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
Benchmark Metrics Comparison
| Metric | | | :--- |
Resource Usage Comparison
- VRAM Use: 7.7870 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
Module Diff Details
Train Dataset
Trained on 145,744,973 tokens from the 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=25.0, loss_fn=cos, layer_mapper=layer-2, projector=linear))
Hyperparameters
The following hyperparameters were used during training:
Expand
- 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:
cosine_with_min_lr - 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=25.0, loss_fn=cos, layer_mapper=layer-2, projector=linear)) - train_embeddings:
True - lr_scheduler:
<torch.optim.lr_scheduler.LambdaLR object at 0x7fa4f426c3d0> - 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 - 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:
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
Framework Versions
- Distily 0.3.0
- Transformers 4.44.1
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
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Model tree for distily/distily_test_attn_linear
Base model
openai-community/gpt2