--- 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()

Train Dataset

Trained on 226,096,614 tokens from the wikimedia/wikipedia dataset.

Num Samples: 396,000
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=batchnorm, projector=orthogonal))

Hyperparameters

The following hyperparameters were used during training:

Expand

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=batchnorm, projector=orthogonal))
train_embeddings: True
lr_scheduler: <torch.optim.lr_scheduler.LambdaLR object at 0x7f678de0f430>
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: 400000
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

Framework Versions

Distily 0.4.1
Transformers 4.44.2
Pytorch 2.3.0
Datasets 2.21.0

distily/distily_norm_distilgpt2_sweep

Summary

Model Architecture:

Benchmark Metrics Comparison

Resource Usage Comparison

Distillation (Teacher -> Student) Architecture Difference:

Train Dataset

Training Objective

Hyperparameters

Framework Versions

distily/
distily_norm_distilgpt2_sweep