---
base_model: distilbert/distilgpt2
datasets:
- wikimedia/wikipedia
library_name: Distily
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distily_norm_distilgpt2_sweep_extended
  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**: 81,912,576
- **Data Type (dtype)**: torch.bfloat16
- **Model Size**: 0.16 GB


# Benchmark Metrics Comparison

| Metric |  |
| :--- |

# Resource Usage Comparison

- VRAM Use: 15.6991 GB

# Distillation (Teacher -> Student) Architecture Difference:

- **Architecture**: `GPT2LMHeadModel` -> `GPT2LMHeadModel`
- **Total Parameters**: 124,439,808 -> 81,912,576
- **Data Type (dtype)**: torch.bfloat16 -> torch.bfloat16
- **Model Size**: 0.24 GB -> 0.16 GB

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

```diff
--- teacher model modules
+++ student model modules
@@ -4,7 +4,7 @@
     (wpe): Embedding(1024, 768)
     (drop): Dropout(p=0.1, inplace=False)
     (h): ModuleList(
-      (0-11): 12 x GPT2Block(
+      (0-5): 6 x GPT2Block(
         (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
         (attn): GPT2FlashAttention2(
           (c_attn): Conv1D()

```

</details>
<br/>

# Train Dataset
Trained on 521,413,804 tokens from the [wikimedia/wikipedia](https://huggingface.co./datasets/wikimedia/wikipedia) dataset.

- Num Samples: `990,000`
- 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=raw_mse, layer_mapper=layer-2, norm=instance_teacher_only, projector=mlp))
```

# Hyperparameters
The following hyperparameters were used during training:

<details>
<summary>Expand</summary>

- learning_rate: `0.0002`
- 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: `polynomial`
- 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=raw_mse, layer_mapper=layer-2, norm=instance_teacher_only, projector=mlp))`
- train_embeddings: `True`
- lr_scheduler: `<torch.optim.lr_scheduler.LambdaLR object at 0x7f99b4ed13c0>`
- student_model_name_or_path: `None`
- student_config_name_or_path: `distilbert/distilgpt2`
- student_model_config: `None`
- reinitialize_weights: `None`
- copy_teacher_modules: `[('lm_head', False)]`
- student_model_as_bitnet: `False`
- dropout: `None`
- teacher_model_name_or_path: `gpt2`
- teacher_load_in_8bit: `False`
- teacher_load_in_4bit: `False`
- dataset_uri: `wikimedia/wikipedia`
- dataset_subset: `20231101.en`
- dataset_split: `train`
- dataset_column_name: `text`
- dataset_sample_size: `1000000`
- dataset_test_size: `0.01`
- gradient_accumulation_steps: `1`
- weight_decay: `0.0`
- max_grad_norm: `1.0`
- warmup_ratio: `0`
- warmup_steps: `0`
- gradient_checkpointing: `True`

</details>
<br/>


# Framework Versions
- Distily 0.4.1
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0