Training Loss	Epoch	Step	Validation Loss	Input Tokens Seen
No log	0	0	1.7073	0
1.5857	0.0062	5	1.7053	23300
1.8106	0.0124	10	1.6941	47874
1.8587	0.0186	15	1.6661	71706
1.6486	0.0249	20	1.6413	94936
1.6528	0.0311	25	1.6197	118328
2.0148	0.0373	30	1.6041	141621
1.6023	0.0435	35	1.5928	164812
1.8049	0.0497	40	1.5828	186651
1.767	0.0559	45	1.5749	210247
1.5168	0.0621	50	1.5688	235362
1.5112	0.0683	55	1.5622	259244
1.6517	0.0746	60	1.5556	284021
1.4636	0.0808	65	1.5508	308263
1.4326	0.0870	70	1.5480	331473
1.444	0.0932	75	1.5442	353788
1.3971	0.0994	80	1.5412	375966
1.51	0.1056	85	1.5385	399868
1.5155	0.1118	90	1.5370	423128
1.4193	0.1180	95	1.5341	447885
1.2897	0.1243	100	1.5324	471399
1.4588	0.1305	105	1.5307	495012
1.5574	0.1367	110	1.5299	517729
1.7028	0.1429	115	1.5279	542317
1.568	0.1491	120	1.5268	566921
1.5237	0.1553	125	1.5251	591023
1.5687	0.1615	130	1.5243	614063
1.5193	0.1678	135	1.5232	637502
1.6782	0.1740	140	1.5225	660381
1.6261	0.1802	145	1.5222	682517
1.9404	0.1864	150	1.5214	706626
1.6949	0.1926	155	1.5208	730516
1.455	0.1988	160	1.5207	751866
1.5505	0.2050	165	1.5198	775126
1.4864	0.2112	170	1.5192	800076
1.4181	0.2175	175	1.5185	822660
1.5358	0.2237	180	1.5178	846012
1.5728	0.2299	185	1.5173	868518
1.6226	0.2361	190	1.5171	894171
1.5347	0.2423	195	1.5156	916089
1.7092	0.2485	200	1.5150	941344
1.5603	0.2547	205	1.5149	965070
1.5135	0.2610	210	1.5149	989228
1.5304	0.2672	215	1.5147	1013620
1.484	0.2734	220	1.5137	1037957
1.4788	0.2796	225	1.5127	1060662
1.6321	0.2858	230	1.5125	1081845
1.3889	0.2920	235	1.5122	1106139
1.6262	0.2982	240	1.5120	1129157
1.4513	0.3044	245	1.5119	1151754
1.4507	0.3107	250	1.5118	1176086
1.5862	0.3169	255	1.5115	1197404
1.4663	0.3231	260	1.5114	1220930
1.3265	0.3293	265	1.5108	1245942
1.7759	0.3355	270	1.5105	1269235
1.4142	0.3417	275	1.5103	1292720
1.5926	0.3479	280	1.5108	1315967
1.5418	0.3541	285	1.5106	1339270
1.4373	0.3604	290	1.5098	1361654
1.4238	0.3666	295	1.5088	1385145
1.2556	0.3728	300	1.5078	1408708
1.4221	0.3790	305	1.5077	1432359
1.7001	0.3852	310	1.5069	1456318
1.6247	0.3914	315	1.5068	1479297
1.5889	0.3976	320	1.5067	1502406
1.4804	0.4039	325	1.5064	1528068
1.5793	0.4101	330	1.5072	1551849
1.5539	0.4163	335	1.5059	1574559
1.461	0.4225	340	1.5058	1597105
1.6384	0.4287	345	1.5050	1620541
1.5942	0.4349	350	1.5048	1644901
1.5969	0.4411	355	1.5045	1666194
1.5441	0.4473	360	1.5044	1689630
1.4234	0.4536	365	1.5044	1711945
1.3431	0.4598	370	1.5043	1735454
1.6438	0.4660	375	1.5035	1758058
1.7625	0.4722	380	1.5035	1780123
1.6344	0.4784	385	1.5032	1803384
1.6887	0.4846	390	1.5032	1826097
1.375	0.4908	395	1.5034	1852360
1.8561	0.4970	400	1.5026	1874675
1.5276	0.5033	405	1.5027	1897842
1.6289	0.5095	410	1.5026	1922419
1.4689	0.5157	415	1.5024	1945363
1.3927	0.5219	420	1.5019	1970007
1.4405	0.5281	425	1.5022	1991023
1.5261	0.5343	430	1.5019	2016228
1.5771	0.5405	435	1.5015	2041353
1.4124	0.5468	440	1.5015	2065027
1.4762	0.5530	445	1.5013	2091829
1.5122	0.5592	450	1.5010	2115407
1.4373	0.5654	455	1.5008	2139660
1.612	0.5716	460	1.5004	2164556
1.6831	0.5778	465	1.5000	2187240
1.5719	0.5840	470	1.5003	2212862
1.7223	0.5902	475	1.4999	2237592
1.4884	0.5965	480	1.5000	2262463
1.4808	0.6027	485	1.5000	2286885
1.307	0.6089	490	1.4995	2310159
1.2725	0.6151	495	1.4994	2333938
1.508	0.6213	500	1.4989	2358667
1.6033	0.6275	505	1.4993	2381569
1.6089	0.6337	510	1.4992	2403765
1.6569	0.6400	515	1.4995	2428681
1.4853	0.6462	520	1.4990	2454512
1.497	0.6524	525	1.4988	2478841
1.3238	0.6586	530	1.4987	2501506
1.6703	0.6648	535	1.4983	2524441
1.5186	0.6710	540	1.4983	2549457
1.4901	0.6772	545	1.4978	2573433
1.6876	0.6834	550	1.4978	2597019
1.6325	0.6897	555	1.4978	2619898
1.5188	0.6959	560	1.4975	2643485
1.4817	0.7021	565	1.4977	2666821
1.3517	0.7083	570	1.4974	2690888
1.5985	0.7145	575	1.4976	2714259
1.359	0.7207	580	1.4973	2738384
1.4631	0.7269	585	1.4970	2763943
1.4704	0.7331	590	1.4972	2787038
1.4088	0.7394	595	1.4973	2808517
1.5307	0.7456	600	1.4970	2831707
1.7859	0.7518	605	1.4969	2856427
1.6914	0.7580	610	1.4969	2878583
1.2985	0.7642	615	1.4963	2903153
1.617	0.7704	620	1.4963	2925534
1.5944	0.7766	625	1.4964	2950787
1.5926	0.7829	630	1.4962	2972737
1.6237	0.7891	635	1.4957	2996398
1.9244	0.7953	640	1.4954	3019984
1.5161	0.8015	645	1.4952	3044136
1.4924	0.8077	650	1.4950	3066991
1.5001	0.8139	655	1.4951	3089873
1.6334	0.8201	660	1.4948	3113511
1.467	0.8263	665	1.4942	3136553
1.3522	0.8326	670	1.4946	3158754
1.6078	0.8388	675	1.4945	3182578
1.4854	0.8450	680	1.4944	3205461
1.3116	0.8512	685	1.4942	3228807
1.4486	0.8574	690	1.4944	3253938
1.4625	0.8636	695	1.4944	3277744
1.4164	0.8698	700	1.4943	3300927
1.9111	0.8760	705	1.4946	3323411
1.68	0.8823	710	1.4943	3347497
1.6438	0.8885	715	1.4935	3370356
1.4979	0.8947	720	1.4933	3392694
1.6394	0.9009	725	1.4933	3417403
1.7865	0.9071	730	1.4932	3440401
1.4014	0.9133	735	1.4935	3464184
1.4851	0.9195	740	1.4932	3487031
1.3159	0.9258	745	1.4926	3510000
1.6518	0.9320	750	1.4929	3531986
1.5457	0.9382	755	1.4925	3554822
1.5792	0.9444	760	1.4924	3580255
1.4561	0.9506	765	1.4923	3604988
1.4633	0.9568	770	1.4923	3629013
1.8603	0.9630	775	1.4924	3652102
1.6592	0.9692	780	1.4922	3675642
1.3376	0.9755	785	1.4921	3699546
1.6504	0.9817	790	1.4919	3722623
1.6749	0.9879	795	1.4921	3745842
1.5117	0.9941	800	1.4920	3769338

Framework versions

Transformers 4.44.0
Pytorch 2.4.0+cu121
Datasets 2.20.0
Tokenizers 0.19.1

leonat3t/collapse_qwen2-0.5b_hs2_replace_iter1_sftsd0

collapse_qwen2-0.5b_hs2_replace_iter1_sftsd0

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

leonat3t/
collapse_qwen2-0.5b_hs2_replace_iter1_sftsd0