---
base_model: gpt2
datasets:
- wikimedia/wikipedia
library_name: Distily
license: mit
tags:
- bitnet
- 1.58b
- generated_from_trainer
model-index:
- name: distily_miles_projector_experiment
  results: []
---


# Summary

Distilled with [Distily](https://github.com/lapp0/distily) library
using teacher model [gpt2](https://huggingface.co/gpt2)
on dataset [wikimedia/wikipedia](https://huggingface.co/datasets/wikimedia/wikipedia).

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment.

# Model description

More information needed

# Intended uses & limitations

More information needed
-->

# Model Architecture:
- **Architecture**: `GPT2LMHeadModel`
- **Total Parameters**: 124,439,808
- **Data Type (dtype)**: torch.bfloat16
- **Model Size**: 0.24 GB


# Evaluation Metrics Comparison

| step | epoch | enwikippl | frwikippl | loss | runtime | samples_per_second | steps_per_second | tinystoriesppl | zhwikippl |
| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
| **teacher eval** |  | 36.25 | 77.0 |  |  |  |  | 11.75 | 21.375 |
| 0 | 0 | 10788957847552.0 | 93458488360960.0 | 23.9652 | 41.1128 | 60.808 | 7.613 | 3539992576.0 | 57174604644352.0 |
| 2500 | 0.0404 | 888.0 | 5536.0 | 3.2958 | 40.0823 | 62.372 | 7.809 | 492.0 | 4576.0 |
| 5000 | 0.0808 | 380.0 | 1448.0 | 2.4808 | 41.6839 | 59.975 | 7.509 | 255.0 | 400.0 |
| 7500 | 0.1212 | 250.0 | 748.0 | 2.1083 | 44.1725 | 56.596 | 7.086 | 197.0 | 233.0 |
| 10000 | 0.1616 | 189.0 | 616.0 | 1.8890 | 43.9453 | 56.889 | 7.122 | 156.0 | 216.0 |
| 12500 | 0.2020 | 140.0 | 488.0 | 1.6027 | 42.1657 | 59.29 | 7.423 | 119.0 | 178.0 |
| 15000 | 0.2424 | 113.5 | 434.0 | 1.4410 | 42.3062 | 59.093 | 7.398 | 94.0 | 183.0 |
| 17500 | 0.2828 | 92.5 | 340.0 | 1.3090 | 42.413 | 58.944 | 7.38 | 76.5 | 165.0 |
| 20000 | 0.3232 | 79.5 | 308.0 | 1.1661 | 40.1951 | 62.197 | 7.787 | 73.0 | 151.0 |
| 22500 | 0.3636 | 68.0 | 229.0 | 0.9997 | 41.1581 | 60.741 | 7.605 | 56.75 | 122.5 |
| 25000 | 0.4040 | 63.25 | 201.0 | 0.9359 | 40.9228 | 61.091 | 7.649 | 50.75 | 99.5 |
| 27500 | 0.4444 | 59.25 | 218.0 | 0.8936 | 40.1195 | 62.314 | 7.802 | 46.25 | 116.5 |
| 30000 | 0.4848 | 59.25 | 204.0 | 0.8841 | 42.297 | 59.106 | 7.4 | 49.75 | 87.0 |
| 32500 | 0.5253 | 57.5 | 184.0 | 0.8730 | 40.8597 | 61.185 | 7.66 | 44.25 | 101.5 |
| 35000 | 0.5657 | 56.0 | 177.0 | 0.8049 | 44.9443 | 55.624 | 6.964 | 39.75 | 62.25 |
| 37500 | 0.6061 | 55.0 | 163.0 | 0.7798 | 44.8966 | 55.684 | 6.972 | 43.5 | 93.5 |
| 40000 | 0.6465 | 52.0 | 166.0 | 0.7611 | 40.5252 | 61.69 | 7.724 | 37.25 | 73.5 |
| 42500 | 0.6869 | 51.5 | 159.0 | 0.7336 | 41.7519 | 59.878 | 7.497 | 38.5 | 70.0 |
| 45000 | 0.7273 | 46.25 | 143.0 | 0.6241 | 40.2456 | 62.119 | 7.777 | 32.25 | 54.5 |
| 47500 | 0.7677 | 45.75 | 136.0 | 0.5998 | 42.1189 | 59.356 | 7.431 | 31.5 | 43.75 |
| 50000 | 0.8081 | 45.25 | 135.0 | 0.5841 | 40.1272 | 62.302 | 7.8 | 31.0 | 43.75 |
| 52500 | 0.8485 | 44.25 | 128.0 | 0.5705 | 41.9206 | 59.637 | 7.466 | 31.25 | 43.25 |
| 55000 | 0.8889 | 43.5 | 125.5 | 0.5532 | 40.1106 | 62.328 | 7.803 | 29.875 | 38.25 |
| 57500 | 0.9293 | 43.5 | 125.5 | 0.5470 | 40.2997 | 62.035 | 7.767 | 29.875 | 38.0 |
| 60000 | 0.9697 | 43.5 | 126.0 | 0.5432 | 39.9729 | 62.542 | 7.83 | 29.625 | 37.5 |
| 61875 | 1.0 | 43.5 | 126.0 | 0.5426 | 41.9287 | 59.625 | 7.465 | 29.625 | 37.5 |

# Resource Usage Comparison

- VRAM Use: 7.7831 GB

# Distillation (Teacher -> Student) Architecture Difference:

- **Architecture**: `GPT2LMHeadModel` -> `GPT2LMHeadModel`
- **Total Parameters**: 124,439,808 -> 124,439,808
- **Data Type (dtype)**: torch.bfloat16 -> torch.bfloat16
- **Model Size**: 0.24 GB -> 0.24 GB

<details>
<summary>Module Diff Details</summary>

```diff

```

</details>
<br/>

# Train 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`
- Split: `train`


# 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=raw_mse, layer_mapper=layer-2))
```

# Hyperparameters
The following hyperparameters were used during training:

<details>
<summary>Expand</summary>

- 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: `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=raw_mse, layer_mapper=layer-2))`
- train_embeddings: `True`
- lr_scheduler: `<torch.optim.lr_scheduler.LambdaLR object at 0x7fd7d547a6e0>`
- student_model_name_or_path: `None`
- student_config_name_or_path: `None`
- student_model_config: `None`
- reinitialize_weights: `None`
- copy_teacher_modules: `[('lm_head', False)]`
- student_model_as_bitnet: `True`
- student_model_compile: `False`
- dropout: `None`
- teacher_model_name_or_path: `gpt2`
- teacher_load_in_8bit: `False`
- teacher_load_in_4bit: `False`
- teacher_model_compile: `False`
- dataset_uri: `wikimedia/wikipedia`
- dataset_subset: `20231101.en`
- dataset_split: `train`
- dataset_column_name: `text`
- dataset_sample_size: `250000`
- dataset_test_size: `0.01`
- gradient_accumulation_steps: `1`
- weight_decay: `0.0`
- max_grad_norm: `1.0`
- warmup_ratio: `0.5`
- warmup_steps: `0`
- gradient_checkpointing: `True`

</details>
<br/>


# Framework Versions
- Distily 0.2.0
- Transformers 4.44.2
- Pytorch 2.3.0
- Datasets 2.21.0