---
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: 87.5
- eval_frwikippl: 358.0
- eval_zhwikippl: 139.0
- eval_tinystoriesppl: 72.5
- eval_loss: 0.6931
- eval_runtime: 29.8206
- eval_samples_per_second: 83.835
- eval_steps_per_second: 10.496

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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=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: 7.5008 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 | 820338753536.0 | 43705587204096.0 | 18.6434 | 30.0294 | 83.252 | 10.423 | 4731174912.0 | 17729624997888.0 |
| 1000 | 0.0162 | 324.0 | 1576.0 | 1.4569 | 29.8145 | 83.852 | 10.498 | 258.0 | 386.0 |
| 2000 | 0.0323 | 220.0 | 844.0 | 1.2562 | 29.8871 | 83.648 | 10.473 | 184.0 | 203.0 |
| 3000 | 0.0485 | 182.0 | 628.0 | 1.1014 | 29.8663 | 83.706 | 10.48 | 141.0 | 178.0 |
| 4000 | 0.0646 | 148.0 | 520.0 | 0.9878 | 29.8318 | 83.803 | 10.492 | 121.0 | 162.0 |
| 5000 | 0.0808 | 130.0 | 456.0 | 0.9061 | 29.8914 | 83.636 | 10.471 | 103.5 | 150.0 |
| 6000 | 0.0970 | 117.0 | 426.0 | 0.8448 | 29.8301 | 83.808 | 10.493 | 95.5 | 165.0 |
| 7000 | 0.1131 | 105.0 | 460.0 | 0.7878 | 29.8233 | 83.827 | 10.495 | 86.0 | 150.0 |
| 8000 | 0.1293 | 98.5 | 396.0 | 0.7433 | 29.8713 | 83.692 | 10.478 | 78.0 | 143.0 |
| 9000 | 0.1455 | 87.5 | 358.0 | 0.6931 | 29.8206 | 83.835 | 10.496 | 72.5 | 139.0 |
| 10000 | 0.1616 | 82.0 | 340.0 | 0.6355 | 29.8348 | 83.795 | 10.491 | 67.5 | 132.0 |
| 11000 | 0.1778 | 77.5 | 330.0 | 0.5981 | 29.8369 | 83.789 | 10.49 | 60.75 | 113.0 |
| 12000 | 0.1939 | 75.0 | 286.0 | 0.5715 | 29.8463 | 83.762 | 10.487 | 62.0 | 152.0 |
| 13000 | 0.2101 | 73.0 | 249.0 | 0.5484 | 29.8498 | 83.753 | 10.486 | 55.5 | 141.0 |
| 14000 | 0.2263 | 72.5 | 245.0 | 0.5344 | 29.8153 | 83.85 | 10.498 | 54.75 | 85.5 |
| 15000 | 0.2424 | 73.0 | 246.0 | 0.5171 | 29.8338 | 83.798 | 10.491 | 55.5 | 87.0 |
| 16000 | 0.2586 | 70.5 | 237.0 | 0.5125 | 29.8543 | 83.74 | 10.484 | 52.5 | 92.0 |
| 17000 | 0.2747 | 70.0 | 219.0 | 0.4954 | 29.8236 | 83.826 | 10.495 | 56.25 | 160.0 |
| 18000 | 0.2909 | 67.5 | 250.0 | 0.5031 | 29.8194 | 83.838 | 10.497 | 52.5 | 173.0 |
| 19000 | 0.3071 | 72.0 | 223.0 | 0.4795 | 29.8542 | 83.74 | 10.484 | 51.5 | 151.0 |
| 20000 | 0.3232 | 68.0 | 218.0 | 0.4735 | 29.8718 | 83.691 | 10.478 | 52.0 | 151.0 |
| 21000 | 0.3394 | 67.5 | 221.0 | 0.4795 | 29.8655 | 83.709 | 10.48 | 52.5 | 190.0 |
| 22000 | 0.3556 | 68.5 | 223.0 | 0.4733 | 29.8778 | 83.674 | 10.476 | 52.0 | 96.0 |
| 23000 | 0.3717 | 69.0 | 204.0 | 0.4633 | 29.8215 | 83.832 | 10.496 | 48.75 | 104.0 |
| 24000 | 0.3879 | 66.0 | 222.0 | 0.4587 | 29.843 | 83.772 | 10.488 | 50.0 | 122.0 |
| 25000 | 0.4040 | 67.0 | 216.0 | 0.4568 | 29.8561 | 83.735 | 10.484 | 48.75 | 92.0 |
| 26000 | 0.4202 | 70.0 | 214.0 | 0.4556 | 29.8665 | 83.706 | 10.48 | 49.0 | 103.5 |
| 27000 | 0.4364 | 66.0 | 220.0 | 0.4601 | 29.8646 | 83.711 | 10.481 | 48.5 | 95.5 |
| 28000 | 0.4525 | 65.0 | 205.0 | 0.4516 | 29.8541 | 83.741 | 10.484 | 46.5 | 150.0 |
| 29000 | 0.4687 | 66.5 | 223.0 | 0.4496 | 29.8307 | 83.806 | 10.493 | 46.5 | 102.5 |
| 30000 | 0.4848 | 66.5 | 237.0 | 0.4509 | 29.8678 | 83.702 | 10.48 | 46.25 | 137.0 |
| 31000 | 0.5010 | 64.5 | 219.0 | 0.4445 | 29.851 | 83.749 | 10.485 | 46.0 | 97.5 |
| 32000 | 0.5172 | 64.0 | 200.0 | 0.4380 | 29.8955 | 83.625 | 10.47 | 49.25 | 101.0 |
| 33000 | 0.5333 | 64.5 | 204.0 | 0.4379 | 29.838 | 83.786 | 10.49 | 49.0 | 85.5 |
| 34000 | 0.5495 | 64.0 | 217.0 | 0.4419 | 29.8427 | 83.773 | 10.488 | 46.25 | 76.0 |
| 35000 | 0.5657 | 72.5 | 229.0 | 0.4345 | 29.8803 | 83.667 | 10.475 | 50.0 | 128.0 |
| 36000 | 0.5818 | 67.5 | 203.0 | 0.4349 | 30.0752 | 83.125 | 10.407 | 45.0 | 147.0 |
| 37000 | 0.5980 | 65.5 | 205.0 | 0.4354 | 29.8558 | 83.736 | 10.484 | 47.75 | 129.0 |
| 38000 | 0.6141 | 63.75 | 208.0 | 0.4375 | 29.868 | 83.702 | 10.479 | 46.0 | 108.5 |
| 39000 | 0.6303 | 64.0 | 215.0 | 0.4395 | 30.2231 | 82.718 | 10.356 | 45.5 | 125.0 |
| 40000 | 0.6465 | 64.5 | 197.0 | 0.4278 | 29.9055 | 83.597 | 10.466 | 46.0 | 84.5 |
| 41000 | 0.6626 | 62.25 | 186.0 | 0.4285 | 29.951 | 83.47 | 10.45 | 44.75 | 80.0 |
| 42000 | 0.6788 | 62.75 | 225.0 | 0.4301 | 29.835 | 83.794 | 10.491 | 46.25 | 168.0 |
| 43000 | 0.6949 | 65.5 | 224.0 | 0.4222 | 29.874 | 83.685 | 10.477 | 46.5 | 139.0 |
| 44000 | 0.7111 | 63.5 | 197.0 | 0.4294 | 29.9084 | 83.589 | 10.465 | 45.75 | 125.5 |
| 45000 | 0.7273 | 63.0 | 192.0 | 0.4263 | 29.8797 | 83.669 | 10.475 | 46.25 | 95.0 |
| 46000 | 0.7434 | 63.25 | 198.0 | 0.4266 | 29.8479 | 83.758 | 10.487 | 44.75 | 120.5 |
| 47000 | 0.7596 | 64.5 | 213.0 | 0.4247 | 29.8769 | 83.677 | 10.476 | 44.5 | 120.5 |
| 48000 | 0.7758 | 62.25 | 202.0 | 0.4214 | 29.8514 | 83.748 | 10.485 | 42.75 | 83.5 |
| 49000 | 0.7919 | 63.75 | 204.0 | 0.4230 | 29.8895 | 83.641 | 10.472 | 46.25 | 94.5 |
| 50000 | 0.8081 | 63.75 | 209.0 | 0.4218 | 29.9008 | 83.61 | 10.468 | 45.25 | 131.0 |
| 51000 | 0.8242 | 65.5 | 223.0 | 0.4213 | 29.8534 | 83.743 | 10.485 | 45.0 | 233.0 |
| 52000 | 0.8404 | 64.5 | 195.0 | 0.4132 | 29.8416 | 83.776 | 10.489 | 44.0 | 99.0 |
| 53000 | 0.8566 | 64.0 | 216.0 | 0.4259 | 29.8576 | 83.731 | 10.483 | 45.5 | 95.0 |
| 54000 | 0.8727 | 65.0 | 207.0 | 0.4207 | 29.8695 | 83.698 | 10.479 | 45.5 | 126.0 |
| 55000 | 0.8889 | 66.5 | 198.0 | 0.4141 | 29.8307 | 83.806 | 10.493 | 42.75 | 118.0 |
| 56000 | 0.9051 | 60.0 | 186.0 | 0.4209 | 29.866 | 83.707 | 10.48 | 43.75 | 142.0 |
| 57000 | 0.9212 | 62.25 | 192.0 | 0.4143 | 29.9063 | 83.594 | 10.466 | 45.0 | 78.0 |
| 58000 | 0.9374 | 63.5 | 205.0 | 0.4192 | 29.859 | 83.727 | 10.483 | 44.75 | 117.5 |
| 59000 | 0.9535 | 62.75 | 191.0 | 0.4202 | 29.8691 | 83.699 | 10.479 | 44.0 | 100.0 |
| 60000 | 0.9697 | 66.0 | 219.0 | 0.4149 | 29.9387 | 83.504 | 10.455 | 43.75 | 130.0 |
| 61000 | 0.9859 | 64.5 | 207.0 | 0.4162 | 29.8366 | 83.79 | 10.49 | 43.5 | 161.0 |
| 61875 | 1.0 | 61.5 | 204.0 | 0.4125 | 29.9423 | 83.494 | 10.453 | 44.25 | 223.0 |

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