Training Loss	Epoch	Step	Validation Loss	Input Tokens Seen
No log	0	0	1.4728	0
1.5446	0.0064	5	1.4691	24560
1.5405	0.0128	10	1.4424	48418
1.4649	0.0192	15	1.3779	72711
1.209	0.0255	20	1.3243	95280
1.2299	0.0319	25	1.2782	118705
1.3254	0.0383	30	1.2496	140707
1.3768	0.0447	35	1.2232	164596
1.225	0.0511	40	1.1943	185674
1.151	0.0575	45	1.1727	209919
1.3374	0.0638	50	1.1536	234235
1.1662	0.0702	55	1.1460	257884
1.2093	0.0766	60	1.1408	282456
1.2667	0.0830	65	1.1386	307218
1.2481	0.0894	70	1.1370	329726
1.3194	0.0958	75	1.1346	349886
1.0113	0.1021	80	1.1340	373223
1.4144	0.1085	85	1.1320	396194
1.1603	0.1149	90	1.1293	421432
1.2591	0.1213	95	1.1281	444802
1.1707	0.1277	100	1.1267	469062
1.168	0.1341	105	1.1259	494303
1.2205	0.1405	110	1.1243	517863
1.0748	0.1468	115	1.1228	541592
1.3323	0.1532	120	1.1221	565270
1.0268	0.1596	125	1.1209	588643
0.9439	0.1660	130	1.1207	612248
1.0729	0.1724	135	1.1202	636354
1.1131	0.1788	140	1.1193	661199
1.178	0.1851	145	1.1186	683535
1.1866	0.1915	150	1.1215	706235
1.3158	0.1979	155	1.1194	730488
1.3337	0.2043	160	1.1177	754801
1.0778	0.2107	165	1.1156	778825
1.0561	0.2171	170	1.1151	803662
1.2131	0.2234	175	1.1144	825402
1.1404	0.2298	180	1.1135	850735
1.1518	0.2362	185	1.1134	874659
1.1035	0.2426	190	1.1130	900128
1.112	0.2490	195	1.1124	924073
1.157	0.2554	200	1.1122	947649
1.4409	0.2618	205	1.1111	968941
1.1141	0.2681	210	1.1109	992840
1.2116	0.2745	215	1.1095	1016077
1.1701	0.2809	220	1.1091	1037217
1.1633	0.2873	225	1.1084	1061379
1.3507	0.2937	230	1.1082	1084125
1.1033	0.3001	235	1.1076	1108920
1.2346	0.3064	240	1.1070	1132072
1.2206	0.3128	245	1.1076	1153431
1.4877	0.3192	250	1.1079	1175897
0.9074	0.3256	255	1.1074	1198368
1.3394	0.3320	260	1.1068	1223602
1.1356	0.3384	265	1.1062	1246791
1.1629	0.3447	270	1.1061	1270607
1.0603	0.3511	275	1.1057	1295607
1.0018	0.3575	280	1.1050	1318736
0.9956	0.3639	285	1.1039	1343818
1.1563	0.3703	290	1.1035	1367218
1.1728	0.3767	295	1.1023	1391241
0.9051	0.3831	300	1.1017	1414764
1.1777	0.3894	305	1.1016	1437611
1.0734	0.3958	310	1.1013	1462929
1.1101	0.4022	315	1.1014	1485389
1.0641	0.4086	320	1.1017	1509636
1.3749	0.4150	325	1.1009	1533472
1.1226	0.4214	330	1.1015	1557156
1.1453	0.4277	335	1.1011	1581917
1.0588	0.4341	340	1.1006	1607013
1.0858	0.4405	345	1.0997	1630158
1.0605	0.4469	350	1.0999	1654986
1.1787	0.4533	355	1.0997	1676113
1.3256	0.4597	360	1.1002	1698098
1.1953	0.4660	365	1.0997	1722703
1.1637	0.4724	370	1.0995	1745434
1.2143	0.4788	375	1.0984	1769533
1.1001	0.4852	380	1.0974	1792806
1.4728	0.4916	385	1.0966	1815262
1.0669	0.4980	390	1.0963	1839623
1.1078	0.5043	395	1.0957	1862778
0.9767	0.5107	400	1.0954	1885981
1.1935	0.5171	405	1.0947	1909881
1.1738	0.5235	410	1.0939	1934582
1.2397	0.5299	415	1.0932	1956521
1.2951	0.5363	420	1.0932	1978924
1.1479	0.5427	425	1.0938	2001534
1.1575	0.5490	430	1.0943	2025410
1.2199	0.5554	435	1.0951	2050581
1.165	0.5618	440	1.0946	2074594
1.1707	0.5682	445	1.0949	2098673
1.0508	0.5746	450	1.0947	2122179
1.1388	0.5810	455	1.0939	2145717
1.1159	0.5873	460	1.0937	2169427
1.0262	0.5937	465	1.0929	2193045
1.0435	0.6001	470	1.0922	2216826
1.2039	0.6065	475	1.0918	2240295
0.9492	0.6129	480	1.0920	2264093
1.1211	0.6193	485	1.0918	2287029
1.0321	0.6256	490	1.0920	2312770
1.2877	0.6320	495	1.0914	2336451
1.1001	0.6384	500	1.0909	2360838
1.2017	0.6448	505	1.0910	2383857
1.0557	0.6512	510	1.0902	2408788
1.2581	0.6576	515	1.0895	2433861
1.0727	0.6640	520	1.0887	2458751
1.1049	0.6703	525	1.0882	2484073
1.2317	0.6767	530	1.0876	2508316
1.1933	0.6831	535	1.0872	2532348
0.9261	0.6895	540	1.0877	2557057
1.0791	0.6959	545	1.0876	2582077
1.1928	0.7023	550	1.0872	2604928
1.1056	0.7086	555	1.0863	2629183
1.0939	0.7150	560	1.0862	2652827
1.0259	0.7214	565	1.0862	2677889
1.1074	0.7278	570	1.0861	2702876
1.123	0.7342	575	1.0860	2727534
1.1243	0.7406	580	1.0862	2751372
1.1621	0.7469	585	1.0864	2774828
0.9925	0.7533	590	1.0862	2797716
1.1107	0.7597	595	1.0861	2822965
1.0294	0.7661	600	1.0856	2845701
1.1406	0.7725	605	1.0855	2868733
1.0496	0.7789	610	1.0850	2892043
1.1144	0.7853	615	1.0846	2916380
1.0956	0.7916	620	1.0844	2941071
1.0265	0.7980	625	1.0844	2963428
1.0174	0.8044	630	1.0852	2985617
1.0111	0.8108	635	1.0861	3010035
1.1138	0.8172	640	1.0853	3032519
1.1175	0.8236	645	1.0846	3055334
1.1166	0.8299	650	1.0843	3079941
1.0502	0.8363	655	1.0838	3102842
1.0915	0.8427	660	1.0836	3126948
1.2649	0.8491	665	1.0833	3150472
1.1139	0.8555	670	1.0831	3174117
1.3086	0.8619	675	1.0823	3196631
1.1299	0.8682	680	1.0827	3222539
1.0953	0.8746	685	1.0829	3245099
0.9782	0.8810	690	1.0828	3269453
1.3343	0.8874	695	1.0822	3293633
1.1853	0.8938	700	1.0813	3318636
1.0825	0.9002	705	1.0810	3341693
0.9452	0.9066	710	1.0809	3366980
0.9782	0.9129	715	1.0811	3389946
1.0891	0.9193	720	1.0810	3413286
1.1592	0.9257	725	1.0805	3436799
1.0143	0.9321	730	1.0811	3460946
1.0841	0.9385	735	1.0810	3484781
1.1517	0.9449	740	1.0814	3508692
1.1318	0.9512	745	1.0818	3530582
1.3033	0.9576	750	1.0819	3554090
1.1029	0.9640	755	1.0813	3579863
1.1691	0.9704	760	1.0809	3603588
0.9389	0.9768	765	1.0811	3628172
1.0748	0.9832	770	1.0808	3651624
1.1557	0.9895	775	1.0811	3675346
1.0327	0.9959	780	1.0808	3700534

Framework versions

Transformers 4.44.0
Pytorch 2.4.0+cu121
Datasets 2.20.0
Tokenizers 0.19.1

leonat3t/collapse_gemma-2-2b_hs2_massive_iter1_sftsd1

collapse_gemma-2-2b_hs2_massive_iter1_sftsd1

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

leonat3t/
collapse_gemma-2-2b_hs2_massive_iter1_sftsd1