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thrunlab/
Mistral_Sparse_pretraining_80_percent_10000

Name: Mistral_Sparse_pretraining_80_percent_10000
Availability: InStock

transformers

safetensors

mistral

generated_from_trainer

openwebtext

mistralai/Mistral-7B-Instruct-v0.1

finetune

apache-2.0

text-generation-inference

endpoints_compatible

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Mistral_Sparse_pretraining_80_percent_10000

This model is a fine-tuned version of mistralai/Mistral-7B-Instruct-v0.1 on the openwebtext dataset. It achieves the following results on the evaluation set:

Loss: 0.6872

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: 8
eval_batch_size: 32
seed: 0
distributed_type: multi-GPU
num_devices: 6
gradient_accumulation_steps: 2
total_train_batch_size: 96
total_eval_batch_size: 192
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
training_steps: 10000

Training results

Training Loss	Epoch	Step	Validation Loss
1.7461	0.05	50	1.7009
1.4034	0.1	100	1.3910
1.2302	0.15	150	1.2330
1.1363	0.19	200	1.1354
1.0699	0.24	250	1.0723
1.0316	0.29	300	1.0284
1.0044	0.34	350	0.9943
0.9719	0.39	400	0.9668
0.9391	0.44	450	0.9430
0.9194	0.48	500	0.9249
0.9131	0.53	550	0.9092
0.877	0.58	600	0.8953
0.8757	0.63	650	0.8852
0.8644	0.68	700	0.8749
0.8625	0.73	750	0.8679
0.867	0.78	800	0.8594
0.852	0.82	850	0.8529
0.8482	0.87	900	0.8473
0.8372	0.92	950	0.8421
0.8391	0.97	1000	0.8366
0.8209	1.02	1050	0.8327
0.8172	1.07	1100	0.8275
0.8094	1.11	1150	0.8247
0.8107	1.16	1200	0.8210
0.8137	1.21	1250	0.8168
0.8122	1.26	1300	0.8143
0.8047	1.31	1350	0.8115
0.804	1.36	1400	0.8083
0.7955	1.41	1450	0.8062
0.7939	1.45	1500	0.8040
0.7835	1.5	1550	0.8019
0.7983	1.55	1600	0.8001
0.7953	1.6	1650	0.7975
0.7903	1.65	1700	0.7945
0.7864	1.7	1750	0.7938
0.7972	1.75	1800	0.7914
0.7855	1.79	1850	0.7905
0.7834	1.84	1900	0.7878
0.7812	1.89	1950	0.7854
0.7865	1.94	2000	0.7847
0.7875	1.99	2050	0.7837
0.7764	2.04	2100	0.7815
0.7676	2.08	2150	0.7807
0.7716	2.13	2200	0.7796
0.777	2.18	2250	0.7781
0.7706	2.23	2300	0.7769
0.7669	2.28	2350	0.7748
0.771	2.33	2400	0.7742
0.7501	2.38	2450	0.7728
0.7653	2.42	2500	0.7713
0.7715	2.47	2550	0.7699
0.7588	2.52	2600	0.7694
0.7665	2.57	2650	0.7676
0.7616	2.62	2700	0.7658
0.7597	2.67	2750	0.7654
0.756	2.71	2800	0.7644
0.7517	2.76	2850	0.7628
0.7561	2.81	2900	0.7628
0.7413	2.86	2950	0.7620
0.7545	2.91	3000	0.7603
0.7442	2.96	3050	0.7592
0.7454	3.01	3100	0.7589
0.7575	3.05	3150	0.7583
0.739	3.1	3200	0.7571
0.7446	3.15	3250	0.7558
0.7428	3.2	3300	0.7557
0.737	3.25	3350	0.7553
0.7512	3.3	3400	0.7536
0.7447	3.34	3450	0.7525
0.7417	3.39	3500	0.7525
0.7403	3.44	3550	0.7512
0.761	3.49	3600	0.7502
0.7475	3.54	3650	0.7498
0.7535	3.59	3700	0.7486
0.733	3.64	3750	0.7483
0.7347	3.68	3800	0.7470
0.7439	3.73	3850	0.7470
0.7417	3.78	3900	0.7460
0.7383	3.83	3950	0.7460
0.7316	3.88	4000	0.7450
0.7273	3.93	4050	0.7442
0.7376	3.97	4100	0.7440
0.73	4.02	4150	0.7424
0.732	4.07	4200	0.7429
0.7278	4.12	4250	0.7419
0.721	4.17	4300	0.7416
0.7309	4.22	4350	0.7410
0.7273	4.27	4400	0.7400
0.7297	4.31	4450	0.7395
0.7321	4.36	4500	0.7385
0.7348	4.41	4550	0.7381
0.7251	4.46	4600	0.7371
0.7175	4.51	4650	0.7372
0.7356	4.56	4700	0.7368
0.7306	4.6	4750	0.7363
0.7248	4.65	4800	0.7359
0.7266	4.7	4850	0.7343
0.7243	4.75	4900	0.7349
0.7256	4.8	4950	0.7338
0.7301	4.85	5000	0.7335
0.7266	4.9	5050	0.7327
0.7229	4.94	5100	0.7321
0.7355	4.99	5150	0.7315
0.7207	5.04	5200	0.7317
0.7157	5.09	5250	0.7314
0.7214	5.14	5300	0.7299
0.7104	5.19	5350	0.7304
0.7059	5.24	5400	0.7296
0.7181	5.28	5450	0.7295
0.7226	5.33	5500	0.7286
0.7077	5.38	5550	0.7282
0.7239	5.43	5600	0.7276
0.7159	5.48	5650	0.7277
0.7169	5.53	5700	0.7271
0.7101	5.57	5750	0.7269
0.7146	5.62	5800	0.7262
0.7191	5.67	5850	0.7265
0.7124	5.72	5900	0.7248
0.7085	5.77	5950	0.7238
0.7052	5.82	6000	0.7235
0.7222	5.87	6050	0.7222
0.7089	5.91	6100	0.7221
0.7088	5.96	6150	0.7222
0.7017	6.01	6200	0.7218
0.7079	6.06	6250	0.7218
0.7209	6.11	6300	0.7211
0.691	6.16	6350	0.7210
0.7035	6.2	6400	0.7203
0.7075	6.25	6450	0.7207
0.7036	6.3	6500	0.7200
0.7023	6.35	6550	0.7189
0.7201	6.4	6600	0.7192
0.7021	6.45	6650	0.7188
0.6971	6.5	6700	0.7174
0.7087	6.54	6750	0.7184
0.7044	6.59	6800	0.7176
0.6921	6.64	6850	0.7179
0.7079	6.69	6900	0.7166
0.6908	6.74	6950	0.7158
0.687	6.79	7000	0.7158
0.696	6.83	7050	0.7148
0.6954	6.88	7100	0.7152
0.7103	6.93	7150	0.7143
0.6999	6.98	7200	0.7140
0.699	7.03	7250	0.7138
0.6959	7.08	7300	0.7138
0.6871	7.13	7350	0.7122
0.6941	7.17	7400	0.7131
0.6931	7.22	7450	0.7132
0.707	7.27	7500	0.7110
0.6911	7.32	7550	0.7122
0.7036	7.37	7600	0.7113
0.7105	7.42	7650	0.7107
0.7035	7.46	7700	0.7108
0.6901	7.51	7750	0.7113
0.6944	7.56	7800	0.7096
0.6927	7.61	7850	0.7093
0.7052	7.66	7900	0.7090
0.7046	7.71	7950	0.7082
0.6949	7.76	8000	0.7082
0.6888	7.8	8050	0.7071
0.6916	7.85	8100	0.7071
0.6937	7.9	8150	0.7067
0.7077	7.95	8200	0.7066
0.6847	8.0	8250	0.7057
0.6908	8.05	8300	0.7056
0.6813	8.1	8350	0.7060
0.6756	8.14	8400	0.7055
0.7006	8.19	8450	0.7052
0.6842	8.24	8500	0.7035
0.6851	8.29	8550	0.7044
0.6944	8.34	8600	0.7042
0.6929	8.39	8650	0.7040
0.6924	8.43	8700	0.7037
0.6843	8.48	8750	0.7037
0.7005	8.53	8800	0.7028
0.6795	8.58	8850	0.7022
0.6946	8.63	8900	0.7019
0.6761	8.68	8950	0.7016
0.6817	8.73	9000	0.7012
0.6838	8.77	9050	0.7012
0.6877	8.82	9100	0.7006
0.6812	8.87	9150	0.7004
0.6966	8.92	9200	0.7005
0.6778	8.97	9250	0.6993
0.6844	9.02	9300	0.6991
0.6853	9.06	9350	0.7000
0.6839	9.11	9400	0.6998
0.6813	9.16	9450	0.6984
0.6903	9.21	9500	0.6985
0.6819	9.26	9550	0.6987
0.6749	9.31	9600	0.6980
0.6782	9.36	9650	0.6979
0.6805	9.4	9700	0.6975
0.6907	9.45	9750	0.6974
0.6854	9.5	9800	0.6967
0.6803	9.55	9850	0.6969
0.6854	9.6	9900	0.6964
0.6761	9.65	9950	0.6966
0.6939	9.69	10000	0.6959

Framework versions

Transformers 4.35.2
Pytorch 2.1.1+cu121
Datasets 2.15.0
Tokenizers 0.15.0

thrunlab/Mistral_Sparse_pretraining_80_percent_10000

Mistral_Sparse_pretraining_80_percent_10000

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

thrunlab/
Mistral_Sparse_pretraining_80_percent_10000