Training in progress, step 61875
Browse files- README.md +22 -35
- logs/attn_loss_fn=mse, attn_weight=10.0, hidden_weight=10.0, hs_loss_fn=raw_mse, learning_rate=0.0004, warmup_ratio=0/events.out.tfevents.1723766581.b7d545513dcf +3 -0
- logs/attn_loss_fn=mse, attn_weight=10.0, hs_loss_fn=raw_mse, hs_weight=10.0, learning_rate=0.0004, warmup_ratio=0/events.out.tfevents.1723766901.b7d545513dcf +3 -0
- logs/attn_loss_fn=raw_mse, attn_weight=10.0, hs_loss_fn=mse, hs_weight=10.0, learning_rate=0.0004/events.out.tfevents.1723766193.b7d545513dcf +2 -2
- model.safetensors +1 -1
- training_args.bin +1 -1
README.md
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@@ -15,14 +15,14 @@ This student model is distilled from the teacher model [roneneldan/TinyStories-3
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The [Distily](https://github.com/lapp0/distily) library was used for this distillation.
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It achieves the following results on the evaluation set:
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- eval_enwikippl:
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- eval_frwikippl:
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- eval_zhwikippl:
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- eval_tinystoriesppl:
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- eval_loss:
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- eval_runtime:
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- eval_samples_per_second:
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- eval_steps_per_second: 9.
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment.
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### Training hyperparameters
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The following hyperparameters were used during training:
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- distillation_objective: DistillationObjective(logits_loss_component=LossComponent(label=logits, weight=1, loss_fn=kl, layer_mapper=None, projector=None), hs_loss_component=LossComponent(label=hs, weight=10.0, loss_fn=
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- train_embeddings: True
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- learning_rate: 0.
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- train_batch_size:
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- eval_batch_size: 8
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- num_epochs: 1.0
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### Resource Usage
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Peak GPU Memory:
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### Eval-Phase Metrics
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| step | epoch | enwikippl | frwikippl | loss | runtime | samples_per_second | steps_per_second | tinystoriesppl | zhwikippl |
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| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
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| **teacher eval** | | 169.9865 | 47377.9414 | | | | | 3.9789 | 4998.1294 |
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| 27000 | 0.4364 | 141.8706 | 28049.1211 | 7.4454 | 65.831 | 75.952 | 9.494 | 10.5280 | 235255.9062 |
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| 30000 | 0.4848 | 144.7906 | 28084.7207 | 7.4449 | 65.9422 | 75.824 | 9.478 | 10.7119 | 240716.3594 |
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| 33000 | 0.5333 | 149.6832 | 28237.4258 | 7.4454 | 65.807 | 75.98 | 9.497 | 11.2524 | 244013.9531 |
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| 36000 | 0.5818 | 148.6030 | 27445.3125 | 7.4453 | 65.6651 | 76.144 | 9.518 | 11.2729 | 236893.5625 |
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| 39000 | 0.6303 | 142.9683 | 27676.2949 | 7.4447 | 65.6729 | 76.135 | 9.517 | 10.6589 | 235381.5781 |
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| 42000 | 0.6788 | 146.5510 | 27895.4648 | 7.4449 | 65.6881 | 76.117 | 9.515 | 10.9904 | 239690.7812 |
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| 45000 | 0.7273 | 149.2144 | 28023.4531 | 7.4454 | 65.9058 | 75.866 | 9.483 | 11.2701 | 240716.3594 |
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| 48000 | 0.7758 | 144.2086 | 28243.4043 | 7.4449 | 65.873 | 75.904 | 9.488 | 10.7022 | 244339.7188 |
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| 51000 | 0.8242 | 141.9915 | 27781.7559 | 7.4450 | 65.9256 | 75.843 | 9.48 | 10.5589 | 239563.0469 |
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| 54000 | 0.8727 | 145.6399 | 28219.5234 | 7.4451 | 65.6892 | 76.116 | 9.515 | 10.8693 | 238542.6094 |
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| 57000 | 0.9212 | 144.2365 | 27040.4609 | 7.4445 | 65.6838 | 76.122 | 9.515 | 10.8312 | 227175.6875 |
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| 60000 | 0.9697 | 144.3482 | 26979.5938 | 7.4447 | 65.623 | 76.193 | 9.524 | 10.9257 | 232138.6562 |
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| 61875 | 1.0 | 146.9147 | 28084.7207 | 7.4450 | 65.6082 | 76.21 | 9.526 | 10.9569 | 237146.5 |
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### Framework versions
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- Distily 0.2.0
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- Transformers 4.44.0
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- Pytorch 2.3.0
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- Datasets 2.
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The [Distily](https://github.com/lapp0/distily) library was used for this distillation.
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It achieves the following results on the evaluation set:
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- eval_enwikippl: 4707.3955
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- eval_frwikippl: 38983.5547
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- eval_zhwikippl: 53243.9219
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- eval_tinystoriesppl: 1636.1367
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- eval_loss: 5.6090
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- eval_runtime: 33.6693
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- eval_samples_per_second: 74.252
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- eval_steps_per_second: 9.296
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment.
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### Training hyperparameters
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The following hyperparameters were used during training:
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- distillation_objective: DistillationObjective(logits_loss_component=LossComponent(label=logits, weight=1, loss_fn=kl, layer_mapper=None, projector=None), hs_loss_component=LossComponent(label=hs, weight=10.0, loss_fn=mse, layer_mapper=None, projector=None), attn_loss_component=LossComponent(label=attn, weight=10.0, loss_fn=raw_mse, layer_mapper=None, projector=None))
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- train_embeddings: True
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- learning_rate: 0.0004
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- train_batch_size: 16
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- eval_batch_size: 8
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- num_epochs: 1.0
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### Resource Usage
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Peak GPU Memory: 16.2515 GB
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### Eval-Phase Metrics
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| step | epoch | enwikippl | frwikippl | loss | runtime | samples_per_second | steps_per_second | tinystoriesppl | zhwikippl |
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| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
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| **teacher eval** | | 169.9865 | 47377.9414 | | | | | 3.9789 | 4998.1294 |
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| 0 | 0 | 27548.9473 | 81896.5703 | 7.1177 | 33.5606 | 74.492 | 9.326 | 15431.1592 | 64176.7344 |
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| 2000 | 0.1293 | 4707.3955 | 39038.5078 | 5.6090 | 33.5798 | 74.45 | 9.321 | 1635.3256 | 53243.9219 |
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| 4000 | 0.2586 | 4704.4829 | 38983.5547 | 5.6090 | 33.4351 | 74.772 | 9.361 | 1632.6235 | 53243.9219 |
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| 6000 | 0.3879 | 4717.6201 | 38972.5898 | 5.6090 | 33.4912 | 74.646 | 9.346 | 1638.3024 | 53243.9219 |
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| 8000 | 0.5172 | 4707.3955 | 38983.5547 | 5.6090 | 33.6693 | 74.252 | 9.296 | 1636.1367 | 53243.9219 |
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| 10000 | 0.6465 | 4707.3955 | 38994.5625 | 5.6090 | 33.5614 | 74.49 | 9.326 | 1636.4075 | 53243.9219 |
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| 12000 | 0.7757 | 4707.3955 | 39005.5352 | 5.6090 | 33.5763 | 74.457 | 9.322 | 1634.2444 | 53243.9219 |
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| 14000 | 0.9050 | 4708.1274 | 38994.5625 | 5.6090 | 33.4805 | 74.67 | 9.349 | 1636.4075 | 53243.9219 |
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| 15469 | 1.0 | 4704.4829 | 39027.5234 | 5.6090 | 33.5496 | 74.517 | 9.329 | 1632.6235 | 53243.9219 |
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### Framework versions
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- Distily 0.2.0
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- Transformers 4.44.0
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- Pytorch 2.3.0
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- Datasets 2.21.0
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logs/attn_loss_fn=mse, attn_weight=10.0, hidden_weight=10.0, hs_loss_fn=raw_mse, learning_rate=0.0004, warmup_ratio=0/events.out.tfevents.1723766581.b7d545513dcf
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training_args.bin
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