---
base_model: gpt2
datasets:
- wikimedia/wikipedia
library_name: Distily
license: mit
tags:
- generated_from_trainer
model-index:
- name: distily_modelcard_try
  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** |  | 43.75 | 61.75 |  |  |  |  | 11.8125 | 19.125 |
| 0 | 0 | 949187772416.0 | 76416058130432.0 | 21.75 | 0.1221 | 16.381 | 8.191 | 3556769792.0 | 13950053777408.0 |
| 20 | 1.0 | 13248.0 | 64000.0 | 5.6562 | 0.0646 | 30.969 | 15.485 | 7712.0 | 181248.0 |


# Resource Usage Comparison

- VRAM Use: 7.9388 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**: 0.16 GB -> 0.24 GB

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

```diff
--- teacher model modules
+++ student model modules
@@ -7,15 +7,15 @@
       (0-11): 12 x GPT2Block(
         (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
         (attn): GPT2FlashAttention2(
-          (c_attn): Linear8bitLt(in_features=768, out_features=2304, bias=True)
-          (c_proj): Linear8bitLt(in_features=768, out_features=768, bias=True)
+          (c_attn): Conv1D()
+          (c_proj): Conv1D()
           (attn_dropout): Dropout(p=0.1, inplace=False)
           (resid_dropout): Dropout(p=0.1, inplace=False)
         )
         (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
         (mlp): GPT2MLP(
-          (c_fc): Linear8bitLt(in_features=768, out_features=3072, bias=True)
-          (c_proj): Linear8bitLt(in_features=3072, out_features=768, bias=True)
+          (c_fc): Conv1D()
+          (c_proj): Conv1D()
           (act): NewGELUActivation()
           (dropout): Dropout(p=0.1, inplace=False)
         )

```

</details>
<br/>

# Train Dataset
Trained on 149,632 tokens from the [wikimedia/wikipedia](https://huggingface.co/datasets/wikimedia/wikipedia) dataset.

- Num Samples: `158`
- Subset: `20231101.en`
- Split: `train`


# Training Objective

```
DistillationObjective(logits_loss_component=LossComponent(label=logits, weight=1, loss_fn=kl))
```

# Hyperparameters
The following hyperparameters were used during training:

<details>
<summary>Expand</summary>

- learning_rate: `0.0001`
- train_batch_size: `8`
- 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`
- distillation_objective: `DistillationObjective(logits_loss_component=LossComponent(label=logits, weight=1, loss_fn=kl))`
- train_embeddings: `True`
- lr_scheduler: `<torch.optim.lr_scheduler.LambdaLR object at 0x7f80845a7190>`
- 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: `False`
- student_model_compile: `False`
- dropout: `None`
- teacher_model_name_or_path: `gpt2`
- teacher_load_in_8bit: `True`
- 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: `160`
- dataset_test_size: `0.01`
- gradient_accumulation_steps: `1`
- weight_decay: `0.0`
- max_grad_norm: `1.0`
- warmup_ratio: `0.2`
- 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