BoolQ_Llama-3.2-1B-cy926ylx

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: 1.4230
• Model Preparation Time: 0.0057
• Mdl: 6713.3268
• Accumulated Loss: 4653.3235
• Correct Preds: 2664.0
• Total Preds: 3270.0
• Accuracy: 0.8147
• Correct Gen Preds: 2667.0
• Gen Accuracy: 0.8156
• Correct Gen Preds 9642: 1802.0
• Correct Preds 9642: 1807.0
• Total Labels 9642: 2026.0
• Accuracy 9642: 0.8919
• Gen Accuracy 9642: 0.8894
• Correct Gen Preds 2822: 856.0
• Correct Preds 2822: 857.0
• Total Labels 2822: 1231.0
• Accuracy 2822: 0.6962
• Gen Accuracy 2822: 0.6954

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: 120
• 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 9642	Correct Preds 9642	Total Labels 9642	Accuracy 9642	Gen Accuracy 9642	Correct Gen Preds 2822	Correct Preds 2822	Total Labels 2822	Accuracy 2822	Gen Accuracy 2822
No log	0	0	0.7080	0.0057	3339.8933	2315.0376	2032.0	3270.0	0.6214	2040.0	0.6239	2007.0	2008.0	2026.0	0.9911	0.9906	24.0	24.0	1231.0	0.0195	0.0195
0.5208	1.0	88	0.5289	0.0057	2495.1055	1729.4753	2515.0	3270.0	0.7691	2521.0	0.7709	1519.0	1519.0	2026.0	0.7498	0.7498	994.0	996.0	1231.0	0.8091	0.8075
0.1936	2.0	176	0.5061	0.0057	2387.4657	1654.8651	2567.0	3270.0	0.7850	2438.0	0.7456	1533.0	1613.0	2026.0	0.7962	0.7567	898.0	954.0	1231.0	0.7750	0.7295
0.0088	3.0	264	0.7730	0.0057	3646.8635	2527.8132	2638.0	3270.0	0.8067	2558.0	0.7823	1701.0	1753.0	2026.0	0.8653	0.8396	849.0	885.0	1231.0	0.7189	0.6897
0.0001	4.0	352	1.3368	0.0057	6306.5652	4371.3779	2659.0	3270.0	0.8131	2596.0	0.7939	1630.0	1688.0	2026.0	0.8332	0.8045	959.0	971.0	1231.0	0.7888	0.7790
0.0001	5.0	440	1.4230	0.0057	6713.3268	4653.3235	2664.0	3270.0	0.8147	2667.0	0.8156	1802.0	1807.0	2026.0	0.8919	0.8894	856.0	857.0	1231.0	0.6962	0.6954
0.2977	6.0	528	1.4487	0.0057	6834.5751	4737.3664	2654.0	3270.0	0.8116	2660.0	0.8135	1755.0	1757.0	2026.0	0.8672	0.8662	896.0	897.0	1231.0	0.7287	0.7279
0.0	7.0	616	1.6005	0.0057	7550.5612	5233.6502	2659.0	3270.0	0.8131	2546.0	0.7786	1659.0	1764.0	2026.0	0.8707	0.8189	878.0	895.0	1231.0	0.7271	0.7132
0.0	8.0	704	1.4996	0.0057	7074.6521	4903.7752	2656.0	3270.0	0.8122	2504.0	0.7657	1611.0	1757.0	2026.0	0.8672	0.7952	884.0	899.0	1231.0	0.7303	0.7181
0.0	9.0	792	1.5944	0.0057	7521.5767	5213.5597	2662.0	3270.0	0.8141	2670.0	0.8165	1780.0	1781.0	2026.0	0.8791	0.8786	881.0	881.0	1231.0	0.7157	0.7157
0.0	10.0	880	1.5889	0.0057	7495.8632	5195.7364	2659.0	3270.0	0.8131	2662.0	0.8141	1755.0	1760.0	2026.0	0.8687	0.8662	898.0	899.0	1231.0	0.7303	0.7295
0.0	11.0	968	1.6243	0.0057	7662.7530	5311.4156	2651.0	3270.0	0.8107	2642.0	0.8080	1737.0	1752.0	2026.0	0.8648	0.8574	896.0	899.0	1231.0	0.7303	0.7279
0.0	12.0	1056	1.6408	0.0057	7740.8061	5365.5180	2654.0	3270.0	0.8116	2642.0	0.8080	1738.0	1755.0	2026.0	0.8662	0.8578	895.0	899.0	1231.0	0.7303	0.7271
0.0	13.0	1144	1.6519	0.0057	7792.9701	5401.6753	2649.0	3270.0	0.8101	2639.0	0.8070	1737.0	1752.0	2026.0	0.8648	0.8574	893.0	897.0	1231.0	0.7287	0.7254
0.0004	14.0	1232	1.6617	0.0057	7839.1774	5433.7037	2651.0	3270.0	0.8107	2639.0	0.8070	1735.0	1753.0	2026.0	0.8653	0.8564	895.0	898.0	1231.0	0.7295	0.7271
0.0001	15.0	1320	1.6678	0.0057	7868.0329	5453.7048	2652.0	3270.0	0.8110	2641.0	0.8076	1736.0	1752.0	2026.0	0.8648	0.8569	896.0	900.0	1231.0	0.7311	0.7279
0.0	16.0	1408	1.6729	0.0057	7891.8646	5470.2237	2653.0	3270.0	0.8113	2640.0	0.8073	1738.0	1755.0	2026.0	0.8662	0.8578	893.0	898.0	1231.0	0.7295	0.7254
0.0	17.0	1496	1.6777	0.0057	7914.6192	5485.9960	2654.0	3270.0	0.8116	2642.0	0.8080	1737.0	1753.0	2026.0	0.8653	0.8574	896.0	901.0	1231.0	0.7319	0.7279
0.0	18.0	1584	1.6785	0.0057	7918.6000	5488.7553	2653.0	3270.0	0.8113	2643.0	0.8083	1736.0	1752.0	2026.0	0.8648	0.8569	898.0	901.0	1231.0	0.7319	0.7295
0.0	19.0	1672	1.6852	0.0057	7949.9338	5510.4742	2653.0	3270.0	0.8113	2642.0	0.8080	1737.0	1753.0	2026.0	0.8653	0.8574	896.0	900.0	1231.0	0.7311	0.7279
0.0	20.0	1760	1.6840	0.0057	7944.2327	5506.5225	2657.0	3270.0	0.8125	2645.0	0.8089	1737.0	1754.0	2026.0	0.8657	0.8574	899.0	903.0	1231.0	0.7335	0.7303
0.0	21.0	1848	1.6851	0.0057	7949.6500	5510.2775	2654.0	3270.0	0.8116	2643.0	0.8083	1739.0	1755.0	2026.0	0.8662	0.8583	895.0	899.0	1231.0	0.7303	0.7271
0.0	22.0	1936	1.6912	0.0057	7978.4416	5530.2343	2650.0	3270.0	0.8104	2641.0	0.8076	1738.0	1753.0	2026.0	0.8653	0.8578	894.0	897.0	1231.0	0.7287	0.7262
0.0	23.0	2024	1.6878	0.0057	7962.5355	5519.2090	2655.0	3270.0	0.8119	2643.0	0.8083	1737.0	1753.0	2026.0	0.8653	0.8574	897.0	902.0	1231.0	0.7327	0.7287
0.0	24.0	2112	1.6930	0.0057	7987.0414	5536.1952	2654.0	3270.0	0.8116	2645.0	0.8089	1740.0	1754.0	2026.0	0.8657	0.8588	896.0	900.0	1231.0	0.7311	0.7279
0.0	25.0	2200	1.6919	0.0057	7981.6813	5532.4799	2650.0	3270.0	0.8104	2640.0	0.8073	1736.0	1751.0	2026.0	0.8643	0.8569	895.0	899.0	1231.0	0.7303	0.7271
0.0	26.0	2288	1.6901	0.0057	7973.0109	5526.4700	2655.0	3270.0	0.8119	2642.0	0.8080	1738.0	1755.0	2026.0	0.8662	0.8578	895.0	900.0	1231.0	0.7311	0.7271
0.0	27.0	2376	1.6942	0.0057	7992.5643	5540.0234	2654.0	3270.0	0.8116	2643.0	0.8083	1738.0	1754.0	2026.0	0.8657	0.8578	896.0	900.0	1231.0	0.7311	0.7279
0.0	28.0	2464	1.6942	0.0057	7992.3890	5539.9019	2654.0	3270.0	0.8116	2644.0	0.8086	1739.0	1754.0	2026.0	0.8657	0.8583	896.0	900.0	1231.0	0.7311	0.7279
0.0	29.0	2552	1.6948	0.0057	7995.6135	5542.1369	2657.0	3270.0	0.8125	2645.0	0.8089	1739.0	1756.0	2026.0	0.8667	0.8583	897.0	901.0	1231.0	0.7319	0.7287
0.0	30.0	2640	1.6959	0.0057	8000.7850	5545.7216	2652.0	3270.0	0.8110	2641.0	0.8076	1739.0	1754.0	2026.0	0.8657	0.8583	893.0	898.0	1231.0	0.7295	0.7254
0.0	31.0	2728	1.6953	0.0057	7997.5769	5543.4979	2656.0	3270.0	0.8122	2644.0	0.8086	1737.0	1754.0	2026.0	0.8657	0.8574	898.0	902.0	1231.0	0.7327	0.7295
0.0	32.0	2816	1.6957	0.0057	7999.8487	5545.0726	2659.0	3270.0	0.8131	2648.0	0.8098	1742.0	1758.0	2026.0	0.8677	0.8598	897.0	901.0	1231.0	0.7319	0.7287
0.0	33.0	2904	1.6941	0.0057	7992.0829	5539.6897	2657.0	3270.0	0.8125	2645.0	0.8089	1740.0	1757.0	2026.0	0.8672	0.8588	896.0	900.0	1231.0	0.7311	0.7279
0.0	34.0	2992	1.6952	0.0057	7997.3524	5543.3422	2653.0	3270.0	0.8113	2642.0	0.8080	1737.0	1753.0	2026.0	0.8653	0.8574	896.0	900.0	1231.0	0.7311	0.7279
0.0	35.0	3080	1.6947	0.0057	7995.0017	5541.7129	2652.0	3270.0	0.8110	2641.0	0.8076	1738.0	1753.0	2026.0	0.8653	0.8578	894.0	899.0	1231.0	0.7303	0.7262

Framework versions

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