readme: ---
  tags:
  - generated_from_trainer
  model-index:
  - name: PromoGen_K562_GPT2_4096_tokens_2080Ti_x4_more_DE
    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. -->
  
  # PromoGen_K562_GPT2_4096_tokens_2080Ti_x4_more_DE
  
  This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 10.7123
  
  ## 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: 0.001
  - train_batch_size: 128
  - eval_batch_size: 128
  - seed: 42
  - gradient_accumulation_steps: 8
  - total_train_batch_size: 1024
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: cosine
  - lr_scheduler_warmup_steps: 1000
  - num_epochs: 200
  
  ### Training results
  
  | Training Loss | Epoch  | Step  | Validation Loss |
  |:-------------:|:------:|:-----:|:---------------:|
  | 7.8346        | 1.69   | 250   | 7.3201          |
  | 6.8483        | 3.38   | 500   | 6.6602          |
  | 6.5149        | 5.07   | 750   | 6.3857          |
  | 6.1941        | 6.76   | 1000  | 6.1764          |
  | 6.0188        | 8.45   | 1250  | 6.0308          |
  | 5.7091        | 10.14  | 1500  | 5.9934          |
  | 5.4459        | 11.82  | 1750  | 5.9371          |
  | 5.1559        | 13.51  | 2000  | 5.9561          |
  | 5.053         | 15.2   | 2250  | 6.0799          |
  | 4.8356        | 16.89  | 2500  | 5.9637          |
  | 4.6625        | 18.58  | 2750  | 6.0990          |
  | 4.4349        | 20.27  | 3000  | 6.4003          |
  | 4.2347        | 21.96  | 3250  | 6.2304          |
  | 4.0581        | 23.65  | 3500  | 6.4391          |
  | 4.1369        | 25.34  | 3750  | 6.6182          |
  | 3.9847        | 27.03  | 4000  | 6.8092          |
  | 3.7499        | 28.72  | 4250  | 6.7152          |
  | 3.6274        | 30.41  | 4500  | 6.8939          |
  | 3.515         | 32.09  | 4750  | 7.1118          |
  | 3.428         | 33.78  | 5000  | 6.8671          |
  | 3.4504        | 35.47  | 5250  | 7.0974          |
  | 3.3346        | 37.16  | 5500  | 7.4627          |
  | 3.1574        | 38.85  | 5750  | 7.1213          |
  | 3.0594        | 40.54  | 6000  | 7.3364          |
  | 3.1296        | 42.23  | 6250  | 7.5662          |
  | 3.0683        | 43.92  | 6500  | 7.3176          |
  | 2.9588        | 45.61  | 6750  | 7.4911          |
  | 2.8816        | 47.3   | 7000  | 7.7823          |
  | 2.7364        | 48.99  | 7250  | 7.4892          |
  | 2.6647        | 50.68  | 7500  | 7.7526          |
  | 2.8314        | 52.36  | 7750  | 7.9454          |
  | 2.7594        | 54.05  | 8000  | 8.2020          |
  | 2.5894        | 55.74  | 8250  | 7.8243          |
  | 2.5204        | 57.43  | 8500  | 8.0420          |
  | 2.5072        | 59.12  | 8750  | 8.2496          |
  | 2.4765        | 60.81  | 9000  | 7.9321          |
  | 2.5039        | 62.5   | 9250  | 8.1675          |
  | 2.4301        | 64.19  | 9500  | 8.4031          |
  | 2.2991        | 65.88  | 9750  | 8.0673          |
  | 2.2471        | 67.57  | 10000 | 8.2341          |
  | 2.3514        | 69.26  | 10250 | 8.5114          |
  | 2.3166        | 70.95  | 10500 | 8.1837          |
  | 2.2225        | 72.63  | 10750 | 8.3777          |
  | 2.17          | 74.32  | 11000 | 8.5542          |
  | 2.0509        | 76.01  | 11250 | 8.6312          |
  | 2.0204        | 77.7   | 11500 | 8.4680          |
  | 2.1625        | 79.39  | 11750 | 8.7197          |
  | 2.1102        | 81.08  | 12000 | 8.8730          |
  | 1.9783        | 82.77  | 12250 | 8.6112          |
  | 1.9293        | 84.46  | 12500 | 8.7798          |
  | 1.9402        | 86.15  | 12750 | 8.9528          |
  | 1.9095        | 87.84  | 13000 | 8.6741          |
  | 1.9188        | 89.53  | 13250 | 8.9483          |
  | 1.877         | 91.22  | 13500 | 9.0630          |
  | 1.7614        | 92.9   | 13750 | 8.8313          |
  | 1.7251        | 94.59  | 14000 | 8.9837          |
  | 1.821         | 96.28  | 14250 | 9.1340          |
  | 1.799         | 97.97  | 14500 | 8.9597          |
  | 1.7024        | 99.66  | 14750 | 9.1133          |
  | 1.6657        | 101.35 | 15000 | 9.2959          |
  | 1.5863        | 103.04 | 15250 | 9.2909          |
  | 1.5708        | 104.73 | 15500 | 9.1864          |
  | 1.6538        | 106.42 | 15750 | 9.3570          |
  | 1.6119        | 108.11 | 16000 | 9.4937          |
  | 1.5147        | 109.8  | 16250 | 9.3262          |
  | 1.4745        | 111.49 | 16500 | 9.4693          |
  | 1.4869        | 113.18 | 16750 | 9.5963          |
  | 1.4664        | 114.86 | 17000 | 9.4436          |
  | 1.4566        | 116.55 | 17250 | 9.5559          |
  | 1.4231        | 118.24 | 17500 | 9.6734          |
  | 1.3421        | 119.93 | 17750 | 9.5746          |
  | 1.3086        | 121.62 | 