This model is a fine-tuned version of jeff31415/TinyLlama-1.1B-1T-OpenOrca on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 1.1667

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: 3e-07
train_batch_size: 20
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 40
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss
2.6285	0.0	4	2.4698
2.6222	0.0	8	2.4660
2.6536	0.0	12	2.4604
2.6785	0.01	16	2.4557
2.6085	0.01	20	2.4515
2.5907	0.01	24	2.4463
2.5942	0.01	28	2.4415
2.6101	0.01	32	2.4372
2.5827	0.01	36	2.4327
2.5729	0.02	40	2.4281
2.5856	0.02	44	2.4231
2.589	0.02	48	2.4186
2.6483	0.02	52	2.4145
2.517	0.02	56	2.4095
2.5987	0.02	60	2.4050
2.5489	0.03	64	2.4004
2.5063	0.03	68	2.3965
2.5867	0.03	72	2.3916
2.544	0.03	76	2.3873
2.5596	0.03	80	2.3828
2.5744	0.03	84	2.3786
2.5588	0.04	88	2.3742
2.5579	0.04	92	2.3702
2.5245	0.04	96	2.3660
2.5132	0.04	100	2.3611
2.5037	0.04	104	2.3570
2.4727	0.04	108	2.3531
2.4709	0.04	112	2.3488
2.4711	0.05	116	2.3445
2.5445	0.05	120	2.3402
2.4994	0.05	124	2.3362
2.5036	0.05	128	2.3319
2.5525	0.05	132	2.3277
2.5245	0.05	136	2.3241
2.4946	0.06	140	2.3198
2.5142	0.06	144	2.3153
2.4639	0.06	148	2.3113
2.4796	0.06	152	2.3070
2.4529	0.06	156	2.3031
2.4823	0.06	160	2.2993
2.4439	0.07	164	2.2948
2.4652	0.07	168	2.2909
2.4574	0.07	172	2.2867
2.4557	0.07	176	2.2830
2.4462	0.07	180	2.2787
2.3962	0.07	184	2.2745
2.3666	0.08	188	2.2706
2.5024	0.08	192	2.2670
2.4419	0.08	196	2.2627
2.4246	0.08	200	2.2584
2.3853	0.08	204	2.2552
2.4032	0.08	208	2.2511
2.4444	0.08	212	2.2470
2.2932	0.09	216	2.2428
2.3929	0.09	220	2.2391
2.4112	0.09	224	2.2350
2.4191	0.09	228	2.2311
2.4408	0.09	232	2.2272
2.3884	0.09	236	2.2234
2.3689	0.1	240	2.2196
2.3689	0.1	244	2.2154
2.3249	0.1	248	2.2114
2.4286	0.1	252	2.2078
2.3497	0.1	256	2.2039
2.284	0.1	260	2.1999
2.3333	0.11	264	2.1958
2.3305	0.11	268	2.1921
2.3465	0.11	272	2.1882
2.3274	0.11	276	2.1841
2.3641	0.11	280	2.1803
2.3089	0.11	284	2.1763
2.2645	0.12	288	2.1721
2.3439	0.12	292	2.1687
2.3285	0.12	296	2.1649
2.3126	0.12	300	2.1604
2.3356	0.12	304	2.1570
2.3396	0.12	308	2.1527
2.2972	0.12	312	2.1487
2.3321	0.13	316	2.1450
2.3348	0.13	320	2.1415
2.2728	0.13	324	2.1374
2.287	0.13	328	2.1334
2.2474	0.13	332	2.1298
2.3214	0.13	336	2.1264
2.2725	0.14	340	2.1223
2.3114	0.14	344	2.1183
2.2333	0.14	348	2.1146
2.2812	0.14	352	2.1107
2.2454	0.14	356	2.1069
2.2261	0.14	360	2.1032
2.2841	0.15	364	2.0989
2.2481	0.15	368	2.0952
2.278	0.15	372	2.0912
2.2765	0.15	376	2.0873
2.2232	0.15	380	2.0833
2.306	0.15	384	2.0795
2.2126	0.16	388	2.0760
2.2557	0.16	392	2.0721
2.1988	0.16	396	2.0684
2.1917	0.16	400	2.0639
2.2479	0.16	404	2.0599
2.1484	0.16	408	2.0558
2.1886	0.16	412	2.0521
2.2026	0.17	416	2.0482
2.1572	0.17	420	2.0442
2.1931	0.17	424	2.0400
2.161	0.17	428	2.0365
2.1115	0.17	432	2.0322
2.173	0.17	436	2.0284
2.1491	0.18	440	2.0247
2.1716	0.18	444	2.0204
2.2031	0.18	448	2.0165
2.1466	0.18	452	2.0126
2.1384	0.18	456	2.0088
2.1824	0.18	460	2.0048
2.1401	0.19	464	2.0006
2.2086	0.19	468	1.9969
2.1687	0.19	472	1.9926
2.145	0.19	476	1.9888
2.2007	0.19	480	1.9850
2.1367	0.19	484	1.9808
2.1291	0.2	488	1.9768
2.1124	0.2	492	1.9729
2.0738	0.2	496	1.9689
2.1048	0.2	500	1.9646
2.0995	0.2	504	1.9607
2.0816	0.2	508	1.9568
2.0969	0.2	512	1.9527
2.1034	0.21	516	1.9484
2.0654	0.21	520	1.9442
2.1175	0.21	524	1.9404
2.0829	0.21	528	1.9363
2.0973	0.21	532	1.9322
2.0439	0.21	536	1.9278
2.0791	0.22	540	1.9239
2.0988	0.22	544	1.9203
2.0179	0.22	548	1.9160
2.0452	0.22	552	1.9119
1.9792	0.22	556	1.9079
1.9862	0.22	560	1.9032
2.0176	0.23	564	1.8994
2.0066	0.23	568	1.8954
2.0333	0.23	572	1.8914
2.0316	0.23	576	1.8871
2.0114	0.23	580	1.8828
2.0093	0.23	584	1.8789
1.9829	0.24	588	1.8749
1.971	0.24	592	1.8706
2.0188	0.24	596	1.8668
2.0081	0.24	600	1.8628
2.0014	0.24	604	1.8587
1.9741	0.24	608	1.8544
1.9828	0.24	612	1.8505
1.9481	0.25	616	1.8463
1.9584	0.25	620	1.8424
1.9449	0.25	624	1.8381
1.9753	0.25	628	1.8343
2.0055	0.25	632	1.8300
1.98	0.25	636	1.8261
1.9757	0.26	640	1.8223
1.9683	0.26	644	1.8182
1.926	0.26	648	1.8141
1.9454	0.26	652	1.8101
1.9352	0.26	656	1.8059
1.8816	0.26	660	1.8021
1.9182	0.27	664	1.7980
1.9659	0.27	668	1.7941
1.8932	0.27	672	1.7901
1.8608	0.27	676	1.7861
1.941	0.27	680	1.7824
1.8854	0.27	684	1.7785
1.8912	0.28	688	1.7743
1.8667	0.28	692	1.7707
1.912	0.28	696	1.7666
1.9009	0.28	700	1.7628
1.906	0.28	704	1.7589
1.8671	0.28	708	1.7550
1.8609	0.28	712	1.7508
1.8485	0.29	716	1.7469
1.8334	0.29	720	1.7431
1.8763	0.29	724	1.7392
1.9005	0.29	728	1.7355
1.8669	0.29	732	1.7315
1.8984	0.29	736	1.7276
1.8074	0.3	740	1.7241
1.8614	0.3	744	1.7202
1.8211	0.3	748	1.7165
1.8553	0.3	752	1.7124
1.7978	0.3	756	1.7085
1.8203	0.3	760	1.7048
1.8192	0.31	764	1.7010
1.8532	0.31	768	1.6974
1.8307	0.31	772	1.6935
1.8207	0.31	776	1.6896
1.7895	0.31	780	1.6858
1.7976	0.31	784	1.6821
1.814	0.32	788	1.6785
1.7972	0.32	792	1.6748
1.8258	0.32	796	1.6714
1.79	0.32	800	1.6674
1.802	0.32	804	1.6640
1.7784	0.32	808	1.6603
1.7671	0.32	812	1.6569
1.7618	0.33	816	1.6535
1.8207	0.33	820	1.6503
1.7837	0.33	824	1.6467
1.8066	0.33	828	1.6439
1.7814	0.33	832	1.6407
1.7244	0.33	836	1.6373
1.7195	0.34	840	1.6342
1.7524	0.34	844	1.6310
1.7644	0.34	848	1.6279
1.7171	0.34	852	1.6245
1.7418	0.34	856	1.6212
1.7337	0.34	860	1.6180
1.7441	0.35	864	1.6148
1.694	0.35	868	1.6115
1.7601	0.35	872	1.6083
1.7081	0.35	876	1.6050
1.7101	0.35	880	1.6020
1.7271	0.35	884	1.5990
1.7402	0.36	888	1.5954
1.7125	0.36	892	1.5922
1.6949	0.36	896	1.5888
1.7145	0.36	900	1.5858
1.6665	0.36	904	1.5824
1.6929	0.36	908	1.5796
1.7068	0.36	912	1.5766
1.6877	0.37	916	1.5734
1.6718	0.37	920	1.5706
1.6886	0.37	924	1.5676
1.7459	0.37	928	1.5646
1.6596	0.37	932	1.5617
1.6689	0.37	936	1.5588
1.6744	0.38	940	1.5561
1.7009	0.38	944	1.5533
1.6651	0.38	948	1.5505
1.6821	0.38	952	1.5479
1.6453	0.38	956	1.5453
1.6624	0.38	960	1.5426
1.6453	0.39	964	1.5402
1.6451	0.39	968	1.5377
1.6627	0.39	972	1.5353
1.6423	0.39	976	1.5326
1.652	0.39	980	1.5302
1.6414	0.39	984	1.5278
1.6107	0.4	988	1.5253
1.6599	0.4	992	1.5225
1.6326	0.4	996	1.5202
1.6324	0.4	1000	1.5175
1.5907	0.4	1004	1.5149
1.6465	0.4	1008	1.5124
1.6148	0.4	1012	1.5102
1.6064	0.41	1016	1.5074
1.6342	0.41	1020	1.5053
1.605	0.41	1024	1.5025
1.6121	0.41	1028	1.5003
1.617	0.41	1032	1.4978
1.5897	0.41	1036	1.4955
1.6022	0.42	1040	1.4930
1.5748	0.42	1044	1.4903
1.5974	0.42	1048	1.4879
1.6126	0.42	1052	1.4855
1.6189	0.42	1056	1.4828
1.5916	0.42	1060	1.4804
1.5938	0.43	1064	1.4779
1.6026	0.43	1068	1.4756
1.5687	0.43	1072	1.4735
1.5413	0.43	1076	1.4712
1.5778	0.43	1080	1.4689
1.5731	0.43	1084	1.4664
1.5625	0.44	1088	1.4642
1.55	0.44	1092	1.4621
1.5852	0.44	1096	1.4599
1.5614	0.44	1100	1.4579
1.5619	0.44	1104	1.4560
1.5658	0.44	1108	1.4542
1.5699	0.44	1112	1.4521
1.5738	0.45	1116	1.4502
1.5823	0.45	1120	1.4481
1.5425	0.45	1124	1.4459
1.5604	0.45	1128	1.4438
1.5562	0.45	1132	1.4420
1.555	0.45	1136	1.4399
1.5158	0.46	1140	1.4380
1.5272	0.46	1144	1.4359
1.5467	0.46	1148	1.4339
1.5399	0.46	1152	1.4317
1.5221	0.46	1156	1.4297
1.5022	0.46	1160	1.4277
1.5385	0.47	1164	1.4257
1.5042	0.47	1168	1.4236
1.5007	0.47	1172	1.4217
1.5323	0.47	1176	1.4196
1.5269	0.47	1180	1.4174
1.5379	0.47	1184	1.4156
1.522	0.48	1188	1.4137
1.506	0.48	1192	1.4116
1.4986	0.48	1196	1.4096
1.4918	0.48	1200	1.4075
1.5124	0.48	1204	1.4056
1.4926	0.48	1208	1.4038
1.5053	0.48	1212	1.4015
1.5043	0.49	1216	1.3996
1.5068	0.49	1220	1.3976
1.5039	0.49	1224	1.3955
1.4772	0.49	1228	1.3933
1.4873	0.49	1232	1.3916
1.4977	0.49	1236	1.3896
1.5016	0.5	1240	1.3873
1.495	0.5	1244	1.3854
1.4803	0.5	1248	1.3835
1.4842	0.5	1252	1.3816
1.4762	0.5	1256	1.3799
1.4859	0.5	1260	1.3781
1.4948	0.51	1264	1.3764
1.4851	0.51	1268	1.3743
1.4749	0.51	1272	1.3724
1.4594	0.51	1276	1.3709
1.4517	0.51	1280	1.3692
1.4239	0.51	1284	1.3673
1.4775	0.52	1288	1.3657
1.4483	0.52	1292	1.3643
1.4688	0.52	1296	1.3624
1.4566	0.52	1300	1.3608
1.4592	0.52	1304	1.3592
1.4505	0.52	1308	1.3573
1.4304	0.52	1312	1.3557
1.4691	0.53	1316	1.3541
1.4423	0.53	1320	1.3522
1.4301	0.53	1324	1.3508
1.4422	0.53	1328	1.3491
1.4577	0.53	1332	1.3475
1.4541	0.53	1336	1.3458
1.4246	0.54	1340	1.3440
1.4507	0.54	1344	1.3424
1.4312	0.54	1348	1.3408
1.4394	0.54	1352	1.3392
1.4271	0.54	1356	1.3374
1.4081	0.54	1360	1.3357
1.4314	0.55	1364	1.3339
1.4359	0.55	1368	1.3326
1.4381	0.55	1372	1.3308
1.4219	0.55	1376	1.3294
1.4669	0.55	1380	1.3279
1.4163	0.55	1384	1.3260
1.4153	0.56	1388	1.3242
1.4506	0.56	1392	1.3230
1.4229	0.56	1396	1.3213
1.4218	0.56	1400	1.3196
1.4185	0.56	1404	1.3181
1.4283	0.56	1408	1.3164
1.4202	0.56	1412	1.3148
1.3736	0.57	1416	1.3131
1.4332	0.57	1420	1.3116
1.4287	0.57	1424	1.3099
1.4175	0.57	1428	1.3081
1.4152	0.57	1432	1.3066
1.4036	0.57	1436	1.3054
1.4033	0.58	1440	1.3038
1.4095	0.58	1444	1.3023
1.4129	0.58	1448	1.3008
1.3838	0.58	1452	1.2995
1.3939	0.58	1456	1.2980
1.4023	0.58	1460	1.2964
1.3751	0.59	1464	1.2953
1.3657	0.59	1468	1.2935
1.375	0.59	1472	1.2927
1.3846	0.59	1476	1.2911
1.4192	0.59	1480	1.2901
1.3629	0.59	1484	1.2886
1.3947	0.6	1488	1.2877
1.3485	0.6	1492	1.2863
1.405	0.6	1496	1.2850
1.3758	0.6	1500	1.2841
1.3832	0.6	1504	1.2828
1.3314	0.6	1508	1.2814
1.3458	0.6	1512	1.2800
1.357	0.61	1516	1.2792
1.3808	0.61	1520	1.2778
1.3692	0.61	1524	1.2765
1.3763	0.61	1528	1.2754
1.3505	0.61	1532	1.2741
1.3579	0.61	1536	1.2731
1.3567	0.62	1540	1.2717
1.3608	0.62	1544	1.2705
1.3895	0.62	1548	1.2692
1.3614	0.62	1552	1.2682
1.3643	0.62	1556	1.2672
1.3537	0.62	1560	1.2658
1.3564	0.63	1564	1.2648
1.3433	0.63	1568	1.2634
1.3805	0.63	1572	1.2625
1.3309	0.63	1576	1.2612
1.3408	0.63	1580	1.2600
1.3505	0.63	1584	1.2591
1.355	0.64	1588	1.2582
1.3426	0.64	1592	1.2570
1.3476	0.64	1596	1.2556
1.3448	0.64	1600	1.2546
1.3702	0.64	1604	1.2532
1.3198	0.64	1608	1.2522
1.3565	0.64	1612	1.2511
1.3496	0.65	1616	1.2499
1.3346	0.65	1620	1.2490
1.3097	0.65	1624	1.2477
1.3224	0.65	1628	1.2468
1.321	0.65	1632	1.2455
1.3069	0.65	1636	1.2446
1.3358	0.66	1640	1.2436
1.3413	0.66	1644	1.2427
1.3328	0.66	1648	1.2416
1.341	0.66	1652	1.2406
1.3022	0.66	1656	1.2396
1.3309	0.66	1660	1.2386
1.3099	0.67	1664	1.2377
1.2979	0.67	1668	1.2368
1.3219	0.67	1672	1.2358
1.328	0.67	1676	1.2349
1.3161	0.67	1680	1.2339
1.3435	0.67	1684	1.2329
1.3191	0.68	1688	1.2323
1.3427	0.68	1692	1.2314
1.3157	0.68	1696	1.2307
1.31	0.68	1700	1.2298
1.3418	0.68	1704	1.2287
1.3051	0.68	1708	1.2280
1.3453	0.68	1712	1.2271
1.3146	0.69	1716	1.2261
1.2961	0.69	1720	1.2255
1.2989	0.69	1724	1.2248
1.314	0.69	1728	1.2239
1.3137	0.69	1732	1.2232
1.323	0.69	1736	1.2221
1.3194	0.7	1740	1.2216
1.2857	0.7	1744	1.2206
1.3101	0.7	1748	1.2199
1.2962	0.7	1752	1.2194
1.2927	0.7	1756	1.2186
1.2728	0.7	1760	1.2179
1.2903	0.71	1764	1.2170
1.3108	0.71	1768	1.2164
1.2899	0.71	1772	1.2156
1.2996	0.71	1776	1.2149
1.2865	0.71	1780	1.2141
1.3004	0.71	1784	1.2135
1.2916	0.72	1788	1.2128
1.2975	0.72	1792	1.2119
1.3071	0.72	1796	1.2114
1.2793	0.72	1800	1.2106
1.2755	0.72	1804	1.2098
1.2968	0.72	1808	1.2093
1.3226	0.72	1812	1.2085
1.3117	0.73	1816	1.2079
1.2957	0.73	1820	1.2073
1.2885	0.73	1824	1.2066
1.2731	0.73	1828	1.2057
1.2821	0.73	1832	1.2051
1.2944	0.73	1836	1.2045
1.2768	0.74	1840	1.2040
1.2917	0.74	1844	1.2034
1.2935	0.74	1848	1.2027
1.268	0.74	1852	1.2023
1.2761	0.74	1856	1.2016
1.3107	0.74	1860	1.2010
1.2752	0.75	1864	1.2004
1.2661	0.75	1868	1.1997
1.2985	0.75	1872	1.1991
1.2801	0.75	1876	1.1985
1.2775	0.75	1880	1.1980
1.2741	0.75	1884	1.1976
1.2747	0.76	1888	1.1972
1.2772	0.76	1892	1.1964
1.2953	0.76	1896	1.1961
1.3052	0.76	1900	1.1958
1.2505	0.76	1904	1.1951
1.3088	0.76	1908	1.1945
1.2705	0.76	1912	1.1939
1.2606	0.77	1916	1.1934
1.2729	0.77	1920	1.1932
1.2642	0.77	1924	1.1927
1.2903	0.77	1928	1.1920
1.2688	0.77	1932	1.1918
1.2677	0.77	1936	1.1909
1.2747	0.78	1940	1.1905
1.2512	0.78	1944	1.1902
1.2651	0.78	1948	1.1898
1.2655	0.78	1952	1.1893
1.2617	0.78	1956	1.1888
1.2764	0.78	1960	1.1885
1.2531	0.79	1964	1.1883
1.2911	0.79	1968	1.1874
1.2616	0.79	1972	1.1871
1.2537	0.79	1976	1.1869
1.2548	0.79	1980	1.1864
1.2722	0.79	1984	1.1861
1.2717	0.8	1988	1.1858
1.281	0.8	1992	1.1854
1.2766	0.8	1996	1.1850
1.2962	0.8	2000	1.1846
1.2732	0.8	2004	1.1842
1.2588	0.8	2008	1.1838
1.2651	0.8	2012	1.1836
1.2755	0.81	2016	1.1833
1.2513	0.81	2020	1.1828
1.2645	0.81	2024	1.1824
1.2593	0.81	2028	1.1819
1.2947	0.81	2032	1.1818
1.2599	0.81	2036	1.1813
1.2094	0.82	2040	1.1809
1.2707	0.82	2044	1.1807
1.2653	0.82	2048	1.1803
1.2637	0.82	2052	1.1799
1.2708	0.82	2056	1.1799
1.283	0.82	2060	1.1794
1.2853	0.83	2064	1.1792
1.2617	0.83	2068	1.1792
1.2476	0.83	2072	1.1788
1.2355	0.83	2076	1.1785
1.2348	0.83	2080	1.1781
1.2468	0.83	2084	1.1780
1.2715	0.84	2088	1.1776
1.2502	0.84	2092	1.1772
1.284	0.84	2096	1.1771
1.2417	0.84	2100	1.1768
1.2516	0.84	2104	1.1766
1.2748	0.84	2108	1.1763
1.2744	0.84	2112	1.1762
1.2551	0.85	2116	1.1757
1.2687	0.85	2120	1.1755
1.2575	0.85	2124	1.1754
1.2501	0.85	2128	1.1749
1.2728	0.85	2132	1.1751
1.2411	0.85	2136	1.1750
1.2505	0.86	2140	1.1744
1.2363	0.86	2144	1.1743
1.2408	0.86	2148	1.1741
1.25	0.86	2152	1.1736
1.2729	0.86	2156	1.1735
1.2467	0.86	2160	1.1735
1.2377	0.87	2164	1.1734
1.2876	0.87	2168	1.1729
1.255	0.87	2172	1.1728
1.2471	0.87	2176	1.1724
1.2641	0.87	2180	1.1726
1.2594	0.87	2184	1.1722
1.2803	0.88	2188	1.1719
1.2404	0.88	2192	1.1718
1.2538	0.88	2196	1.1718
1.2423	0.88	2200	1.1716
1.2344	0.88	2204	1.1713
1.2646	0.88	2208	1.1711
1.2501	0.88	2212	1.1712
1.2455	0.89	2216	1.1709
1.2588	0.89	2220	1.1705
1.2629	0.89	2224	1.1706
1.2572	0.89	2228	1.1706
1.2625	0.89	2232	1.1704
1.2479	0.89	2236	1.1700
1.2698	0.9	2240	1.1701
1.2619	0.9	2244	1.1699
1.2455	0.9	2248	1.1696
1.2523	0.9	2252	1.1696
1.2695	0.9	2256	1.1692
1.258	0.9	2260	1.1691
1.2393	0.91	2264	1.1691
1.2567	0.91	2268	1.1690
1.2373	0.91	2272	1.1688
1.2797	0.91	2276	1.1687
1.2646	0.91	2280	1.1686
1.2502	0.91	2284	1.1686
1.2317	0.92	2288	1.1684
1.2443	0.92	2292	1.1683
1.2563	0.92	2296	1.1683
1.2485	0.92	2300	1.1683
1.2542	0.92	2304	1.1682
1.2381	0.92	2308	1.1679
1.2244	0.92	2312	1.1679
1.2725	0.93	2316	1.1678
1.2441	0.93	2320	1.1677
1.2441	0.93	2324	1.1678
1.2546	0.93	2328	1.1676
1.2279	0.93	2332	1.1674
1.2635	0.93	2336	1.1675
1.2572	0.94	2340	1.1673
1.2421	0.94	2344	1.1673
1.2022	0.94	2348	1.1671
1.2307	0.94	2352	1.1670
1.2525	0.94	2356	1.1670
1.2353	0.94	2360	1.1671
1.25	0.95	2364	1.1672
1.2493	0.95	2368	1.1670
1.2453	0.95	2372	1.1670
1.2714	0.95	2376	1.1669
1.2435	0.95	2380	1.1668
1.2518	0.95	2384	1.1668
1.2594	0.96	2388	1.1669
1.2149	0.96	2392	1.1670
1.2676	0.96	2396	1.1667
1.2337	0.96	2400	1.1669
1.2329	0.96	2404	1.1666
1.269	0.96	2408	1.1667
1.2298	0.96	2412	1.1665
1.2481	0.97	2416	1.1667
1.2674	0.97	2420	1.1668
1.2482	0.97	2424	1.1666
1.2604	0.97	2428	1.1667
1.2471	0.97	2432	1.1666
1.2069	0.97	2436	1.1665
1.2734	0.98	2440	1.1667
1.239	0.98	2444	1.1666
1.2245	0.98	2448	1.1664
1.244	0.98	2452	1.1664
1.2458	0.98	2456	1.1666
1.2566	0.98	2460	1.1665
1.2582	0.99	2464	1.1667
1.2312	0.99	2468	1.1667
1.229	0.99	2472	1.1666
1.2186	0.99	2476	1.1667
1.2423	0.99	2480	1.1665
1.2465	0.99	2484	1.1665
1.2332	1.0	2488	1.1663
1.2466	1.0	2492	1.1665
1.2308	1.0	2496	1.1665
1.2435	1.0	2500	1.1667

Framework versions

Transformers 4.36.0.dev0
Pytorch 2.1.0+cu118
Datasets 2.15.1.dev0
Tokenizers 0.15.0

RichardErkhov/marcchew_-_TinyLLaMA-1.1B-OrcaLaMini-awq

Original model description:

results

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

RichardErkhov/
marcchew_-_TinyLLaMA-1.1B-OrcaLaMini-awq