lapp0/distily_modelcard_try

Summary

Distilled with Distily library using teacher model gpt2 on dataset wikimedia/wikipedia.

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

Module Diff Details

--- 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)
         )

Train Dataset

Trained on 149,632 tokens from the 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:

Expand

• 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

Framework Versions

• Distily 0.2.0
• Transformers 4.44.0
• Pytorch 2.3.0
• Datasets 2.21.0