readme: ---
  tags:
  - generated_from_trainer
  model-index:
  - name: enlm-r
    results: []
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # enlm-r
  
  This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.4837
  
  ## 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: 0.0006
  - train_batch_size: 16
  - eval_batch_size: 16
  - seed: 42
  - distributed_type: multi-GPU
  - num_devices: 4
  - gradient_accumulation_steps: 128
  - total_train_batch_size: 8192
  - total_eval_batch_size: 64
  - optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-06
  - lr_scheduler_type: polynomial
  - lr_scheduler_warmup_steps: 24000
  - num_epochs: 81
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss |
  |:-------------:|:-----:|:-----:|:---------------:|
  | 6.4           | 0.33  | 160   | 10.7903         |
  | 6.4           | 0.66  | 320   | 10.1431         |
  | 6.4           | 0.99  | 480   | 9.8708          |
  | 6.4           | 0.33  | 640   | 9.3884          |
  | 6.4           | 0.66  | 800   | 8.7352          |
  | 6.4           | 0.99  | 960   | 8.3341          |
  | 6.4           | 1.33  | 1120  | 8.0614          |
  | 6.4           | 1.66  | 1280  | 7.8582          |
  | 4.2719        | 1.99  | 1440  | 7.4879          |
  | 3.2           | 3.3   | 1600  | 7.2689          |
  | 3.2           | 3.63  | 1760  | 7.1434          |
  | 3.2           | 3.96  | 1920  | 7.0576          |
  | 3.2           | 4.29  | 2080  | 7.0030          |
  | 3.2           | 4.62  | 2240  | 6.9612          |
  | 3.2           | 4.95  | 2400  | 6.9394          |
  | 3.2           | 5.28  | 2560  | 6.9559          |
  | 3.2           | 5.61  | 2720  | 6.8964          |
  | 3.2           | 5.94  | 2880  | 6.8939          |
  | 3.2           | 6.27  | 3040  | 6.8871          |
  | 3.2           | 6.6   | 3200  | 6.8771          |
  | 3.2           | 6.93  | 3360  | 6.8617          |
  | 3.2           | 7.26  | 3520  | 6.8472          |
  | 3.2           | 7.59  | 3680  | 6.8283          |
  | 3.2           | 7.92  | 3840  | 6.8082          |
  | 3.2           | 8.25  | 4000  | 6.8119          |
  | 3.2           | 8.58  | 4160  | 6.7962          |
  | 3.2           | 8.91  | 4320  | 6.7751          |
  | 3.2           | 9.24  | 4480  | 6.7405          |
  | 3.2           | 9.57  | 4640  | 6.7412          |
  | 3.2           | 9.9   | 4800  | 6.7279          |
  | 3.2           | 10.22 | 4960  | 6.7069          |
  | 3.2           | 10.55 | 5120  | 6.6998          |
  | 3.2           | 10.88 | 5280  | 6.6875          |
  | 3.2           | 11.22 | 5440  | 6.6580          |
  | 3.2           | 11.55 | 5600  | 6.6402          |
  | 3.2           | 11.88 | 5760  | 6.6281          |
  | 3.2           | 12.21 | 5920  | 6.6181          |
  | 3.2           | 12.54 | 6080  | 6.5995          |
  | 3.2           | 12.87 | 6240  | 6.5970          |
  | 3.2           | 13.2  | 6400  | 6.5772          |
  | 3.2           | 13.53 | 6560  | 6.5594          |
  | 3.2           | 13.85 | 6720  | 6.5400          |
  | 3.2           | 14.19 | 6880  | 6.5396          |
  | 3.2           | 14.51 | 7040  | 6.5211          |
  | 3.2           | 14.84 | 7200  | 6.5140          |
  | 3.2           | 15.18 | 7360  | 6.4002          |
  | 3.2           | 15.5  | 7520  | 6.3170          |
  | 3.2           | 15.83 | 7680  | 6.2621          |
  | 3.2           | 16.16 | 7840  | 6.2253          |
  | 3.2           | 16.49 | 8000  | 6.1722          |
  | 3.2           | 16.82 | 8160  | 6.1106          |
  | 3.2           | 17.15 | 8320  | 6.1281          |
  | 3.2           | 17.48 | 8480  | 6.0019          |
  | 3.2           | 17.81 | 8640  | 5.9069          |
  | 3.2           | 18.14 | 8800  | 5.7105          |
  | 3.2           | 18.47 | 8960  | 5.2741          |
  | 3.2           | 18.8  | 9120  | 5.0369          |
  | 5.0352        | 19.13 | 9280  | 4.8148          |
  | 4.5102        | 19.26 | 9440  | 4.3175          |
  | 4.1247        | 19.59 | 9600  | 3.9518          |
  | 3.8443        | 20.12 | 9760  | 3.6712          |
  | 3.6334        | 20.45 | 9920  | 3.4654          |
  | 3.4698        | 20.78 | 10080 | 3.2994          |
  | 3.3267        | 21.11 | 10240 | 3.1638          |
  | 3.2173        | 21.44 | 10400 | 3.0672          |
  | 3.1255        | 21.77 | 10560 | 2.9687          |
  | 3.0344        | 22.1  | 10720 | 2.8865          |
  | 2.9645        | 22.43 | 10880 | 2.8104          |
  | 2.9046        | 22.76 | 11040 | 2.7497          |
  | 2.8707        | 23.09 | 11200 | 2.7040          |
  | 2.7903        | 23.42 | 11360 | 2.6416          |
  | 2.7339        | 23.75 | 11520 | 2.5891          |
  | 2.6894        | 24.08 | 11680 | 2.5370          |
  