End of training

README.md

@@ -44,36 +44,36 @@ 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 | 1133871366144.0 | 97306779058176.0 | 44.6892 | 25.2062 | 99.182 | 12.418 | 2785017856.0 | 54425825574912.0 |
-| 2500 | 0.0404 | 1648.0 | 17664.0 | 20.4150 | 25.2355 | 99.067 | 12.403 | 1368.0 | 30464.0 |
-| 5000 | 0.0808 | 498.0 | 3152.0 | 18.2270 | 25.2737 | 98.917 | 12.384 | 338.0 | 620.0 |
-| 7500 | 0.1212 | 272.0 | 1288.0 | 16.8848 | 25.2363 | 99.064 | 12.403 | 241.0 | 262.0 |
-| 10000 | 0.1616 | 199.0 | 852.0 | 16.0482 | 25.2552 | 98.99 | 12.394 | 181.0 | 160.0 |
-| 12500 | 0.2020 | 142.0 | 544.0 | 14.9888 | 25.2627 | 98.96 | 12.39 | 122.0 | 159.0 |
-| 15000 | 0.2424 | 119.0 | 506.0 | 14.4049 | 25.2537 | 98.995 | 12.394 | 95.5 | 151.0 |
-| 17500 | 0.2828 | 98.0 | 376.0 | 14.0632 | 25.1898 | 99.247 | 12.426 | 74.0 | 128.0 |
-| 20000 | 0.3232 | 76.5 | 280.0 | 13.5213 | 25.2312 | 99.084 | 12.405 | 68.0 | 94.0 |
-| 22500 | 0.3636 | 66.0 | 210.0 | 13.0349 | 25.2005 | 99.204 | 12.42 | 49.25 | 73.5 |
-| 25000 | 0.4040 | 62.25 | 187.0 | 12.8246 | 25.2755 | 98.91 | 12.384 | 44.75 | 65.5 |
-| 27500 | 0.4444 | 60.25 | 175.0 | 12.7070 | 25.2654 | 98.949 | 12.388 | 43.25 | 72.5 |
-| 30000 | 0.4848 | 62.25 | 183.0 | 12.7168 | 25.2653 | 98.95 | 12.389 | 42.25 | 87.0 |
-| 32500 | 0.5253 | 59.0 | 184.0 | 12.6674 | 25.2119 | 99.16 | 12.415 | 37.75 | 70.5 |
-| 35000 | 0.5657 | 58.0 | 176.0 | 12.5288 | 25.2238 | 99.113 | 12.409 | 34.75 | 50.0 |
-| 37500 | 0.6061 | 56.5 | 166.0 | 12.4810 | 25.192 | 99.238 | 12.425 | 36.75 | 69.5 |
-| 40000 | 0.6465 | 55.0 | 151.0 | 12.4422 | 25.2105 | 99.165 | 12.415 | 34.0 | 48.25 |
-| 42500 | 0.6869 | 52.75 | 161.0 | 12.3894 | 25.258 | 98.979 | 12.392 | 33.5 | 58.75 |
-| 45000 | 0.7273 | 51.25 | 134.0 | 12.1660 | 25.1916 | 99.239 | 12.425 | 29.75 | 43.0 |
-| 47500 | 0.7677 | 48.75 | 129.0 | 12.125 | 25.243 | 99.037 | 12.399 | 28.625 | 38.25 |
-| 50000 | 0.8081 | 49.75 | 126.5 | 12.0924 | 25.25 | 99.01 | 12.396 | 28.375 | 35.0 |
-| 52500 | 0.8485 | 50.75 | 125.0 | 12.0760 | 25.2184 | 99.134 | 12.412 | 28.0 | 39.0 |
-| 55000 | 0.8889 | 49.75 | 124.5 | 12.0411 | 25.2538 | 98.995 | 12.394 | 27.625 | 36.75 |
-| 57500 | 0.9293 | 49.0 | 120.5 | 12.0289 | 25.2405 | 99.047 | 12.401 | 27.375 | 34.5 |
-| 60000 | 0.9697 | 48.75 | 120.5 | 12.0196 | 25.192 | 99.238 | 12.425 | 27.375 | 35.0 |
-| 61875 | 1.0 | 49.0 | 121.0 | 12.0190 | 25.1853 | 99.264 | 12.428 | 27.375 | 35.0 |
+| 0 | 0 | 2473901162496.0 | 170424302305280.0 | 21.1784 | 25.4152 | 98.366 | 12.315 | 4060086272.0 | 71468255805440.0 |
+| 2500 | 0.0404 | 764.0 | 5376.0 | 2.3596 | 25.4066 | 98.4 | 12.32 | 470.0 | 2096.0 |
+| 5000 | 0.0808 | 312.0 | 1344.0 | 1.6581 | 25.4693 | 98.157 | 12.289 | 232.0 | 241.0 |
+| 7500 | 0.1212 | 210.0 | 744.0 | 1.3794 | 25.4328 | 98.298 | 12.307 | 172.0 | 154.0 |
+| 10000 | 0.1616 | 158.0 | 560.0 | 1.1831 | 25.4425 | 98.261 | 12.302 | 129.0 | 156.0 |
+| 12500 | 0.2020 | 117.0 | 456.0 | 0.9687 | 25.4175 | 98.357 | 12.314 | 98.5 | 140.0 |
+| 15000 | 0.2424 | 103.5 | 418.0 | 0.8616 | 25.4115 | 98.381 | 12.317 | 84.5 | 110.5 |
+| 17500 | 0.2828 | 91.0 | 340.0 | 0.7766 | 25.4459 | 98.248 | 12.301 | 74.0 | 107.5 |
+| 20000 | 0.3232 | 78.5 | 300.0 | 0.7221 | 25.4945 | 98.06 | 12.277 | 66.0 | 173.0 |
+| 22500 | 0.3636 | 72.0 | 230.0 | 0.6270 | 25.4673 | 98.165 | 12.29 | 59.0 | 99.0 |
+| 25000 | 0.4040 | 67.5 | 211.0 | 0.5925 | 25.4934 | 98.064 | 12.278 | 49.75 | 118.5 |
+| 27500 | 0.4444 | 66.0 | 208.0 | 0.5720 | 25.4123 | 98.378 | 12.317 | 47.75 | 159.0 |
+| 30000 | 0.4848 | 65.5 | 215.0 | 0.5761 | 25.481 | 98.112 | 12.284 | 50.75 | 86.5 |
+| 32500 | 0.5253 | 64.5 | 189.0 | 0.5649 | 25.4585 | 98.199 | 12.295 | 47.0 | 80.5 |
+| 35000 | 0.5657 | 61.5 | 190.0 | 0.5213 | 25.4186 | 98.353 | 12.314 | 43.75 | 125.5 |
+| 37500 | 0.6061 | 61.75 | 167.0 | 0.5083 | 25.4687 | 98.16 | 12.29 | 46.5 | 74.0 |
+| 40000 | 0.6465 | 59.25 | 182.0 | 0.4943 | 25.4354 | 98.288 | 12.306 | 40.25 | 64.5 |
+| 42500 | 0.6869 | 58.75 | 165.0 | 0.4828 | 25.4783 | 98.123 | 12.285 | 41.75 | 83.0 |
+| 45000 | 0.7273 | 53.75 | 161.0 | 0.4080 | 25.4586 | 98.199 | 12.294 | 36.0 | 79.0 |
+| 47500 | 0.7677 | 53.0 | 135.0 | 0.3898 | 25.4198 | 98.349 | 12.313 | 35.0 | 72.0 |
+| 50000 | 0.8081 | 52.5 | 138.0 | 0.3778 | 25.4834 | 98.103 | 12.283 | 34.25 | 42.0 |
+| 52500 | 0.8485 | 50.75 | 132.0 | 0.3690 | 25.432 | 98.301 | 12.307 | 34.0 | 48.5 |
+| 55000 | 0.8889 | 50.5 | 126.0 | 0.3554 | 25.4536 | 98.218 | 12.297 | 32.5 | 37.25 |
+| 57500 | 0.9293 | 49.75 | 127.0 | 0.3516 | 25.4028 | 98.415 | 12.321 | 32.25 | 36.0 |
+| 60000 | 0.9697 | 49.75 | 124.5 | 0.3487 | 25.4725 | 98.145 | 12.288 | 32.0 | 35.75 |
+| 61875 | 1.0 | 49.75 | 125.0 | 0.3485 | 25.4582 | 98.2 | 12.295 | 32.0 | 35.75 |
 
 # Resource Usage Comparison
 
-- VRAM Use: 7.7823 GB
+- VRAM Use: 7.7851 GB
 
 # Distillation (Teacher -> Student) Architecture Difference:
 
@@ -93,7 +93,7 @@ More information needed
 <br/>
 
 # Train Dataset
-Trained on 145,731,804 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 145,731,804 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=kl, 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=kl, layer_mapper=layer-2))`
 - train_embeddings: `True`
-- lr_scheduler: `<torch.optim.lr_scheduler.LambdaLR object at 0x7f040856c4f0>`
+- lr_scheduler: `<torch.optim.lr_scheduler.LambdaLR object at 0x7f0408367160>`
 - student_model_name_or_path: `None`
 - student_config_name_or_path: `None`
 - student_model_config: `None`

logs/attn_loss_fn=kl, attn_weight=5, layer_mapper=all, projector=linear/events.out.tfevents.1724411654.e3f806ea38c9

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