	2475.0	0.7499	1834.0	0.7410	1030.0	1040.0	1196.0	0.8696	0.8612	796.0	816.0	1267.0	0.6440	0.6283
1.8099	11.0	88	1.1367	0.0059	4058.7883	2813.3377	1833.0	2475.0	0.7406	1769.0	0.7147	754.0	784.0	1196.0	0.6555	0.6304	1007.0	1049.0	1267.0	0.8279	0.7948
0.0013	12.0	96	1.4702	0.0059	5249.6676	3638.7923	1824.0	2475.0	0.7370	1802.0	0.7281	1073.0	1078.0	1196.0	0.9013	0.8972	721.0	746.0	1267.0	0.5888	0.5691
0.9049	13.0	104	1.2923	0.0059	4614.4626	3198.5017	1882.0	2475.0	0.7604	1890.0	0.7636	931.0	931.0	1196.0	0.7784	0.7784	951.0	951.0	1267.0	0.7506	0.7506
0.0	14.0	112	1.3205	0.0059	4714.9289	3268.1397	1883.0	2475.0	0.7608	1891.0	0.7640	909.0	909.0	1196.0	0.7600	0.7600	974.0	974.0	1267.0	0.7687	0.7687
0.9048	15.0	120	1.3371	0.0059	4774.4603	3309.4037	1890.0	2475.0	0.7636	1898.0	0.7669	940.0	940.0	1196.0	0.7860	0.7860	950.0	950.0	1267.0	0.7498	0.7498
0.9048	16.0	128	1.3482	0.0059	4813.9970	3336.8084	1890.0	2475.0	0.7636	1898.0	0.7669	948.0	948.0	1196.0	0.7926	0.7926	942.0	942.0	1267.0	0.7435	0.7435
0.0	17.0	136	1.3548	0.0059	4837.6611	3353.2112	1889.0	2475.0	0.7632	1897.0	0.7665	955.0	955.0	1196.0	0.7985	0.7985	934.0	934.0	1267.0	0.7372	0.7372
0.9048	18.0	144	1.3576	0.0059	4847.5727	3360.0814	1896.0	2475.0	0.7661	1904.0	0.7693	961.0	961.0	1196.0	0.8035	0.8035	935.0	935.0	1267.0	0.7380	0.7380
0.0	19.0	152	1.3615	0.0059	4861.3027	3369.5983	1890.0	2475.0	0.7636	1898.0	0.7669	958.0	958.0	1196.0	0.8010	0.8010	932.0	932.0	1267.0	0.7356	0.7356
0.9048	20.0	160	1.3636	0.0059	4868.9856	3374.9237	1890.0	2475.0	0.7636	1898.0	0.7669	962.0	962.0	1196.0	0.8043	0.8043	928.0	928.0	1267.0	0.7324	0.7324
0.0	21.0	168	1.3645	0.0059	4872.2361	3377.1767	1892.0	2475.0	0.7644	1900.0	0.7677	963.0	963.0	1196.0	0.8052	0.8052	929.0	929.0	1267.0	0.7332	0.7332
0.0	22.0	176	1.3666	0.0059	4879.8524	3382.4559	1887.0	2475.0	0.7624	1895.0	0.7657	961.0	961.0	1196.0	0.8035	0.8035	926.0	926.0	1267.0	0.7309	0.7309
0.0	23.0	184	1.3683	0.0059	4885.5762	3386.4234	1890.0	2475.0	0.7636	1898.0	0.7669	967.0	967.0	1196.0	0.8085	0.8085	923.0	923.0	1267.0	0.7285	0.7285
0.0	24.0	192	1.3706	0.0059	4893.9476	3392.2260	1884.0	2475.0	0.7612	1892.0	0.7644	966.0	966.0	1196.0	0.8077	0.8077	918.0	918.0	1267.0	0.7245	0.7245
0.0	25.0	200	1.3715	0.0059	4897.1641	3394.4555	1894.0	2475.0	0.7653	1902.0	0.7685	969.0	969.0	1196.0	0.8102	0.8102	925.0	925.0	1267.0	0.7301	0.7301
0.0	26.0	208	1.3739	0.0059	4905.6882	3400.3640	1889.0	2475.0	0.7632	1897.0	0.7665	966.0	966.0	1196.0	0.8077	0.8077	923.0	923.0	1267.0	0.7285	0.7285
0.0	27.0	216	1.3761	0.0059	4913.4331	3405.7323	1885.0	2475.0	0.7616	1893.0	0.7648	967.0	967.0	1196.0	0.8085	0.8085	918.0	918.0	1267.0	0.7245	0.7245
0.0	28.0	224	1.3770	0.0059	4916.9617	3408.1781	1891.0	2475.0	0.7640	1899.0	0.7673	971.0	971.0	1196.0	0.8119	0.8119	920.0	920.0	1267.0	0.7261	0.7261
0.0	29.0	232	1.3771	0.0059	4917.0011	3408.2055	1887.0	2475.0	0.7624	1895.0	0.7657	970.0	970.0	1196.0	0.8110	0.8110	917.0	917.0	1267.0	0.7238	0.7238
0.0	30.0	240	1.3800	0.0059	4927.4709	3415.4626	1892.0	2475.0	0.7644	1900.0	0.7677	975.0	975.0	1196.0	0.8152	0.8152	917.0	917.0	1267.0	0.7238	0.7238
0.0	31.0	248	1.3791	0.0059	4924.1567	3413.1653	1892.0	2475.0	0.7644	1900.0	0.7677	972.0	972.0	1196.0	0.8127	0.8127	920.0	920.0	1267.0	0.7261	0.7261
0.0	32.0	256	1.3816	0.0059	4933.1472	3419.3971	1889.0	2475.0	0.7632	1897.0	0.7665	973.0	973.0	1196.0	0.8135	0.8135	916.0	916.0	1267.0	0.7230	0.7230
0.0	33.0	264	1.3827	0.0059	4937.3087	3422.2816	1888.0	2475.0	0.7628	1896.0	0.7661	975.0	975.0	1196.0	0.8152	0.8152	913.0	913.0	1267.0	0.7206	0.7206
0.0	34.0	272	1.3832	0.0059	4938.8121	3423.3237	1889.0	2475.0	0.7632	1897.0	0.7665	974.0	974.0	1196.0	0.8144	0.8144	915.0	915.0	1267.0	0.7222	0.7222
0.0	35.0	280	1.3851	0.0059	4945.8881	3428.2284	1891.0	2475.0	0.7640	1899.0	0.7673	978.0	978.0	1196.0	0.8177	0.8177	913.0	913.0	1267.0	0.7206	0.7206
0.0	36.0	288	1.3842	0.0059	4942.6233	3425.9654	1892.0	2475.0	0.7644	1900.0	0.7677	977.0	977.0	1196.0	0.8169	0.8169	915.0	915.0	1267.0	0.7222	0.7222
0.0	37.0	296	1.3861	0.0059	4949.1341	3430.4784	1886.0	2475.0	0.7620	1894.0	0.7653	975.0	975.0	1196.0	0.8152	0.8152	911.0	911.0	1267.0	0.7190	0.7190
0.0	38.0	304	1.3866	0.0059	4951.1992	3431.9098	1891.0	2475.0	0.7640	1899.0	0.7673	976.0	976.0	1196.0	0.8161	0.8161	915.0	915.0	1267.0	0.7222	0.7222
0.0	39.0	312	1.3877	0.0059	4954.8532	3434.4425	1891.0	2475.0	0.7640	1899.0	0.7673	979.0	979.0	1196.0	0.8186	0.8186	912.0	912.0	1267.0	0.7198	0.7198
0.9048	40.0	320	1.3879	0.0059	4955.7900	3435.0919	1891.0	2475.0	0.7640	1899.0	0.7673	978.0	978.0	1196.0	0.8177	0.8177	913.0	913.0	1267.0	0.7206	0.7206
0.0	41.0	328	1.3896	0.0059	4961.8652	3439.3029	1891.0	2475.0	0.7640	1899.0	0.7673	978.0	978.0	1196.0	0.8177	0.8177	913.0	913.0	1267.0	0.7206	0.7206
0.0	42.0	336	1.3883	0.0059	4957.0459	3435.9624	1891.0	2475.0	0.7640	1899.0	0.7673	980.0	980.0	1196.0	0.8194	0.8194	911.0	911.0	1267.0	0.7190	0.7190
0.0	43.0	344	1.3898	0.0059	4962.4755	3439.7259	1889.0	2475.0	0.7632	1897.0	0.7665	979.0	979.0	1196.0	0.8186	0.8186	910.0	910.0	1267.0	0.7182	0.7182
0.0	44.0	352	1.3905	0.0059	4965.0345	3441.4997	1889.0	2475.0	0.7632	1897.0	0.7665	980.0	980.0	1196.0	0.8194	0.8194	909.0	909.0	1267.0	0.7174	0.7174
0.0	45.0	360	1.3905	0.0059	4964.9209	3441.4209	1891.0	2475.0	0.7640	1899.0	0.7673	981.0	981.0	1196.0	0.8202	0.8202	910.0	910.0	1267.0	0.7182	0.7182
0.0	46.0	368	1.3915	0.0059	4968.4294	3443.8529	1891.0	2475.0	0.7640	1899.0	0.7673	980.0	980.0	1196.0	0.8194	0.8194	911.0	911.0	1267.0	0.7190	0.7190
0.0	47.0	376	1.3918	0.0059	4969.5671	3444.6414	1889.0	2475.0	0.7632	1897.0	0.7665	981.0	981.0	1196.0	0.8202	0.8202	908.0	908.0	1267.0	0.7167	0.7167
0.0	48.0	384	1.3916	0.0059	4968.9056	3444.1829	1890.0	2475.0	0.7636	1897.0	0.7665	980.0	981.0	1196.0	0.8202	0.8194	909.0	909.0	1267.0	0.7174	0.7174

Framework versions

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