learning_rate: 5e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 3.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
1.6092	0.0134	200	1.6427
1.6207	0.0268	400	1.5731
1.4977	0.0401	600	1.5319
1.4884	0.0535	800	1.4842
1.4742	0.0669	1000	1.4475
1.4454	0.0803	1200	1.4337
1.4859	0.0936	1400	1.4163
1.4306	0.1070	1600	1.3901
1.3368	0.1204	1800	1.3759
1.4050	0.1338	2000	1.3601
1.3805	0.1471	2200	1.3531
1.3305	0.1605	2400	1.3400
1.3535	0.1739	2600	1.3332
1.3303	0.1873	2800	1.3215
1.3759	0.2006	3000	1.3155
1.3741	0.2140	3200	1.3112
1.3115	0.2274	3400	1.3031
1.3462	0.2408	3600	1.2976
1.2986	0.2541	3800	1.2904
1.3035	0.2675	4000	1.2914
1.3145	0.2809	4200	1.2878
1.2939	0.2943	4400	1.2806
1.2163	0.3077	4600	1.2769
1.2687	0.3210	4800	1.2684
1.2849	0.3344	5000	1.2662
1.2934	0.3478	5200	1.2649
1.2936	0.3612	5400	1.2611
1.2497	0.3745	5600	1.2589
1.1704	0.3879	5800	1.2587
1.2640	0.4013	6000	1.2535
1.2498	0.4147	6200	1.2478
1.1985	0.4280	6400	1.2478
1.2602	0.4414	6600	1.2463
1.2873	0.4548	6800	1.2435
1.2324	0.4682	7000	1.2451
1.2262	0.4815	7200	1.2373
1.2210	0.4949	7400	1.2380
1.2214	0.5083	7600	1.2331
1.2407	0.5217	7800	1.2334
1.2038	0.5350	8000	1.2323
1.2484	0.5484	8200	1.2307
1.1974	0.5618	8400	1.2262
1.2343	0.5752	8600	1.2265
1.2272	0.5886	8800	1.2248
1.2416	0.6019	9000	1.2219
1.2321	0.6153	9200	1.2215
1.2422	0.6287	9400	1.2218
1.2362	0.6421	9600	1.2192
1.2399	0.6554	9800	1.2163
1.2124	0.6688	10000	1.2172
1.2301	0.6822	10200	1.2157
1.2301	0.6956	10400	1.2143
1.2715	0.7089	10600	1.2151
1.1994	0.7223	10800	1.2109
1.2357	0.7357	11000	1.2111
1.1722	0.7491	11200	1.2105
1.1941	0.7624	11400	1.2082
1.1541	0.7758	11600	1.2075
1.2279	0.7892	11800	1.2091
1.1811	0.8026	12000	1.2063
1.2564	0.8159	12200	1.2058
1.2472	0.8293	12400	1.2043
1.1944	0.8427	12600	1.2045
1.2073	0.8561	12800	1.2048
1.1659	0.8694	13000	1.2001
1.2318	0.8828	13200	1.1998
1.2112	0.8962	13400	1.1999
1.2124	0.9096	13600	1.1993
1.2338	0.9230	13800	1.1960
1.1718	0.9363	14000	1.1962
1.2180	0.9497	14200	1.1968
1.2153	0.9631	14400	1.1956
1.2828	0.9765	14600	1.1938
1.1799	0.9898	14800	1.1945
1.1916	1.0032	15000	1.1939
1.1924	1.0166	15200	1.1921
1.1977	1.0300	15400	1.1936
1.1702	1.0433	15600	1.1918
1.1588	1.0567	15800	1.1909
1.1540	1.0701	16000	1.1913
1.1849	1.0835	16200	1.1923
1.1818	1.0968	16400	1.1903
1.1676	1.1102	16600	1.1876
1.1831	1.1236	16800	1.1879
1.1937	1.1370	17000	1.1880
1.2169	1.1503	17200	1.1863
1.1968	1.1637	17400	1.1862
1.2273	1.1771	17600	1.1862
1.1441	1.1905	17800	1.1848
1.2475	1.2039	18000	1.1844
1.1536	1.2172	18200	1.1843
1.1812	1.2306	18400	1.1831
1.1310	1.2440	18600	1.1855
1.1403	1.2574	18800	1.1848
1.2272	1.2707	19000	1.1829
1.1584	1.2841	19200	1.1808
1.1535	1.2975	19400	1.1798
1.2034	1.3109	19600	1.1797
1.1795	1.3242	19800	1.1791
1.1426	1.3376	20000	1.1783
1.1897	1.3510	20200	1.1800
1.1324	1.3644	20400	1.1782
1.1774	1.3777	20600	1.1787
1.1264	1.3911	20800	1.1765
1.2382	1.4045	21000	1.1764
1.2121	1.4179	21200	1.1764
1.2084	1.4312	21400	1.1754
1.1812	1.4446	21600	1.1767
1.1293	1.4580	21800	1.1764
1.1890	1.4714	22000	1.1741
1.1860	1.4848	22200	1.1756
1.1822	1.4981	22400	1.1731
1.1832	1.5115	22600	1.1744
1.1951	1.5249	22800	1.1716
1.1717	1.5383	23000	1.1735
1.1793	1.5516	23200	1.1724
1.1796	1.5650	23400	1.1714
1.1786	1.5784	23600	1.1731
1.1689	1.5918	23800	1.1712
1.1756	1.6051	24000	1.1711
1.1569	1.6185	24200	1.1685
1.1989	1.6319	24400	1.1708
1.2129	1.6453	24600	1.1678
1.1168	1.6586	24800	1.1714
1.1921	1.6720	25000	1.1694
1.1670	1.6854	25200	1.1678
1.1961	1.6988	25400	1.1673
1.1569	1.7121	25600	1.1685
1.1910	1.7255	25800	1.1673
1.1593	1.7389	26000	1.1673
1.1793	1.7523	26200	1.1665
1.1831	1.7657	26400	1.1654
1.1438	1.7790	26600	1.1663
1.1991	1.7924	26800	1.1666
1.1129	1.8058	27000	1.1664
1.2022	1.8192	27200	1.1650
1.1715	1.8325	27400	1.1641
1.1601	1.8459	27600	1.1645
1.1807	1.8593	27800	1.1644
1.1636	1.8727	28000	1.1643
1.1790	1.8860	28200	1.1644
1.1054	1.8994	28400	1.1641
1.1845	1.9128	28600	1.1634
1.1734	1.9262	28800	1.1630
1.1385	1.9395	29000	1.1621
1.1193	1.9529	29200	1.1632
1.2078	1.9663	29400	1.1608
1.1633	1.9797	29600	1.1610
1.1684	1.9930	29800	1.1608
1.1159	2.0064	30000	1.1621
1.1486	2.0198	30200	1.1612
1.1776	2.0332	30400	1.1602
1.1583	2.0465	30600	1.1624
1.1628	2.0599	30800	1.1604
1.2045	2.0733	31000	1.1601
1.1584	2.0867	31200	1.1610
1.1760	2.1001	31400	1.1597
1.1251	2.1134	31600	1.1589
1.1454	2.1268	31800	1.1593
1.1476	2.1402	32000	1.1589
1.1121	2.1536	32200	1.1609
1.2363	2.1669	32400	1.1579
1.0949	2.1803	32600	1.1591
1.1649	2.1937	32800	1.1581
1.1211	2.2071	33000	1.1577
1.1305	2.2204	33200	1.1597
1.1315	2.2338	33400	1.1578
1.1293	2.2472	33600	1.1584
1.1196	2.2606	33800	1.1577
1.1811	2.2739	34000	1.1571
1.1691	2.2873	34200	1.1561
1.1378	2.3007	34400	1.1563
1.1470	2.3141	34600	1.1560
1.1272	2.3274	34800	1.1556
1.0983	2.3408	35000	1.1553
1.1735	2.3542	35200	1.1553
1.1552	2.3676	35400	1.1560
1.1567	2.3810	35600	1.1554
1.1922	2.3943	35800	1.1546
1.1458	2.4077	36000	1.1538
1.1189	2.4211	36200	1.1542
1.1294	2.4345	36400	1.1552
1.2344	2.4478	36600	1.1538
1.1340	2.4612	36800	1.1539
1.1178	2.4746	37000	1.1539
1.1834	2.4880	37200	1.1528
1.1879	2.5013	37400	1.1525
1.1314	2.5147	37600	1.1545
1.1254	2.5281	37800	1.1536
1.1758	2.5415	38000	1.1524
1.1507	2.5548	38200	1.1527
1.1038	2.5682	38400	1.1527
1.1460	2.5816	38600	1.1523
1.1290	2.5950	38800	1.1523
1.1560	2.6083	39000	1.1521
1.1276	2.6217	39200	1.1514
1.1618	2.6351	39400	1.1516
1.1560	2.6485	39600	1.1517
1.1441	2.6619	39800	1.1516
1.1728	2.6752	40000	1.1513
1.1516	2.6886	40200	1.1512
1.1261	2.7020	40400	1.1525
1.0957	2.7154	40600	1.1512
1.1266	2.7287	40800	1.1511
1.1513	2.7421	41000	1.1513
1.1778	2.7555	41200	1.1508
1.1561	2.7689	41400	1.1508
1.1673	2.7822	41600	1.1514
1.1484	2.7956	41800	1.1511
1.1558	2.8090	42000	1.1510
1.1361	2.8224	42200	1.1510
1.1866	2.8357	42400	1.1503
1.1126	2.8491	42600	1.1502
1.1503	2.8625	42800	1.1499
1.1289	2.8759	43000	1.1503
1.1300	2.8892	43200	1.1503
1.1093	2.9026	43400	1.1502
1.1340	2.9160	43600	1.1499
1.0831	2.9294	43800	1.1503
1.1109	2.9428	44000	1.1501
1.1342	2.9561	44200	1.1501
1.1503	2.9695	44400	1.1501

Framework versions

Transformers 5.15.0
Pytorch 2.13.0+cu130
Datasets 5.0.1
Tokenizers 0.22.2

jeanmidev/trackmania-tmnf-gpt2-safefilter4

trackmania-tmnf-gpt2-safefilter4

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

jeanmidev/
trackmania-tmnf-gpt2-safefilter4