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---
base_model: gpt2
library_name: Distily
license: mit
tags:
- generated_from_trainer
model-index:
- name: distily_bench_obj_cross_v2.15_gpt2
results: []
---
# distily_bench_obj_cross_v2.15_gpt2
This student model is distilled from the teacher model [gpt2](https://huggingface.co./gpt2) using the dataset (unspecified).
The [Distily](https://github.com/lapp0/distily) library was used for this distillation.
It achieves the following results on the evaluation set:
- eval_enwikippl: 84.0
- eval_frwikippl: 336.0
- eval_zhwikippl: 143.0
- eval_tinystoriesppl: 68.0
- eval_loss: 0.6821
- eval_runtime: 16.9876
- eval_samples_per_second: 58.866
- eval_steps_per_second: 7.358
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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## Model description
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## Intended uses & limitations
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## Training and evaluation data
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## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- 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=1.0, loss_fn=mse, layer_mapper=last, projector=None), attn_loss_component=LossComponent(label=attn, weight=0, loss_fn=None, layer_mapper=None, projector=None))
- train_embeddings: True
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 1.0
### Resource Usage
Peak GPU Memory: 7.7252 GB
### Eval-Phase Metrics
| step | epoch | enwikippl | frwikippl | loss | runtime | samples_per_second | steps_per_second | tinystoriesppl | zhwikippl |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| **teacher eval** | | 43.75 | 61.75 | | | | | 11.8125 | 19.125 |
| 0 | 0 | 2473901162496.0 | 170424302305280.0 | 20.7680 | 16.9731 | 58.917 | 7.365 | 4060086272.0 | 71468255805440.0 |
| 1000 | 0.0404 | 328.0 | 1488.0 | 1.5338 | 17.0565 | 58.629 | 7.329 | 243.0 | 608.0 |
| 2000 | 0.0808 | 229.0 | 792.0 | 1.3211 | 16.9911 | 58.854 | 7.357 | 190.0 | 260.0 |
| 3000 | 0.1212 | 178.0 | 624.0 | 1.1600 | 17.0018 | 58.817 | 7.352 | 149.0 | 176.0 |
| 4000 | 0.1616 | 147.0 | 580.0 | 1.0397 | 17.0215 | 58.749 | 7.344 | 119.0 | 161.0 |
| 5000 | 0.2020 | 128.0 | 516.0 | 0.9532 | 17.0153 | 58.771 | 7.346 | 102.0 | 159.0 |
| 6000 | 0.2424 | 111.0 | 410.0 | 0.8655 | 17.0046 | 58.808 | 7.351 | 90.0 | 147.0 |
| 7000 | 0.2828 | 104.5 | 410.0 | 0.8083 | 16.9742 | 58.913 | 7.364 | 82.0 | 145.0 |
| 8000 | 0.3232 | 97.5 | 382.0 | 0.7412 | 16.9735 | 58.915 | 7.364 | 74.0 | 128.0 |
| 9000 | 0.3636 | 84.0 | 336.0 | 0.6821 | 16.9876 | 58.866 | 7.358 | 68.0 | 143.0 |
| 10000 | 0.4040 | 77.5 | 312.0 | 0.6396 | 16.9771 | 58.903 | 7.363 | 65.0 | 140.0 |
| 11000 | 0.4444 | 75.5 | 280.0 | 0.5964 | 17.02 | 58.754 | 7.344 | 60.75 | 122.5 |
| 12000 | 0.4848 | 74.5 | 268.0 | 0.5797 | 16.9985 | 58.829 | 7.354 | 58.0 | 152.0 |
| 13000 | 0.5253 | 71.5 | 274.0 | 0.5537 | 16.9566 | 58.974 | 7.372 | 58.25 | 134.0 |
| 14000 | 0.5657 | 72.0 | 252.0 | 0.5429 | 16.9325 | 59.058 | 7.382 | 58.0 | 99.0 |
| 15000 | 0.6061 | 69.0 | 229.0 | 0.5308 | 16.9917 | 58.852 | 7.357 | 51.25 | 94.0 |
| 16000 | 0.6465 | 67.0 | 223.0 | 0.5209 | 16.9686 | 58.932 | 7.367 | 52.5 | 108.0 |
| 17000 | 0.6869 | 67.5 | 227.0 | 0.5046 | 16.979 | 58.896 | 7.362 | 54.25 | 118.0 |
| 18000 | 0.7273 | 67.5 | 244.0 | 0.5024 | 16.994 | 58.844 | 7.356 | 50.5 | 128.0 |
| 19000 | 0.7677 | 66.0 | 212.0 | 0.4931 | 16.9719 | 58.921 | 7.365 | 49.25 | 88.0 |
| 20000 | 0.8081 | 64.5 | 202.0 | 0.4925 | 17.0171 | 58.764 | 7.346 | 49.75 | 169.0 |
| 21000 | 0.8485 | 67.0 | 222.0 | 0.4839 | 16.9754 | 58.909 | 7.364 | 47.75 | 126.0 |
| 22000 | 0.8889 | 66.0 | 227.0 | 0.4759 | 16.9314 | 59.062 | 7.383 | 48.0 | 100.0 |
| 23000 | 0.9293 | 61.75 | 208.0 | 0.4704 | 16.9662 | 58.941 | 7.368 | 47.25 | 125.5 |
| 24000 | 0.9697 | 66.0 | 210.0 | 0.4706 | 17.0394 | 58.688 | 7.336 | 47.5 | 173.0 |
| 24750 | 1.0 | 63.75 | 218.0 | 0.4686 | 16.9798 | 58.894 | 7.362 | 46.75 | 82.5 |
### Framework versions
- Distily 0.2.0
- Transformers 4.44.0
- Pytorch 2.3.0
- Datasets 2.21.0
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