GSM8K-Binary_Llama-3.2-1B-fmntg7h8

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: 1.1051
• Model Preparation Time: 0.0056
• Mdl: 3945.9379
• Accumulated Loss: 2735.1157
• Correct Preds: 1562.0
• Total Preds: 2475.0
• Accuracy: 0.6311
• Correct Gen Preds: 804.0
• Gen Accuracy: 0.3248
• Correct Gen Preds 34192: 398.0
• Correct Preds 34192: 875.0
• Total Labels 34192: 1196.0
• Accuracy 34192: 0.7316
• Gen Accuracy 34192: 0.3328
• Correct Gen Preds 41568: 399.0
• Correct Preds 41568: 687.0
• Total Labels 41568: 1267.0
• Accuracy 41568: 0.5422
• Gen Accuracy 41568: 0.3149

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	0.0056	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
1.2894	1.0	1	1.4656	0.0056	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
1.2894	2.0	2	5.0031	0.0056	17864.2446	12382.5508	1267.0	2475.0	0.5119	1274.0	0.5147	0.0	0.0	1196.0	0.0	0.0	1266.0	1267.0	1267.0	1.0	0.9992
5.3725	3.0	3	1.0418	0.0056	3719.9628	2578.4817	1267.0	2475.0	0.5119	8.0	0.0032	0.0	0.0	1196.0	0.0	0.0	0.0	1267.0	1267.0	1.0	0.0
1.0334	4.0	4	1.9141	0.0056	6834.5336	4737.3377	1196.0	2475.0	0.4832	8.0	0.0032	0.0	1196.0	1196.0	1.0	0.0	0.0	0.0	1267.0	0.0	0.0
1.7152	5.0	5	0.8599	0.0056	3070.4544	2128.2768	1201.0	2475.0	0.4853	7.0	0.0028	0.0	1188.0	1196.0	0.9933	0.0	0.0	13.0	1267.0	0.0103	0.0
0.676	6.0	6	0.9139	0.0056	3263.1526	2261.8450	1275.0	2475.0	0.5152	7.0	0.0028	0.0	17.0	1196.0	0.0142	0.0	0.0	1258.0	1267.0	0.9929	0.0
0.8111	7.0	7	0.7598	0.0056	2712.8353	1880.3942	1494.0	2475.0	0.6036	7.0	0.0028	0.0	1026.0	1196.0	0.8579	0.0	0.0	468.0	1267.0	0.3694	0.0
0.5625	8.0	8	0.7617	0.0056	2719.8767	1885.2748	1367.0	2475.0	0.5523	7.0	0.0028	0.0	296.0	1196.0	0.2475	0.0	0.0	1071.0	1267.0	0.8453	0.0
0.4464	9.0	9	0.7365	0.0056	2629.9084	1822.9136	1462.0	2475.0	0.5907	7.0	0.0028	0.0	817.0	1196.0	0.6831	0.0	0.0	645.0	1267.0	0.5091	0.0
0.295	10.0	10	0.8989	0.0056	3209.6418	2224.7542	1361.0	2475.0	0.5499	7.0	0.0028	0.0	1160.0	1196.0	0.9699	0.0	0.0	201.0	1267.0	0.1586	0.0
0.2414	11.0	11	0.7902	0.0056	2821.4112	1955.6532	1484.0	2475.0	0.5996	7.0	0.0028	0.0	1042.0	1196.0	0.8712	0.0	0.0	442.0	1267.0	0.3489	0.0
0.0982	12.0	12	0.7771	0.0056	2774.9207	1923.4284	1463.0	2475.0	0.5911	7.0	0.0028	0.0	554.0	1196.0	0.4632	0.0	0.0	909.0	1267.0	0.7174	0.0
0.068	13.0	13	0.7808	0.0056	2788.0061	1932.4986	1521.0	2475.0	0.6145	7.0	0.0028	0.0	752.0	1196.0	0.6288	0.0	0.0	769.0	1267.0	0.6069	0.0
0.015	14.0	14	1.1364	0.0056	4057.6446	2812.5449	1476.0	2475.0	0.5964	7.0	0.0028	0.0	1116.0	1196.0	0.9331	0.0	0.0	360.0	1267.0	0.2841	0.0
0.0068	15.0	15	1.2663	0.0056	4521.6005	3134.1346	1470.0	2475.0	0.5939	10.0	0.0040	0.0	1108.0	1196.0	0.9264	0.0	3.0	362.0	1267.0	0.2857	0.0024
0.0009	16.0	16	1.2510	0.0056	4467.0043	3096.2915	1493.0	2475.0	0.6032	40.0	0.0162	16.0	1072.0	1196.0	0.8963	0.0134	17.0	421.0	1267.0	0.3323	0.0134
0.0002	17.0	17	1.2050	0.0056	4302.6121	2982.3435	1527.0	2475.0	0.6170	154.0	0.0622	70.0	1041.0	1196.0	0.8704	0.0585	77.0	486.0	1267.0	0.3836	0.0608
0.0001	18.0	18	1.1676	0.0056	4169.0566	2889.7698	1536.0	2475.0	0.6206	317.0	0.1281	153.0	999.0	1196.0	0.8353	0.1279	157.0	537.0	1267.0	0.4238	0.1239
0.0001	19.0	19	1.1388	0.0056	4066.2817	2818.5317	1550.0	2475.0	0.6263	485.0	0.1960	235.0	960.0	1196.0	0.8027	0.1965	243.0	590.0	1267.0	0.4657	0.1918
0.0	20.0	20	1.1190	0.0056	3995.7100	2769.6151	1552.0	2475.0	0.6271	606.0	0.2448	307.0	922.0	1196.0	0.7709	0.2567	292.0	630.0	1267.0	0.4972	0.2305
0.0	21.0	21	1.1097	0.0056	3962.2472	2746.4205	1556.0	2475.0	0.6287	723.0	0.2921	360.0	894.0	1196.0	0.7475	0.3010	356.0	662.0	1267.0	0.5225	0.2810
0.0	22.0	22	1.1051	0.0056	3945.9379	2735.1157	1562.0	2475.0	0.6311	804.0	0.3248	398.0	875.0	1196.0	0.7316	0.3328	399.0	687.0	1267.0	0.5422	0.3149
0.0	23.0	23	1.1066	0.0056	3951.2043	2738.7661	1556.0	2475.0	0.6287	867.0	0.3503	434.0	861.0	1196.0	0.7199	0.3629	426.0	695.0	1267.0	0.5485	0.3362
0.0	24.0	24	1.1099	0.0056	3963.2388	2747.1078	1551.0	2475.0	0.6267	919.0	0.3713	463.0	848.0	1196.0	0.7090	0.3871	449.0	703.0	1267.0	0.5549	0.3544
0.0	25.0	25	1.1150	0.0056	3981.1612	2759.5307	1546.0	2475.0	0.6246	967.0	0.3907	488.0	841.0	1196.0	0.7032	0.4080	472.0	705.0	1267.0	0.5564	0.3725
0.0	26.0	26	1.1229	0.0056	4009.6006	2779.2434	1538.0	2475.0	0.6214	1000.0	0.4040	512.0	841.0	1196.0	0.7032	0.4281	481.0	697.0	1267.0	0.5501	0.3796
0.0	27.0	27	1.1302	0.0056	4035.6677	2797.3117	1546.0	2475.0	0.6246	1038.0	0.4194	535.0	844.0	1196.0	0.7057	0.4473	496.0	702.0	1267.0	0.5541	0.3915
0.0	28.0	28	1.1359	0.0056	4056.0096	2811.4116	1543.0	2475.0	0.6234	1062.0	0.4291	556.0	844.0	1196.0	0.7057	0.4649	499.0	699.0	1267.0	0.5517	0.3938
0.0	29.0	29	1.1418	0.0056	4076.9828	2825.9492	1539.0	2475.0	0.6218	1078.0	0.4356	569.0	845.0	1196.0	0.7065	0.4758	502.0	694.0	1267.0	0.5478	0.3962
0.0	30.0	30	1.1483	0.0056	4100.0299	2841.9241	1532.0	2475.0	0.6190	1096.0	0.4428	581.0	843.0	1196.0	0.7048	0.4858	508.0	689.0	1267.0	0.5438	0.4009
0.0	31.0	31	1.1516	0.0056	4111.8632	2850.1264	1542.0	2475.0	0.6230	1104.0	0.4461	590.0	852.0	1196.0	0.7124	0.4933	507.0	690.0	1267.0	0.5446	0.4002
0.0	32.0	32	1.1572	0.0056	4132.1376	2864.1795	1536.0	2475.0	0.6206	1118.0	0.4517	600.0	851.0	1196.0	0.7115	0.5017	511.0	685.0	1267.0	0.5406	0.4033
0.0	33.0	33	1.1625	0.0056	4150.7724	2877.0962	1540.0	2475.0	0.6222	1124.0	0.4541	605.0	854.0	1196.0	0.7140	0.5059	512.0	686.0	1267.0	0.5414	0.4041
0.0	34.0	34	1.1650	0.0056	4159.6856	2883.2743	1543.0	2475.0	0.6234	1132.0	0.4574	614.0	860.0	1196.0	0.7191	0.5134	511.0	683.0	1267.0	0.5391	0.4033
0.0	35.0	35	1.1686	0.0056	4172.7025	2892.2970	1546.0	2475.0	0.6246	1144.0	0.4622	622.0	861.0	1196.0	0.7199	0.5201	515.0	685.0	1267.0	0.5406	0.4065
0.0	36.0	36	1.1715	0.0056	4182.9415	2899.3941	1541.0	2475.0	0.6226	1139.0	0.4602	619.0	860.0	1196.0	0.7191	0.5176	513.0	681.0	1267.0	0.5375	0.4049
0.0	37.0	37	1.1733	0.0056	4189.4789	2903.9255	1541.0	2475.0	0.6226	1140.0	0.4606	621.0	860.0	1196.0	0.7191	0.5192	512.0	681.0	1267.0	0.5375	0.4041
0.0	38.0	38	1.1751	0.0056	4195.7990	2908.3062	1546.0	2475.0	0.6246	1141.0	0.4610	624.0	864.0	1196.0	0.7224	0.5217	510.0	682.0	1267.0	0.5383	0.4025
0.0	39.0	39	1.1772	0.0056	4203.2968	2913.5034	1542.0	2475.0	0.6230	1151.0	0.4651	629.0	863.0	1196.0	0.7216	0.5259	515.0	679.0	1267.0	0.5359	0.4065
0.0	40.0	40	1.1783	0.0056	4207.4070	2916.3523	1545.0	2475.0	0.6242	1153.0	0.4659	632.0	867.0	1196.0	0.7249	0.5284	514.0	678.0	1267.0	0.5351	0.4057
0.0	41.0	41	1.1797	0.0056	4212.1617	2919.6480	1546.0	2475.0	0.6246	1156.0	0.4671	637.0	869.0	1196.0	0.7266	0.5326	512.0	677.0	1267.0	0.5343	0.4041
0.0	42.0	42	1.1807	0.0056	4215.7863	2922.1604	1549.0	2475.0	0.6259	1152.0	0.4655	635.0	871.0	1196.0	0.7283	0.5309	510.0	678.0	1267.0	0.5351	0.4025
0.0	43.0	43	1.1811	0.0056	4217.3027	2923.2115	1546.0	2475.0	0.6246	1153.0	0.4659	638.0	870.0	1196.0	0.7274	0.5334	508.0	676.0	1267.0	0.5335	0.4009
0.0	44.0	44	1.1826	0.0056	4222.7328	2926.9753	1543.0	2475.0	0.6234	1159.0	0.4683	638.0	867.0	1196.0	0.7249	0.5334	514.0	676.0	1267.0	0.5335	0.4057
0.0	45.0	45	1.1820	0.0056	4220.4729	2925.4089	1549.0	2475.0	0.6259	1163.0	0.4699	642.0	872.0	1196.0	0.7291	0.5368	514.0	677.0	1267.0	0.5343	0.4057
0.0	46.0	46	1.1830	0.0056	4223.9636	2927.8285	1543.0	2475.0	0.6234	1156.0	0.4671	640.0	871.0	1196.0	0.7283	0.5351	509.0	672.0	1267.0	0.5304	0.4017
0.0	47.0	47	1.1842	0.0056	4228.2689	2930.8126	1546.0	2475.0	0.6246	1159.0	0.4683	641.0	871.0	1196.0	0.7283	0.5360	511.0	675.0	1267.0	0.5328	0.4033
0.0	48.0	48	1.1843	0.0056	4228.6913	2931.1054	1541.0	2475.0	0.6226	1157.0	0.4675	639.0	868.0	1196.0	0.7258	0.5343	511.0	673.0	1267.0	0.5312	0.4033
0.0	49.0	49	1.1849	0.0056	4230.8653	2932.6124	1548.0	2475.0	0.6255	1156.0	0.4671	638.0	874.0	1196.0	0.7308	0.5334	511.0	674.0	1267.0	0.5320	0.4033
0.0	50.0	50	1.1857	0.0056	4233.7065	2934.5817	1542.0	2475.0	0.6230	1158.0	0.4679	640.0	871.0	1196.0	0.7283	0.5351	511.0	671.0	1267.0	0.5296	0.4033
0.0	51.0	51	1.1861	0.0056	4235.1580	2935.5879	1545.0	2475.0	0.6242	1161.0	0.4691	639.0	869.0	1196.0	0.7266	0.5343	515.0	676.0	1267.0	0.5335	0.4065
0.0	52.0	52	1.1867	0.0056	4237.4739	2937.1931	1538.0	2475.0	0.6214	1159.0	0.4683	639.0	867.0	1196.0	0.7249	0.5343	513.0	671.0	1267.0	0.5296	0.4049

Framework versions

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