BoolQ_Llama-3.2-1B-26t8ytsb

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: 1.6420
• Model Preparation Time: 0.0059
• Mdl: 7746.5496
• Accumulated Loss: 5369.4990
• Correct Preds: 2337.0
• Total Preds: 3270.0
• Accuracy: 0.7147
• Correct Gen Preds: 2302.0
• Gen Accuracy: 0.7040
• Correct Gen Preds 9642: 1525.0
• Correct Preds 9642: 1556.0
• Total Labels 9642: 2026.0
• Accuracy 9642: 0.7680
• Gen Accuracy 9642: 0.7527
• Correct Gen Preds 2822: 768.0
• Correct Preds 2822: 781.0
• Total Labels 2822: 1231.0
• Accuracy 2822: 0.6344
• Gen Accuracy 2822: 0.6239

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	0.0059	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
0.5194	1.0	3	0.7779	0.0059	3669.7865	2543.7022	1857.0	3270.0	0.5679	1701.0	0.5202	670.0	760.0	2026.0	0.3751	0.3307	1022.0	1097.0	1231.0	0.8911	0.8302
0.2429	2.0	6	0.8811	0.0059	4156.8448	2881.3053	2088.0	3270.0	0.6385	1960.0	0.5994	1723.0	1837.0	2026.0	0.9067	0.8504	228.0	251.0	1231.0	0.2039	0.1852
0.0895	3.0	9	0.7633	0.0059	3601.1554	2496.1307	2252.0	3270.0	0.6887	2036.0	0.6226	1357.0	1527.0	2026.0	0.7537	0.6698	670.0	725.0	1231.0	0.5890	0.5443
0.3695	4.0	12	2.0120	0.0059	9491.7849	6579.2040	2288.0	3270.0	0.6997	2274.0	0.6954	1905.0	1917.0	2026.0	0.9462	0.9403	360.0	371.0	1231.0	0.3014	0.2924
0.0001	5.0	15	1.6420	0.0059	7746.5496	5369.4990	2337.0	3270.0	0.7147	2302.0	0.7040	1525.0	1556.0	2026.0	0.7680	0.7527	768.0	781.0	1231.0	0.6344	0.6239
0.0	6.0	18	1.8149	0.0059	8562.1293	5934.8158	2298.0	3270.0	0.7028	2231.0	0.6823	1282.0	1340.0	2026.0	0.6614	0.6328	940.0	958.0	1231.0	0.7782	0.7636
0.0006	7.0	21	1.9537	0.0059	9216.6870	6388.5206	2267.0	3270.0	0.6933	2202.0	0.6734	1241.0	1299.0	2026.0	0.6412	0.6125	952.0	968.0	1231.0	0.7864	0.7734
0.0	8.0	24	2.0340	0.0059	9595.7529	6651.2691	2302.0	3270.0	0.7040	2246.0	0.6869	1366.0	1414.0	2026.0	0.6979	0.6742	871.0	888.0	1231.0	0.7214	0.7076
0.0	9.0	27	2.1642	0.0059	10209.8590	7076.9350	2299.0	3270.0	0.7031	2241.0	0.6853	1413.0	1464.0	2026.0	0.7226	0.6974	819.0	835.0	1231.0	0.6783	0.6653
0.0	10.0	30	2.2394	0.0059	10564.7680	7322.9392	2289.0	3270.0	0.7	2231.0	0.6823	1436.0	1485.0	2026.0	0.7330	0.7088	786.0	804.0	1231.0	0.6531	0.6385
0.0	11.0	33	2.2853	0.0059	10781.2871	7473.0188	2289.0	3270.0	0.7	2239.0	0.6847	1459.0	1500.0	2026.0	0.7404	0.7201	771.0	789.0	1231.0	0.6409	0.6263
0.0	12.0	36	2.3170	0.0059	10930.7882	7576.6450	2293.0	3270.0	0.7012	2249.0	0.6878	1474.0	1513.0	2026.0	0.7468	0.7275	766.0	780.0	1231.0	0.6336	0.6223
0.0	13.0	39	2.3358	0.0059	11019.6269	7638.2233	2291.0	3270.0	0.7006	2250.0	0.6881	1480.0	1519.0	2026.0	0.7498	0.7305	761.0	772.0	1231.0	0.6271	0.6182
0.0	14.0	42	2.3477	0.0059	11075.6473	7677.0537	2288.0	3270.0	0.6997	2249.0	0.6878	1484.0	1521.0	2026.0	0.7507	0.7325	756.0	767.0	1231.0	0.6231	0.6141
0.0	15.0	45	2.3567	0.0059	11118.0345	7706.4343	2285.0	3270.0	0.6988	2248.0	0.6875	1483.0	1517.0	2026.0	0.7488	0.7320	756.0	768.0	1231.0	0.6239	0.6141
0.0	16.0	48	2.3619	0.0059	11142.3851	7723.3128	2282.0	3270.0	0.6979	2248.0	0.6875	1483.0	1517.0	2026.0	0.7488	0.7320	756.0	765.0	1231.0	0.6214	0.6141
0.0	17.0	51	2.3645	0.0059	11154.6211	7731.7942	2292.0	3270.0	0.7009	2256.0	0.6899	1489.0	1524.0	2026.0	0.7522	0.7349	758.0	768.0	1231.0	0.6239	0.6158
0.0	18.0	54	2.3710	0.0059	11185.5857	7753.2572	2283.0	3270.0	0.6982	2251.0	0.6884	1485.0	1517.0	2026.0	0.7488	0.7330	757.0	766.0	1231.0	0.6223	0.6149
0.0	19.0	57	2.3719	0.0059	11189.8794	7756.2333	2285.0	3270.0	0.6988	2252.0	0.6887	1488.0	1520.0	2026.0	0.7502	0.7345	755.0	765.0	1231.0	0.6214	0.6133
0.0	20.0	60	2.3739	0.0059	11199.2181	7762.7064	2287.0	3270.0	0.6994	2255.0	0.6896	1489.0	1520.0	2026.0	0.7502	0.7349	757.0	767.0	1231.0	0.6231	0.6149
0.0	21.0	63	2.3731	0.0059	11195.3841	7760.0489	2287.0	3270.0	0.6994	2255.0	0.6896	1491.0	1521.0	2026.0	0.7507	0.7359	755.0	766.0	1231.0	0.6223	0.6133
0.0	22.0	66	2.3758	0.0059	11208.0963	7768.8604	2285.0	3270.0	0.6988	2258.0	0.6905	1494.0	1522.0	2026.0	0.7512	0.7374	756.0	763.0	1231.0	0.6198	0.6141
0.0	23.0	69	2.3778	0.0059	11217.4939	7775.3743	2284.0	3270.0	0.6985	2255.0	0.6896	1493.0	1521.0	2026.0	0.7507	0.7369	753.0	763.0	1231.0	0.6198	0.6117
0.0	24.0	72	2.3792	0.0059	11224.3777	7780.1458	2289.0	3270.0	0.7	2258.0	0.6905	1491.0	1522.0	2026.0	0.7512	0.7359	758.0	767.0	1231.0	0.6231	0.6158
0.0	25.0	75	2.3799	0.0059	11227.6572	7782.4189	2290.0	3270.0	0.7003	2260.0	0.6911	1493.0	1522.0	2026.0	0.7512	0.7369	759.0	768.0	1231.0	0.6239	0.6166
0.0	26.0	78	2.3831	0.0059	11242.5989	7792.7757	2283.0	3270.0	0.6982	2251.0	0.6884	1488.0	1520.0	2026.0	0.7502	0.7345	754.0	763.0	1231.0	0.6198	0.6125
0.0	27.0	81	2.3824	0.0059	11239.3411	7790.5176	2287.0	3270.0	0.6994	2259.0	0.6908	1492.0	1520.0	2026.0	0.7502	0.7364	758.0	767.0	1231.0	0.6231	0.6158
0.0	28.0	84	2.3854	0.0059	11253.1701	7800.1031	2288.0	3270.0	0.6997	2258.0	0.6905	1494.0	1522.0	2026.0	0.7512	0.7374	756.0	766.0	1231.0	0.6223	0.6141
0.0	29.0	87	2.3858	0.0059	11255.3105	7801.5867	2292.0	3270.0	0.7009	2261.0	0.6914	1496.0	1526.0	2026.0	0.7532	0.7384	756.0	766.0	1231.0	0.6223	0.6141
0.0	30.0	90	2.3892	0.0059	11271.3917	7812.7334	2285.0	3270.0	0.6988	2260.0	0.6911	1494.0	1520.0	2026.0	0.7502	0.7374	757.0	765.0	1231.0	0.6214	0.6149
0.0	31.0	93	2.3900	0.0059	11274.9614	7815.2077	2287.0	3270.0	0.6994	2259.0	0.6908	1493.0	1521.0	2026.0	0.7507	0.7369	757.0	766.0	1231.0	0.6223	0.6149
0.0	32.0	96	2.3922	0.0059	11285.5291	7822.5327	2285.0	3270.0	0.6988	2256.0	0.6899	1492.0	1520.0	2026.0	0.7502	0.7364	755.0	765.0	1231.0	0.6214	0.6133
0.0	33.0	99	2.3909	0.0059	11279.2140	7818.1554	2287.0	3270.0	0.6994	2262.0	0.6917	1493.0	1518.0	2026.0	0.7493	0.7369	760.0	769.0	1231.0	0.6247	0.6174
0.0	34.0	102	2.3927	0.0059	11287.6169	7823.9798	2283.0	3270.0	0.6982	2259.0	0.6908	1495.0	1519.0	2026.0	0.7498	0.7379	755.0	764.0	1231.0	0.6206	0.6133
0.0	35.0	105	2.3930	0.0059	11289.1913	7825.0711	2285.0	3270.0	0.6988	2258.0	0.6905	1494.0	1523.0	2026.0	0.7517	0.7374	755.0	762.0	1231.0	0.6190	0.6133

Framework versions

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