End of training

README.md

@@ -44,32 +44,32 @@ More information needed
 | step | epoch | enwikippl | frwikippl | loss | runtime | samples_per_second | steps_per_second | tinystoriesppl | zhwikippl |
 | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
 | **teacher eval** |  | 43.25 | 61.25 |  |  |  |  | 11.6875 | 19.125 |
-| 0 | 0 | 957777707008.0 | 56624848830464.0 | 45.7836 | 30.1745 | 82.852 | 10.373 | 2566914048.0 | 36283883716608.0 |
-| 2500 | 0.0404 | 2032.0 | 25856.0 | 20.6614 | 30.1889 | 82.812 | 10.368 | 1432.0 | 92672.0 |
-| 5000 | 0.0808 | 488.0 | 3008.0 | 18.3376 | 30.1572 | 82.899 | 10.379 | 412.0 | 984.0 |
-| 7500 | 0.1212 | 276.0 | 1488.0 | 16.9326 | 30.1713 | 82.86 | 10.374 | 239.0 | 231.0 |
-| 10000 | 0.1616 | 204.0 | 880.0 | 16.0592 | 30.189 | 82.812 | 10.368 | 182.0 | 294.0 |
-| 12500 | 0.2020 | 149.0 | 506.0 | 14.9386 | 30.151 | 82.916 | 10.381 | 125.0 | 158.0 |
-| 15000 | 0.2424 | 119.5 | 470.0 | 14.3804 | 30.2036 | 82.772 | 10.363 | 86.5 | 144.0 |
-| 17500 | 0.2828 | 93.5 | 430.0 | 14.0105 | 30.2613 | 82.614 | 10.343 | 72.5 | 178.0 |
-| 20000 | 0.3232 | 77.0 | 280.0 | 13.4770 | 30.1964 | 82.791 | 10.365 | 59.75 | 85.5 |
-| 22500 | 0.3636 | 63.75 | 219.0 | 12.9954 | 30.296 | 82.519 | 10.331 | 50.25 | 75.0 |
-| 25000 | 0.4040 | 60.75 | 185.0 | 12.7840 | 30.2946 | 82.523 | 10.332 | 46.0 | 74.5 |
-| 27500 | 0.4444 | 58.75 | 190.0 | 12.6366 | 30.3968 | 82.246 | 10.297 | 41.0 | 51.25 |
-| 30000 | 0.4848 | 58.75 | 177.0 | 12.6497 | 30.3256 | 82.439 | 10.321 | 42.5 | 62.5 |
-| 32500 | 0.5253 | 59.5 | 171.0 | 12.5958 | 30.3473 | 82.38 | 10.314 | 38.75 | 69.0 |
-| 35000 | 0.5657 | 55.5 | 164.0 | 12.4809 | 30.4047 | 82.224 | 10.294 | 36.25 | 49.25 |
-| 37500 | 0.6061 | 55.75 | 165.0 | 12.4218 | 30.2813 | 82.559 | 10.336 | 35.0 | 51.5 |
-| 40000 | 0.6465 | 54.0 | 147.0 | 12.3726 | 30.199 | 82.784 | 10.365 | 33.75 | 51.75 |
-| 42500 | 0.6869 | 55.0 | 144.0 | 12.3525 | 30.6915 | 81.456 | 10.198 | 34.0 | 53.75 |
-| 45000 | 0.7273 | 50.75 | 129.0 | 12.1198 | 30.7649 | 81.262 | 10.174 | 29.875 | 36.5 |
-| 47500 | 0.7677 | 50.5 | 122.5 | 12.0744 | 30.289 | 82.538 | 10.334 | 28.875 | 34.75 |
-| 50000 | 0.8081 | 49.5 | 121.5 | 12.0388 | 30.3972 | 82.244 | 10.297 | 28.75 | 34.25 |
-| 52500 | 0.8485 | 50.0 | 122.5 | 12.0214 | 30.351 | 82.37 | 10.313 | 28.5 | 38.75 |
-| 55000 | 0.8889 | 49.5 | 119.0 | 11.9902 | 30.2303 | 82.698 | 10.354 | 27.625 | 34.5 |
-| 57500 | 0.9293 | 49.25 | 119.0 | 11.9806 | 30.6005 | 81.698 | 10.229 | 27.625 | 33.25 |
-| 60000 | 0.9697 | 49.25 | 118.0 | 11.9745 | 31.1957 | 80.139 | 10.033 | 27.5 | 33.0 |
-| 61875 | 1.0 | 49.0 | 118.0 | 11.9734 | 31.2236 | 80.068 | 10.024 | 27.5 | 33.0 |
+| 0 | 0 | 2473901162496.0 | 170424302305280.0 | 25.7744 | 30.269 | 82.593 | 10.341 | 4060086272.0 | 71468255805440.0 |
+| 2500 | 0.0404 | 952.0 | 8032.0 | 6.1202 | 30.2377 | 82.678 | 10.351 | 652.0 | 6432.0 |
+| 5000 | 0.0808 | 378.0 | 1880.0 | 5.0293 | 30.3345 | 82.414 | 10.318 | 270.0 | 288.0 |
+| 7500 | 0.1212 | 230.0 | 820.0 | 4.5127 | 30.2749 | 82.577 | 10.339 | 201.0 | 174.0 |
+| 10000 | 0.1616 | 173.0 | 628.0 | 4.2288 | 30.3713 | 82.315 | 10.306 | 152.0 | 172.0 |
+| 12500 | 0.2020 | 127.5 | 482.0 | 3.8554 | 30.4218 | 82.178 | 10.289 | 106.5 | 157.0 |
+| 15000 | 0.2424 | 109.0 | 436.0 | 3.6684 | 30.2501 | 82.644 | 10.347 | 87.5 | 148.0 |
+| 17500 | 0.2828 | 93.5 | 350.0 | 3.5226 | 30.2559 | 82.628 | 10.345 | 73.5 | 119.5 |
+| 20000 | 0.3232 | 75.5 | 282.0 | 3.3351 | 30.5792 | 81.755 | 10.236 | 64.5 | 114.5 |
+| 22500 | 0.3636 | 67.5 | 213.0 | 3.1522 | 30.4457 | 82.113 | 10.281 | 53.25 | 80.5 |
+| 25000 | 0.4040 | 64.0 | 197.0 | 3.0806 | 30.4623 | 82.069 | 10.275 | 45.0 | 95.5 |
+| 27500 | 0.4444 | 58.25 | 203.0 | 3.0308 | 30.2865 | 82.545 | 10.335 | 40.75 | 93.0 |
+| 30000 | 0.4848 | 59.5 | 195.0 | 3.0207 | 30.2442 | 82.66 | 10.349 | 43.25 | 61.5 |
+| 32500 | 0.5253 | 58.5 | 172.0 | 3.0015 | 30.3473 | 82.38 | 10.314 | 41.75 | 60.75 |
+| 35000 | 0.5657 | 57.25 | 171.0 | 2.9425 | 30.2295 | 82.701 | 10.354 | 38.0 | 51.5 |
+| 37500 | 0.6061 | 57.25 | 157.0 | 2.9166 | 31.0953 | 80.398 | 10.066 | 38.0 | 55.0 |
+| 40000 | 0.6465 | 54.75 | 160.0 | 2.8987 | 30.9607 | 80.747 | 10.11 | 35.5 | 58.75 |
+| 42500 | 0.6869 | 53.75 | 153.0 | 2.8783 | 30.2174 | 82.734 | 10.358 | 35.75 | 67.0 |
+| 45000 | 0.7273 | 50.25 | 136.0 | 2.7764 | 30.1636 | 82.881 | 10.377 | 30.375 | 41.25 |
+| 47500 | 0.7677 | 50.25 | 126.5 | 2.7509 | 30.252 | 82.639 | 10.346 | 29.375 | 38.0 |
+| 50000 | 0.8081 | 49.0 | 127.5 | 2.7362 | 30.1988 | 82.785 | 10.365 | 28.75 | 40.0 |
+| 52500 | 0.8485 | 48.5 | 121.0 | 2.7263 | 30.2772 | 82.57 | 10.338 | 29.0 | 35.5 |
+| 55000 | 0.8889 | 48.0 | 119.0 | 2.7102 | 30.1534 | 82.909 | 10.38 | 28.0 | 34.0 |
+| 57500 | 0.9293 | 47.5 | 118.5 | 2.7043 | 30.181 | 82.834 | 10.371 | 27.875 | 32.5 |
+| 60000 | 0.9697 | 47.5 | 118.0 | 2.7013 | 30.3322 | 82.421 | 10.319 | 27.75 | 32.25 |
+| 61875 | 1.0 | 47.5 | 118.0 | 2.7006 | 30.5306 | 81.885 | 10.252 | 27.625 | 32.25 |
 
 # Resource Usage Comparison
 
@@ -93,7 +93,7 @@ More information needed
 <br/>
 
 # Train Dataset
-Trained on 145,725,467 tokens from the [wikimedia/wikipedia](https://huggingface.co/datasets/wikimedia/wikipedia) dataset.
+Trained on 145,744,973 tokens from the [wikimedia/wikipedia](https://huggingface.co/datasets/wikimedia/wikipedia) dataset.
 
 - Num Samples: `247,500`
 - Subset: `20231101.en`
@@ -103,7 +103,7 @@ Trained on 145,725,467 tokens from the [wikimedia/wikipedia](https://huggingface
 # Training Objective
 
 ```
-DistillationObjective(logits_loss_component=LossComponent(label=logits, weight=1, loss_fn=kl), attn_loss_component=LossComponent(label=attn, weight=25.0, loss_fn=cos, layer_mapper=layer-2))
+DistillationObjective(logits_loss_component=LossComponent(label=logits, weight=1, loss_fn=kl), attn_loss_component=LossComponent(label=attn, weight=5, loss_fn=cos, layer_mapper=layer-2))
 ```
 
 # Hyperparameters
@@ -120,9 +120,9 @@ The following hyperparameters were used during training:
 - lr_scheduler_type: `linear`
 - lr_scheduler_warmup_ratio: `0.5`
 - num_epochs: `1.0`
-- distillation_objective: `DistillationObjective(logits_loss_component=LossComponent(label=logits, weight=1, loss_fn=kl), attn_loss_component=LossComponent(label=attn, weight=25.0, loss_fn=cos, layer_mapper=layer-2))`
+- distillation_objective: `DistillationObjective(logits_loss_component=LossComponent(label=logits, weight=1, loss_fn=kl), attn_loss_component=LossComponent(label=attn, weight=5, loss_fn=cos, layer_mapper=layer-2))`
 - train_embeddings: `True`
-- lr_scheduler: `<torch.optim.lr_scheduler.LambdaLR object at 0x7fb6c83166e0>`
+- lr_scheduler: `<torch.optim.lr_scheduler.LambdaLR object at 0x7fb7a85a5a50>`
 - student_model_name_or_path: `None`
 - student_config_name_or_path: `None`
 - student_model_config: `None`

logs/attn_loss_fn=cos, attn_weight=5, layer_mapper=layer-2, projector=orthogonal/events.out.tfevents.1724422905.f383272e719b

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