Training Loss	Epoch	Step	Validation Loss
1.5581	0.01	50	0.7223
0.6511	0.02	100	0.6142
0.5881	0.02	150	0.5763
0.5705	0.03	200	0.5529
0.5534	0.04	250	0.5324
0.5168	0.05	300	0.5190
0.5185	0.06	350	0.5079
0.4996	0.06	400	0.4984
0.5031	0.07	450	0.4911
0.4797	0.08	500	0.4840
0.4844	0.09	550	0.4766
0.4789	0.1	600	0.4713
0.467	0.1	650	0.4653
0.461	0.11	700	0.4604
0.4562	0.12	750	0.4547
0.4521	0.13	800	0.4479
0.4298	0.14	850	0.4395
0.4405	0.14	900	0.4316
0.4265	0.15	950	0.4257
0.4335	0.16	1000	0.4212
0.4054	0.17	1050	0.4179
0.4285	0.18	1100	0.4164
0.4235	0.18	1150	0.4136
0.4047	0.19	1200	0.4117
0.416	0.2	1250	0.4090
0.4194	0.21	1300	0.4071
0.4164	0.22	1350	0.4060
0.4069	0.22	1400	0.4035
0.4049	0.23	1450	0.4028
0.4	0.24	1500	0.4002
0.4085	0.25	1550	0.3973
0.4093	0.26	1600	0.3972
0.3875	0.26	1650	0.3944
0.403	0.27	1700	0.3941
0.399	0.28	1750	0.3919
0.3938	0.29	1800	0.3914
0.3958	0.3	1850	0.3891
0.3991	0.3	1900	0.3876
0.3956	0.31	1950	0.3863
0.4004	0.32	2000	0.3853
0.398	0.33	2050	0.3845
0.3998	0.34	2100	0.3832
0.3741	0.34	2150	0.3819
0.382	0.35	2200	0.3808
0.3839	0.36	2250	0.3799
0.3797	0.37	2300	0.3789
0.3783	0.38	2350	0.3784
0.3809	0.39	2400	0.3778
0.3976	0.39	2450	0.3757
0.3877	0.4	2500	0.3753
0.3814	0.41	2550	0.3746
0.3631	0.42	2600	0.3734
0.3803	0.43	2650	0.3726
0.3791	0.43	2700	0.3720
0.3733	0.44	2750	0.3711
0.3726	0.45	2800	0.3705
0.3778	0.46	2850	0.3687
0.378	0.47	2900	0.3684
0.3769	0.47	2950	0.3674
0.3712	0.48	3000	0.3670
0.3629	0.49	3050	0.3668
0.3714	0.5	3100	0.3653
0.3743	0.51	3150	0.3639
0.3631	0.51	3200	0.3637
0.3805	0.52	3250	0.3628
0.3577	0.53	3300	0.3626
0.373	0.54	3350	0.3628
0.3609	0.55	3400	0.3608
0.358	0.55	3450	0.3604
0.3556	0.56	3500	0.3596
0.3442	0.57	3550	0.3603
0.3619	0.58	3600	0.3590
0.3691	0.59	3650	0.3573
0.3614	0.59	3700	0.3577
0.3661	0.6	3750	0.3558
0.3667	0.61	3800	0.3561
0.3653	0.62	3850	0.3554
0.3645	0.63	3900	0.3547
0.3496	0.63	3950	0.3545
0.3689	0.64	4000	0.3539
0.3554	0.65	4050	0.3531
0.3567	0.66	4100	0.3520
0.361	0.67	4150	0.3519
0.3522	0.67	4200	0.3514
0.347	0.68	4250	0.3507
0.3481	0.69	4300	0.3504
0.3646	0.7	4350	0.3497
0.3524	0.71	4400	0.3501
0.3487	0.71	4450	0.3492
0.3496	0.72	4500	0.3482
0.3691	0.73	4550	0.3481
0.36	0.74	4600	0.3484
0.3485	0.75	4650	0.3473
0.3492	0.75	4700	0.3471
0.3505	0.76	4750	0.3458
0.3472	0.77	4800	0.3466
0.3438	0.78	4850	0.3449
0.3516	0.79	4900	0.3447
0.3388	0.79	4950	0.3440
0.3443	0.8	5000	0.3433
0.3465	0.81	5050	0.3439
0.3335	0.82	5100	0.3421
0.3421	0.83	5150	0.3419
0.3424	0.83	5200	0.3418
0.338	0.84	5250	0.3411
0.3507	0.85	5300	0.3413
0.3347	0.86	5350	0.3400
0.3449	0.87	5400	0.3402
0.3396	0.87	5450	0.3404
0.3461	0.88	5500	0.3404
0.3519	0.89	5550	0.3394
0.3458	0.9	5600	0.3384
0.344	0.91	5650	0.3389
0.3415	0.91	5700	0.3386
0.3444	0.92	5750	0.3381
0.3366	0.93	5800	0.3377
0.3472	0.94	5850	0.3366
0.3335	0.95	5900	0.3363
0.3362	0.95	5950	0.3358
0.3408	0.96	6000	0.3354
0.353	0.97	6050	0.3355
0.3333	0.98	6100	0.3352
0.3356	0.99	6150	0.3341
0.3418	0.99	6200	0.3343
0.332	1.0	6250	0.3339
0.3359	1.01	6300	0.3341
0.3316	1.02	6350	0.3337
0.3356	1.03	6400	0.3324
0.3322	1.03	6450	0.3328
0.3319	1.04	6500	0.3317
0.3275	1.05	6550	0.3315
0.3245	1.06	6600	0.3316
0.3372	1.07	6650	0.3312
0.326	1.07	6700	0.3311
0.3246	1.08	6750	0.3311
0.3333	1.09	6800	0.3298
0.3321	1.1	6850	0.3292
0.3467	1.11	6900	0.3293
0.333	1.12	6950	0.3297
0.3328	1.12	7000	0.3296
0.3309	1.13	7050	0.3290
0.3338	1.14	7100	0.3284
0.3267	1.15	7150	0.3281
0.3342	1.16	7200	0.3273
0.321	1.16	7250	0.3277
0.3258	1.17	7300	0.3273
0.3263	1.18	7350	0.3277
0.3321	1.19	7400	0.3269
0.325	1.2	7450	0.3268
0.3261	1.2	7500	0.3262
0.3337	1.21	7550	0.3257
0.3353	1.22	7600	0.3254
0.3089	1.23	7650	0.3250
0.3388	1.24	7700	0.3250
0.3266	1.24	7750	0.3244
0.3316	1.25	7800	0.3243
0.3192	1.26	7850	0.3245
0.3444	1.27	7900	0.3239
0.3212	1.28	7950	0.3248
0.3237	1.28	8000	0.3237
0.3297	1.29	8050	0.3230
0.3252	1.3	8100	0.3231
0.3211	1.31	8150	0.3228
0.3323	1.32	8200	0.3238
0.3127	1.32	8250	0.3220
0.3163	1.33	8300	0.3223
0.322	1.34	8350	0.3211
0.3288	1.35	8400	0.3213
0.3248	1.36	8450	0.3214

Framework versions

PEFT 0.7.1
Transformers 4.36.2
Pytorch 2.1.2+cu121
Datasets 2.16.1
Tokenizers 0.15.0

megha-shroff/mistral-rand-300k-newinst

mistral-rand-300k-newinst

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

megha-shroff/
mistral-rand-300k-newinst