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Mistral_Sparse_refined_web_50p_graceful_True

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metadata
license: apache-2.0
base_model: mistralai/Mistral-7B-v0.1
tags:
  - generated_from_trainer
model-index:
  - name: Mistral_Sparse_refined_web_50p_graceful_True
    results: []

Mistral_Sparse_refined_web_50p_graceful_True

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: 2.3270

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: 5000

Training results

Training Loss Epoch Step Validation Loss
3.7944 0.0 25 2.4024
3.7251 0.01 50 2.3892
2.3685 0.01 75 2.5422
2.2521 0.02 100 2.4731
2.2468 0.02 125 2.4459
2.3204 0.02 150 2.4365
2.2561 0.03 175 2.4258
2.2388 0.03 200 2.4174
2.2738 0.04 225 2.4128
2.3089 0.04 250 2.4093
2.2252 0.04 275 2.4057
2.2394 0.05 300 2.3994
2.2531 0.05 325 2.4003
2.0899 0.06 350 2.4004
2.2479 0.06 375 2.3982
2.2875 0.06 400 2.3987
2.282 0.07 425 2.3959
2.2434 0.07 450 2.3920
2.1592 0.08 475 2.3930
2.2374 0.08 500 2.3915
2.2968 0.08 525 2.3906
2.1904 0.09 550 2.3883
2.3101 0.09 575 2.3894
2.1126 0.1 600 2.3899
2.2092 0.1 625 2.3934
2.3005 0.1 650 2.3903
2.2779 0.11 675 2.3876
2.2523 0.11 700 2.3886
2.2307 0.12 725 2.3879
2.1317 0.12 750 2.3832
2.1893 0.12 775 2.3848
2.2732 0.13 800 2.3822
2.2914 0.13 825 2.3855
2.2633 0.14 850 2.3859
2.1339 0.14 875 2.3866
2.2065 0.14 900 2.3862
2.1436 0.15 925 2.3807
2.2782 0.15 950 2.3822
2.2001 0.16 975 2.3800
2.2608 0.16 1000 2.3785
2.2654 0.16 1025 2.3817
2.2662 0.17 1050 2.3783
2.3061 0.17 1075 2.3777
2.2297 0.18 1100 2.3763
2.2705 0.18 1125 2.3775
2.2261 0.18 1150 2.3759
2.2812 0.19 1175 2.3773
2.1363 0.19 1200 2.3753
2.2382 0.2 1225 2.3756
2.2064 0.2 1250 2.3744
2.2559 0.2 1275 2.3698
2.2875 0.21 1300 2.3730
2.2541 0.21 1325 2.3737
2.1415 0.22 1350 2.3732
2.2529 0.22 1375 2.3721
2.2271 0.22 1400 2.3752
2.1849 0.23 1425 2.3738
2.1707 0.23 1450 2.3728
2.1363 0.24 1475 2.3729
2.1778 0.24 1500 2.3731
2.1146 0.24 1525 2.3795
2.1843 0.25 1550 2.3775
2.3094 0.25 1575 2.3727
2.2488 0.26 1600 2.3758
2.226 0.26 1625 2.3723
2.3067 0.26 1650 2.3734
2.2167 0.27 1675 2.3760
2.2466 0.27 1700 2.3750
2.2446 0.28 1725 2.3775
2.2268 0.28 1750 2.3741
2.2113 0.28 1775 2.3733
2.1608 0.29 1800 2.3762
2.2354 0.29 1825 2.3758
2.2433 0.3 1850 2.3745
2.2266 0.3 1875 2.3769
2.2453 0.3 1900 2.3726
2.3001 0.31 1925 2.3713
2.2447 0.31 1950 2.3722
2.2708 0.32 1975 2.3730
2.1878 0.32 2000 2.3743
2.2041 0.32 2025 2.3751
2.1935 0.33 2050 2.3750
2.1981 0.33 2075 2.3744
2.2777 0.34 2100 2.3720
2.3121 0.34 2125 2.3725
2.2294 0.34 2150 2.3750
2.1802 0.35 2175 2.3772
2.214 0.35 2200 2.3738
2.1631 0.36 2225 2.3740
2.1546 0.36 2250 2.3764
2.2841 0.36 2275 2.3743
2.271 0.37 2300 2.3707
2.1627 0.37 2325 2.3719
2.2071 0.38 2350 2.3678
2.2423 0.38 2375 2.3703
2.2554 0.38 2400 2.3700
2.1057 0.39 2425 2.3720
2.0983 0.39 2450 2.3690
2.1844 0.4 2475 2.3686
2.2797 0.4 2500 2.3719
2.2749 0.4 2525 2.3707
2.1326 0.41 2550 2.3728
2.1461 0.41 2575 2.3693
2.2324 0.42 2600 2.3699
2.2412 0.42 2625 2.3690
2.28 0.42 2650 2.3696
2.261 0.43 2675 2.3666
2.2737 0.43 2700 2.3674
2.2653 0.44 2725 2.3671
2.2269 0.44 2750 2.3643
2.245 0.44 2775 2.3641
2.3077 0.45 2800 2.3665
2.2143 0.45 2825 2.3667
2.2595 0.46 2850 2.3662
2.1638 0.46 2875 2.3661
2.1935 0.46 2900 2.3645
2.2063 0.47 2925 2.3659
2.2755 0.47 2950 2.3664
2.1977 0.48 2975 2.3649
2.2519 0.48 3000 2.3616
2.3353 0.48 3025 2.3648
2.223 0.49 3050 2.3629
2.2614 0.49 3075 2.3611
2.2983 0.5 3100 2.3663
2.1907 0.5 3125 2.3650
2.2683 0.5 3150 2.3629
2.1609 0.51 3175 2.3633
2.2316 0.51 3200 2.3626
2.1589 0.52 3225 2.3603
2.1479 0.52 3250 2.3598
2.2401 0.52 3275 2.3617
2.2073 0.53 3300 2.3608
2.094 0.53 3325 2.3609
2.2297 0.54 3350 2.3592
2.1305 0.54 3375 2.3585
2.1517 0.54 3400 2.3598
2.1592 0.55 3425 2.3626
2.0812 0.55 3450 2.3636
2.219 0.56 3475 2.3633
2.2632 0.56 3500 2.3625
2.2302 0.56 3525 2.3616
2.1926 0.57 3550 2.3623
2.1878 0.57 3575 2.3630
2.3519 0.58 3600 2.3609
2.1699 0.58 3625 2.3599
2.3576 0.58 3650 2.3618
2.1629 0.59 3675 2.3637
2.2982 0.59 3700 2.3606
2.1949 0.6 3725 2.3649
2.135 0.6 3750 2.3618
2.0752 0.6 3775 2.3637
2.2786 0.61 3800 2.3643
2.0974 0.61 3825 2.3644
2.2097 0.62 3850 2.3621
2.1372 0.62 3875 2.3642
2.2502 0.62 3900 2.3652
2.1817 0.63 3925 2.3613
2.1891 0.63 3950 2.3659
2.2261 0.64 3975 2.3630
2.2826 0.64 4000 2.3591
2.2308 0.64 4025 2.3617
2.1944 0.65 4050 2.3615
2.1638 0.65 4075 2.3644
2.1741 0.66 4100 2.3600
2.2092 0.66 4125 2.3602
2.1921 0.66 4150 2.3610
2.1029 0.67 4175 2.3604
2.2553 0.67 4200 2.3578
2.1924 0.68 4225 2.3625
2.1914 0.68 4250 2.3619
2.2556 0.68 4275 2.3583
2.188 0.69 4300 2.3600
2.2339 0.69 4325 2.3594
2.2484 0.7 4350 2.3593
2.2761 0.7 4375 2.3585
2.2305 0.7 4400 2.3606
2.2434 0.71 4425 2.3586
2.2879 0.71 4450 2.3586
2.1643 0.72 4475 2.3639
2.1697 0.72 4500 2.3573
2.2665 0.72 4525 2.3574
2.2349 0.73 4550 2.3593
2.1459 0.73 4575 2.3554
2.1619 0.74 4600 2.3546
2.1292 0.74 4625 2.3542
2.3202 0.74 4650 2.3591
2.2165 0.75 4675 2.3562
2.233 0.75 4700 2.3566
2.2378 0.76 4725 2.3574
2.1236 0.76 4750 2.3554
2.2054 0.76 4775 2.3586
2.1807 0.77 4800 2.3543
2.2965 0.77 4825 2.3570
2.3543 0.78 4850 2.3562
2.1981 0.78 4875 2.3575
2.1156 0.78 4900 2.3586
2.1352 0.79 4925 2.3560
2.3162 0.79 4950 2.3607
2.1469 0.8 4975 2.3575
2.2243 0.8 5000 2.3591

Framework versions

  • Transformers 4.37.2
  • Pytorch 2.1.1+cu121
  • Datasets 2.15.0
  • Tokenizers 0.15.0