---
base_model: gpt2
datasets:
- wikimedia/wikipedia
library_name: Distily
license: mit
tags:
- bitnet
- 1.58b
- generated_from_trainer
model-index:
- name: distily_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** |  | 43.25 | 61.25 |  |  |  |  | 11.6875 | 19.125 |
| 0 | 0 | 2473901162496.0 | 170424302305280.0 | 30.7740 | 30.0939 | 83.073 | 10.401 | 4060086272.0 | 71468255805440.0 |
| 2500 | 0.0404 | 1192.0 | 11840.0 | 9.8250 | 30.1508 | 82.916 | 10.381 | 772.0 | 15040.0 |
| 5000 | 0.0808 | 412.0 | 2240.0 | 8.3978 | 30.1808 | 82.834 | 10.371 | 290.0 | 438.0 |
| 7500 | 0.1212 | 245.0 | 908.0 | 7.6620 | 30.1603 | 82.891 | 10.378 | 219.0 | 198.0 |
| 10000 | 0.1616 | 182.0 | 672.0 | 7.2415 | 30.2587 | 82.621 | 10.344 | 165.0 | 204.0 |
| 12500 | 0.2020 | 132.0 | 504.0 | 6.6895 | 30.1682 | 82.869 | 10.375 | 115.0 | 155.0 |
| 15000 | 0.2424 | 113.0 | 436.0 | 6.4127 | 30.186 | 82.82 | 10.369 | 89.5 | 137.0 |
| 17500 | 0.2828 | 92.5 | 340.0 | 6.1945 | 30.108 | 83.035 | 10.396 | 71.0 | 132.0 |
| 20000 | 0.3232 | 74.0 | 278.0 | 5.9293 | 30.1455 | 82.931 | 10.383 | 63.25 | 134.0 |
| 22500 | 0.3636 | 66.0 | 215.0 | 5.6606 | 30.0869 | 83.093 | 10.403 | 50.5 | 81.5 |
| 25000 | 0.4040 | 63.25 | 189.0 | 5.5592 | 30.1385 | 82.95 | 10.385 | 44.0 | 72.5 |
| 27500 | 0.4444 | 59.0 | 202.0 | 5.4963 | 30.1334 | 82.964 | 10.387 | 40.5 | 79.0 |
| 30000 | 0.4848 | 59.75 | 198.0 | 5.4789 | 30.1924 | 82.802 | 10.367 | 42.25 | 63.75 |
| 32500 | 0.5253 | 58.75 | 177.0 | 5.4552 | 30.1133 | 83.02 | 10.394 | 40.25 | 56.5 |
| 35000 | 0.5657 | 57.5 | 167.0 | 5.3773 | 30.1179 | 83.007 | 10.393 | 36.0 | 51.0 |
| 37500 | 0.6061 | 57.5 | 161.0 | 5.3443 | 30.1249 | 82.988 | 10.39 | 37.75 | 53.25 |
| 40000 | 0.6465 | 54.5 | 159.0 | 5.3258 | 30.1211 | 82.998 | 10.391 | 34.25 | 59.0 |
| 42500 | 0.6869 | 55.25 | 150.0 | 5.2937 | 30.1886 | 82.813 | 10.368 | 35.75 | 50.75 |
| 45000 | 0.7273 | 50.5 | 132.0 | 5.1564 | 30.1176 | 83.008 | 10.393 | 30.125 | 42.75 |
| 47500 | 0.7677 | 50.75 | 123.0 | 5.1254 | 30.0774 | 83.119 | 10.406 | 29.375 | 37.5 |
| 50000 | 0.8081 | 50.0 | 123.5 | 5.1100 | 30.1068 | 83.038 | 10.396 | 28.75 | 39.0 |
| 52500 | 0.8485 | 49.0 | 120.0 | 5.0958 | 30.1022 | 83.05 | 10.398 | 29.125 | 35.0 |
| 55000 | 0.8889 | 48.75 | 117.5 | 5.0753 | 30.968 | 80.728 | 10.107 | 28.125 | 35.75 |
| 57500 | 0.9293 | 48.25 | 117.0 | 5.0696 | 30.0872 | 83.092 | 10.403 | 28.0 | 33.25 |
| 60000 | 0.9697 | 48.25 | 117.0 | 5.0655 | 30.1265 | 82.983 | 10.39 | 28.0 | 33.0 |
| 61875 | 1.0 | 48.25 | 117.0 | 5.0651 | 30.1098 | 83.03 | 10.395 | 28.0 | 33.25 |

# Resource Usage Comparison

- VRAM Use: 7.7843 GB

`# Distillation (Teacher -> Student) Architecture Difference:

- **Architecture**: `GPT2LMHeadModel` -> `GPT2LMHeadModel`
- **Total Parameters**: 124,439,808 -> 124,439,808
- **Data Type (dtype)**: 124439808 -> 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=10.0, loss_fn=cos, 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=10.0, loss_fn=cos, layer_mapper=layer-2))`
- train_embeddings: `True`
- lr_scheduler: `<torch.optim.lr_scheduler.LambdaLR object at 0x7f010c102dd0>`
- 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 4.44.0
- Pytorch 2.3.0
- Datasets 2.21.0