Mistral_Sparse_refined_web_70p_2024-03-12

This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on the None dataset. It achieves the following results on the evaluation set:

• Loss: 2.1373

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: 1e-05
• train_batch_size: 1
• eval_batch_size: 1
• seed: 0
• distributed_type: multi-GPU
• num_devices: 4
• gradient_accumulation_steps: 4
• total_train_batch_size: 16
• total_eval_batch_size: 4
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• training_steps: 2600

Training results

Training Loss	Epoch	Step	Validation Loss
2.7221	0.0	25	2.8218
2.4266	0.01	50	2.6972
2.4153	0.01	75	2.6181
2.3588	0.02	100	2.5695
2.3274	0.02	125	2.5427
2.4054	0.02	150	2.5244
2.3274	0.03	175	2.5144
2.3042	0.03	200	2.4995
2.3296	0.04	225	2.4898
2.3621	0.04	250	2.4844
2.2825	0.04	275	2.4756
2.2932	0.05	300	2.4704
2.3015	0.05	325	2.4693
2.139	0.06	350	2.4612
2.2953	0.06	375	2.4553
2.3358	0.06	400	2.4546
2.3302	0.07	425	2.4506
2.2814	0.07	450	2.4506
2.2014	0.08	475	2.4455
2.266	0.08	500	2.4434
2.3309	0.08	525	2.4430
2.2278	0.09	550	2.4417
2.3621	0.09	575	2.4384
2.1614	0.1	600	2.4385
2.2504	0.1	625	2.4370
2.3301	0.1	650	2.4350
2.3177	0.11	675	2.4331
2.2784	0.11	700	2.4307
2.2681	0.12	725	2.4305
2.1777	0.12	750	2.4314
2.2164	0.12	775	2.4321
2.3068	0.13	800	2.4292
2.3131	0.13	825	2.4267
2.2971	0.14	850	2.4256
2.1623	0.14	875	2.4231
2.2308	0.14	900	2.4246
2.1772	0.15	925	2.4259
2.3114	0.15	950	2.4226
2.2434	0.16	975	2.4268
2.2852	0.16	1000	2.4259
2.2924	0.16	1025	2.4262
2.3095	0.17	1050	2.4231
2.3378	0.17	1075	2.4225
2.265	0.18	1100	2.4181
2.2893	0.18	1125	2.4237
2.2577	0.18	1150	2.4176
2.3088	0.19	1175	2.4166
2.1623	0.19	1200	2.4139
2.2576	0.2	1225	2.4177
2.2411	0.2	1250	2.4160
2.28	0.2	1275	2.4171
2.3077	0.21	1300	2.4176
2.2814	0.21	1325	2.4186
2.1772	0.22	1350	2.4199
2.2554	0.22	1375	2.4190
2.2665	0.22	1400	2.4182
2.2058	0.23	1425	2.4171
2.1881	0.23	1450	2.4209
2.1567	0.24	1475	2.4186
2.2146	0.24	1500	2.4210
2.1493	0.24	1525	2.4207
2.2145	0.25	1550	2.4167
2.3312	0.25	1575	2.4187
2.2897	0.26	1600	2.4193
2.2592	0.26	1625	2.4170
2.3402	0.26	1650	2.4137
2.2354	0.27	1675	2.4165
2.2839	0.27	1700	2.4173
2.2681	0.28	1725	2.4177
2.2501	0.28	1750	2.4154
2.232	0.28	1775	2.4138
2.1882	0.29	1800	2.4142
2.2668	0.29	1825	2.4136
2.2641	0.3	1850	2.4110
2.2536	0.3	1875	2.4148
2.2732	0.3	1900	2.4159
2.3244	0.31	1925	2.4129
2.2639	0.31	1950	2.4135
2.2876	0.32	1975	2.4149
2.2108	0.32	2000	2.4116
2.233	0.32	2025	2.4163
2.2177	0.33	2050	2.4141
2.2132	0.33	2075	2.4143
2.3103	0.34	2100	2.4161
2.3486	0.34	2125	2.4129
2.2573	0.34	2150	2.4170
2.2096	0.35	2175	2.4110
2.241	0.35	2200	2.4135
2.1914	0.36	2225	2.4148
2.1867	0.36	2250	2.4132
2.3178	0.36	2275	2.4120
2.2948	0.37	2300	2.4071
2.1954	0.37	2325	2.4108
2.2368	0.38	2350	2.4105
2.2714	0.38	2375	2.4060
2.2808	0.38	2400	2.4097
2.1327	0.39	2425	2.4075
2.1245	0.39	2450	2.4101
2.2168	0.4	2475	2.4119
2.2988	0.4	2500	2.4106
2.3049	0.4	2525	2.4084
2.159	0.41	2550	2.4103
2.183	0.41	2575	2.4105
2.2598	0.42	2600	2.4103

Framework versions

• Transformers 4.36.2
• Pytorch 2.1.2+cu121
• Datasets 2.15.0
• Tokenizers 0.15.0