README.md

---
base_model: gpt2
datasets:
- wikimedia/wikipedia
library_name: Distily
license: mit
tags:
- bitnet
- 1.58b
- generated_from_trainer
model-index:
- name: distily_multi_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 | 25.7744 | 25.131 | 99.479 | 12.455 | 4060086272.0 | 71468255805440.0 |
| 2500 | 0.0404 | 960.0 | 8064.0 | 6.1231 | 25.2285 | 99.094 | 12.407 | 652.0 | 6816.0 |
| 5000 | 0.0808 | 380.0 | 1896.0 | 5.0307 | 25.2563 | 98.985 | 12.393 | 270.0 | 286.0 |
| 7500 | 0.1212 | 230.0 | 824.0 | 4.5129 | 25.128 | 99.491 | 12.456 | 202.0 | 174.0 |
| 10000 | 0.1616 | 171.0 | 628.0 | 4.2261 | 25.2755 | 98.91 | 12.384 | 151.0 | 173.0 |
| 12500 | 0.2020 | 126.5 | 482.0 | 3.8533 | 25.2021 | 99.198 | 12.42 | 106.0 | 156.0 |
| 15000 | 0.2424 | 109.5 | 430.0 | 3.6650 | 25.2487 | 99.015 | 12.397 | 88.0 | 155.0 |
| 17500 | 0.2828 | 93.0 | 350.0 | 3.5198 | 25.1751 | 99.305 | 12.433 | 73.5 | 119.0 |
| 20000 | 0.3232 | 77.5 | 282.0 | 3.3352 | 25.2573 | 98.981 | 12.392 | 63.25 | 135.0 |
| 22500 | 0.3636 | 66.5 | 213.0 | 3.1511 | 25.1782 | 99.292 | 12.431 | 50.75 | 80.0 |
| 25000 | 0.4040 | 63.25 | 197.0 | 3.0803 | 25.2258 | 99.105 | 12.408 | 44.5 | 80.5 |
| 27500 | 0.4444 | 58.5 | 212.0 | 3.0299 | 25.2357 | 99.066 | 12.403 | 41.75 | 68.5 |
| 30000 | 0.4848 | 58.5 | 202.0 | 3.0169 | 25.2481 | 99.017 | 12.397 | 43.25 | 91.5 |
| 32500 | 0.5253 | 58.75 | 173.0 | 3.0014 | 25.2575 | 98.981 | 12.392 | 41.5 | 62.75 |
| 35000 | 0.5657 | 57.25 | 164.0 | 2.9385 | 25.2523 | 99.001 | 12.395 | 38.0 | 49.0 |
| 37500 | 0.6061 | 57.0 | 157.0 | 2.9163 | 25.1539 | 99.388 | 12.443 | 39.25 | 61.75 |
| 40000 | 0.6465 | 54.75 | 172.0 | 2.8984 | 25.2388 | 99.054 | 12.402 | 35.0 | 67.5 |
| 42500 | 0.6869 | 53.0 | 151.0 | 2.8789 | 25.2418 | 99.042 | 12.4 | 35.25 | 49.75 |
| 45000 | 0.7273 | 49.5 | 134.0 | 2.7753 | 25.2511 | 99.005 | 12.395 | 30.25 | 42.25 |
| 47500 | 0.7677 | 50.0 | 124.0 | 2.7506 | 25.2475 | 99.02 | 12.397 | 29.5 | 38.75 |
| 50000 | 0.8081 | 49.0 | 124.5 | 2.7361 | 25.2146 | 99.149 | 12.413 | 28.75 | 38.25 |
| 52500 | 0.8485 | 48.25 | 120.0 | 2.7262 | 25.1855 | 99.264 | 12.428 | 29.125 | 35.0 |
| 55000 | 0.8889 | 47.75 | 117.0 | 2.7099 | 25.2332 | 99.076 | 12.404 | 28.25 | 33.0 |
| 57500 | 0.9293 | 47.25 | 117.5 | 2.7045 | 25.2693 | 98.934 | 12.387 | 28.0 | 32.5 |
| 60000 | 0.9697 | 47.25 | 116.5 | 2.7013 | 25.2549 | 98.991 | 12.394 | 27.875 | 32.25 |
| 61875 | 1.0 | 47.25 | 116.5 | 2.7009 | 25.2212 | 99.123 | 12.41 | 28.0 | 32.25 |

# Resource Usage Comparison

- VRAM Use: 7.7830 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=5, 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=5, loss_fn=cos, layer_mapper=layer-2))`
- train_embeddings: `True`
- lr_scheduler: `<torch.optim.lr_scheduler.LambdaLR object at 0x7f14d416e830>`
- 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 4.44.1
- Pytorch 2.5.0.dev20240821+cu121
- Datasets 2.21.0