Training Loss	Epoch	Step	Validation Loss
1.9667	0.02	1	1.6464
2.4545	0.04	2	1.6360
2.4307	0.06	3	1.6170
2.1502	0.09	4	1.5949
1.8514	0.11	5	1.5724
1.7189	0.13	6	1.5528
1.9036	0.15	7	1.5352
2.0255	0.17	8	1.5151
2.2073	0.19	9	1.4944
2.1577	0.21	10	1.4748
1.6581	0.23	11	1.4545
1.9323	0.26	12	1.4363
1.4871	0.28	13	1.4198
1.574	0.3	14	1.4032
1.7671	0.32	15	1.3898
1.567	0.34	16	1.3782
1.5162	0.36	17	1.3686
1.9622	0.38	18	1.3599
1.8378	0.4	19	1.3525
1.7199	0.43	20	1.3460
1.3917	0.45	21	1.3402
1.4417	0.47	22	1.3345
1.4023	0.49	23	1.3293
1.5427	0.51	24	1.3239
1.2344	0.53	25	1.3192
2.281	0.55	26	1.3136
1.9236	0.57	27	1.3077
1.4392	0.6	28	1.3029
1.9168	0.62	29	1.2976
2.1688	0.64	30	1.2930
1.2504	0.66	31	1.2890
1.5946	0.68	32	1.2853
1.6979	0.7	33	1.2820
1.6712	0.72	34	1.2789
1.7862	0.74	35	1.2759
1.534	0.77	36	1.2734
1.6904	0.79	37	1.2712
1.6023	0.81	38	1.2692
1.6756	0.83	39	1.2667
2.0195	0.85	40	1.2640
1.2913	0.87	41	1.2618
1.1534	0.89	42	1.2607
1.5612	0.91	43	1.2597
1.3159	0.94	44	1.2586
1.6303	0.96	45	1.2582
1.3721	0.98	46	1.2584
2.703	1.0	47	1.2583
1.3063	1.02	48	1.2585
1.1093	1.04	49	1.2594
1.1362	1.06	50	1.2609
1.6691	1.09	51	1.2619
1.44	1.11	52	1.2620
1.8026	1.13	53	1.2612
1.8663	1.15	54	1.2601
1.3662	1.17	55	1.2584
1.7172	1.19	56	1.2562
1.554	1.21	57	1.2535
1.0628	1.23	58	1.2514
1.389	1.26	59	1.2494
1.0307	1.28	60	1.2481
1.5557	1.3	61	1.2462
1.6536	1.32	62	1.2438
1.652	1.34	63	1.2415
1.51	1.36	64	1.2396
1.5407	1.38	65	1.2374
1.6681	1.4	66	1.2349
1.4797	1.43	67	1.2323
1.326	1.45	68	1.2304
1.8683	1.47	69	1.2285
1.3007	1.49	70	1.2270
1.5261	1.51	71	1.2256
1.6908	1.53	72	1.2241
1.4631	1.55	73	1.2226
1.5474	1.57	74	1.2213
1.0559	1.6	75	1.2209
1.5217	1.62	76	1.2206
1.7606	1.64	77	1.2201
1.5246	1.66	78	1.2197
1.8001	1.68	79	1.2192
1.4414	1.7	80	1.2185
1.4168	1.72	81	1.2182
1.2429	1.74	82	1.2180
1.7092	1.77	83	1.2178
1.4605	1.79	84	1.2178
1.2242	1.81	85	1.2180
1.6583	1.83	86	1.2180
1.7079	1.85	87	1.2181
0.9831	1.87	88	1.2186
1.6504	1.89	89	1.2191
1.7244	1.91	90	1.2194
1.2895	1.94	91	1.2196
1.03	1.96	92	1.2201
1.377	1.98	93	1.2205
1.0463	2.0	94	1.2210
1.3759	2.02	95	1.2214
1.7144	2.04	96	1.2218
1.6047	2.06	97	1.2220
1.6515	2.09	98	1.2222
1.2909	2.11	99	1.2221
1.6717	2.13	100	1.2219
1.1318	2.15	101	1.2220
1.3417	2.17	102	1.2219
1.3242	2.19	103	1.2219
1.4135	2.21	104	1.2221
1.3863	2.23	105	1.2222
1.2301	2.26	106	1.2222
1.5481	2.28	107	1.2220
1.0813	2.3	108	1.2219
1.4198	2.32	109	1.2218
1.4751	2.34	110	1.2214
1.4133	2.36	111	1.2212
0.8784	2.38	112	1.2212
1.514	2.4	113	1.2211
1.2913	2.43	114	1.2210
1.1341	2.45	115	1.2209
1.262	2.47	116	1.2210
1.3282	2.49	117	1.2209
1.5217	2.51	118	1.2206
1.6127	2.53	119	1.2203
1.5625	2.55	120	1.2201
1.4603	2.57	121	1.2199
1.8532	2.6	122	1.2196
1.3278	2.62	123	1.2194
1.0632	2.64	124	1.2192
1.5837	2.66	125	1.2190
1.4593	2.68	126	1.2188
1.2919	2.7	127	1.2188
1.1228	2.72	128	1.2187
1.3098	2.74	129	1.2188
1.6073	2.77	130	1.2188
1.1484	2.79	131	1.2189
1.6054	2.81	132	1.2190
1.5228	2.83	133	1.2190
1.5577	2.85	134	1.2190
1.4234	2.87	135	1.2191
1.7341	2.89	136	1.2191
1.6164	2.91	137	1.2190
1.6621	2.94	138	1.2190
1.5781	2.96	139	1.2189
1.0756	2.98	140	1.2189
1.8596	3.0	141	1.2189

Framework versions

Transformers 4.38.1
Pytorch 2.1.0+cu121
Datasets 2.17.0
Tokenizers 0.15.2

mixtralyanis/flant5-tuned-3

flant5-tuned-3

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

mixtralyanis/
flant5-tuned-3