---
base_model: gpt2
library_name: Distily
license: mit
tags:
- bitnet
- 1.58b
- generated_from_trainer
model-index:
- name: distily_bitnet_gpt2
  results: []
---

# distily_bitnet_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: 179.0
- eval_frwikippl: 624.0
- eval_zhwikippl: 166.0
- eval_tinystoriesppl: 159.0
- eval_loss: 1.8254
- eval_runtime: 30.606
- eval_samples_per_second: 81.683
- eval_steps_per_second: 10.227

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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=0, loss_fn=None, layer_mapper=None, projector=None), attn_loss_component=LossComponent(label=attn, weight=1.0, loss_fn=cos, layer_mapper=layer-2, 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: cosine
- lr_scheduler_warmup_ratio: 0.5
- num_epochs: 1.0

### Resource Usage
Peak GPU Memory: 7.7840 GB

### Eval-Phase Metrics
| 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 | 1082331758592.0 | 57174604644352.0 | 19.3008 | 30.4471 | 82.11 | 10.28 | 5268045824.0 | 25013889531904.0 |
| 1000 | 0.0162 | 8320.0 | 67072.0 | 4.7159 | 30.4361 | 82.139 | 10.284 | 4640.0 | 399360.0 |
| 2000 | 0.0323 | 1352.0 | 6688.0 | 3.3630 | 30.4661 | 82.058 | 10.274 | 856.0 | 42240.0 |
| 3000 | 0.0485 | 644.0 | 4192.0 | 2.7949 | 30.5076 | 81.947 | 10.26 | 410.0 | 2160.0 |
| 4000 | 0.0646 | 452.0 | 2672.0 | 2.5266 | 30.4909 | 81.992 | 10.265 | 308.0 | 592.0 |
| 5000 | 0.0808 | 348.0 | 1504.0 | 2.2655 | 30.4813 | 82.017 | 10.269 | 262.0 | 288.0 |
| 6000 | 0.0970 | 270.0 | 1072.0 | 2.0833 | 30.4751 | 82.034 | 10.271 | 223.0 | 225.0 |
| 7000 | 0.1131 | 226.0 | 944.0 | 1.9806 | 30.4982 | 81.972 | 10.263 | 189.0 | 182.0 |
| 8000 | 0.1293 | 198.0 | 752.0 | 1.8914 | 30.4573 | 82.082 | 10.277 | 172.0 | 159.0 |
| 9000 | 0.1455 | 179.0 | 624.0 | 1.8254 | 30.606 | 81.683 | 10.227 | 159.0 | 166.0 |
| 10000 | 0.1616 | 172.0 | 568.0 | 1.7317 | 30.4969 | 81.976 | 10.263 | 138.0 | 141.0 |
| 11000 | 0.1778 | 139.0 | 486.0 | 1.6030 | 30.4606 | 82.073 | 10.276 | 111.5 | 147.0 |
| 12000 | 0.1939 | 124.5 | 460.0 | 1.5229 | 30.4525 | 82.095 | 10.278 | 98.0 | 120.0 |
| 13000 | 0.2101 | 112.0 | 432.0 | 1.4591 | 30.4583 | 82.079 | 10.276 | 87.0 | 116.0 |
| 14000 | 0.2263 | 102.5 | 400.0 | 1.4049 | 30.4283 | 82.16 | 10.286 | 83.0 | 110.5 |
| 15000 | 0.2424 | 102.5 | 408.0 | 1.3847 | 30.4854 | 82.007 | 10.267 | 82.5 | 162.0 |
| 16000 | 0.2586 | 94.5 | 382.0 | 1.3539 | 30.5028 | 81.96 | 10.261 | 73.5 | 120.5 |
| 17000 | 0.2747 | 86.5 | 330.0 | 1.3206 | 30.5072 | 81.948 | 10.26 | 72.0 | 125.5 |
| 18000 | 0.2909 | 86.5 | 314.0 | 1.2763 | 30.4803 | 82.02 | 10.269 | 67.5 | 121.5 |
| 19000 | 0.3071 | 89.0 | 342.0 | 1.2988 | 30.498 | 81.973 | 10.263 | 74.0 | 130.0 |
| 20000 | 0.3232 | 78.0 | 310.0 | 1.2192 | 30.473 | 82.04 | 10.271 | 60.75 | 106.5 |
| 21000 | 0.3394 | 75.5 | 284.0 | 1.1612 | 30.4776 | 82.028 | 10.27 | 56.75 | 121.0 |
| 22000 | 0.3556 | 73.5 | 233.0 | 1.1233 | 30.4472 | 82.109 | 10.28 | 57.5 | 124.0 |
| 23000 | 0.3717 | 68.0 | 231.0 | 1.0997 | 30.4635 | 82.065 | 10.275 | 55.75 | 93.5 |
| 24000 | 0.3879 | 65.5 | 213.0 | 1.0867 | 30.4576 | 82.081 | 10.277 | 52.0 | 102.0 |
| 25000 | 0.4040 | 63.0 | 207.0 | 1.0647 | 30.4272 | 82.163 | 10.287 | 50.25 | 109.0 |
| 26000 | 0.4202 | 65.0 | 197.0 | 1.0463 | 30.5134 | 81.931 | 10.258 | 52.5 | 98.5 |
| 27000 | 0.4364 | 65.5 | 211.0 | 1.0503 | 30.4982 | 81.972 | 10.263 | 48.75 | 97.0 |
| 28000 | 0.4525 | 62.25 | 199.0 | 1.0304 | 30.4658 | 82.059 | 10.274 | 47.5 | 104.5 |
| 29000 | 0.4687 | 66.0 | 214.0 | 1.0459 | 30.4838 | 82.011 | 10.268 | 49.75 | 94.0 |
| 30000 | 0.4848 | 63.0 | 209.0 | 1.0378 | 30.4635 | 82.065 | 10.275 | 49.75 | 90.5 |
| 31000 | 0.5010 | 63.5 | 218.0 | 1.0314 | 30.5768 | 81.761 | 10.237 | 48.5 | 96.5 |
| 32000 | 0.5172 | 62.25 | 204.0 | 1.0225 | 30.4582 | 82.08 | 10.276 | 45.5 | 68.5 |
| 33000 | 0.5333 | 64.5 | 202.0 | 1.0209 | 30.4807 | 82.019 | 10.269 | 48.0 | 93.5 |
| 34000 | 0.5495 | 64.0 | 196.0 | 1.0165 | 30.4726 | 82.041 | 10.272 | 47.0 | 102.0 |
| 35000 | 0.5657 | 64.5 | 210.0 | 1.0109 | 30.4775 | 82.028 | 10.27 | 45.5 | 97.5 |
| 36000 | 0.5818 | 64.0 | 188.0 | 1.0084 | 30.4363 | 82.139 | 10.284 | 45.5 | 96.0 |
| 37000 | 0.5980 | 60.75 | 184.0 | 0.9871 | 30.479 | 82.024 | 10.269 | 44.25 | 77.5 |
| 38000 | 0.6141 | 59.75 | 190.0 | 0.9688 | 30.5442 | 81.849 | 10.247 | 43.5 | 74.0 |
| 39000 | 0.6303 | 59.0 | 172.0 | 0.9639 | 30.551 | 81.83 | 10.245 | 43.0 | 61.25 |
| 40000 | 0.6465 | 58.25 | 172.0 | 0.9537 | 30.489 | 81.997 | 10.266 | 42.0 | 69.0 |
| 41000 | 0.6626 | 60.5 | 176.0 | 0.9559 | 30.4953 | 81.98 | 10.264 | 41.0 | 60.5 |
| 42000 | 0.6788 | 57.75 | 184.0 | 0.9516 | 30.488 | 81.999 | 10.266 | 40.5 | 56.5 |
| 43000 | 0.6949 | 57.25 | 191.0 | 0.9369 | 30.4691 | 82.05 | 10.273 | 41.0 | 53.25 |
| 44000 | 0.7111 | 58.0 | 178.0 | 0.9370 | 30.4852 | 82.007 | 10.267 | 39.5 | 54.25 |
| 45000 | 0.7273 | 54.75 | 157.0 | 0.8904 | 30.4948 | 81.981 | 10.264 | 36.75 | 64.5 |
| 46000 | 0.7434 | 52.0 | 148.0 | 0.8656 | 30.5027 | 81.96 | 10.261 | 35.0 | 49.25 |
| 47000 | 0.7596 | 53.0 | 144.0 | 0.8540 | 30.4803 | 82.02 | 10.269 | 34.25 | 44.0 |
| 48000 | 0.7758 | 51.25 | 139.0 | 0.8430 | 30.452 | 82.097 | 10.278 | 33.0 | 44.25 |
| 49000 | 0.7919 | 52.0 | 146.0 | 0.8397 | 30.4747 | 82.035 | 10.271 | 33.5 | 41.75 |
| 50000 | 0.8081 | 51.75 | 140.0 | 0.8340 | 30.4359 | 82.14 | 10.284 | 33.5 | 40.0 |
| 51000 | 0.8242 | 50.25 | 136.0 | 0.8262 | 30.4828 | 82.013 | 10.268 | 32.5 | 40.25 |
| 52000 | 0.8404 | 50.5 | 138.0 | 0.8209 | 30.4471 | 82.11 | 10.28 | 32.25 | 37.5 |
| 53000 | 0.8566 | 50.25 | 140.0 | 0.8173 | 30.4685 | 82.052 | 10.273 | 32.0 | 36.0 |
| 54000 | 0.8727 | 49.75 | 136.0 | 0.8153 | 30.4908 | 81.992 | 10.265 | 31.875 | 36.5 |
| 55000 | 0.8889 | 50.0 | 138.0 | 0.8132 | 30.4798 | 82.022 | 10.269 | 31.75 | 36.75 |
| 56000 | 0.9051 | 50.0 | 136.0 | 0.8123 | 30.4915 | 81.99 | 10.265 | 31.75 | 36.5 |
| 57000 | 0.9212 | 50.0 | 135.0 | 0.8117 | 30.5178 | 81.919 | 10.256 | 31.75 | 36.25 |
| 58000 | 0.9374 | 49.75 | 134.0 | 0.8113 | 30.4452 | 82.115 | 10.281 | 31.625 | 36.25 |
| 59000 | 0.9535 | 50.0 | 134.0 | 0.8111 | 30.5075 | 81.947 | 10.26 | 31.75 | 36.25 |
| 60000 | 0.9697 | 50.0 | 134.0 | 0.8112 | 30.4961 | 81.978 | 10.264 | 31.75 | 36.25 |
| 61000 | 0.9859 | 50.0 | 134.0 | 0.8112 | 30.5439 | 81.849 | 10.248 | 31.75 | 36.25 |
| 61875 | 1.0 | 50.0 | 134.0 | 0.8112 | 30.6307 | 81.618 | 10.219 | 31.75 | 36.25 |

### Framework versions
- Distily 0.2.0
- Transformers 4.44.0
- Pytorch 2.3.0
- Datasets 2.21.0