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 | attn_layer_mapper=all, attn_loss_fn=cos, attn_projector=orthogonal, attn_weight=5 | attn_layer_mapper=layer-2, attn_loss_fn=raw_mse, attn_projector=orthogonal, attn_weight=25.0 | teacher |
|---|---|---|---|
| ai2_arc (acc) | 0.313 | 0.305 | 0.354 |
| ai2_arc (acc_norm) | 0.31 | 0.302 | 0.339 |
| arc_challenge (acc) | 0.181 | 0.173 | 0.188 |
| arc_challenge (acc_norm) | 0.224 | 0.223 | 0.222 |
| arc_easy (acc) | 0.378 | 0.37 | 0.436 |
| arc_easy (acc_norm) | 0.353 | 0.34 | 0.396 |
| boolq (acc) | 0.49 | 0.387 | 0.51 |
| cola (mcc) | -0.041 | 0.044 | 0.01 |
| glue (acc) | 0.396 | 0.412 | 0.403 |
| glue (f1) | 0.516 | 0.451 | 0.529 |
| glue (mcc) | -0.041 | 0.044 | 0.01 |
| hellaswag (acc) | 0.32 | 0.315 | 0.343 |
| hellaswag (acc_norm) | 0.348 | 0.344 | 0.393 |
| mnli (acc) | 0.336 | 0.338 | 0.338 |
| mnli_mismatch (acc) | 0.343 | 0.351 | 0.346 |
| mrpc (acc) | 0.444 | 0.353 | 0.515 |
| mrpc (f1) | 0.478 | 0.143 | 0.631 |
| qnli (acc) | 0.488 | 0.497 | 0.491 |
| qqp (acc) | 0.356 | 0.406 | 0.367 |
| qqp (f1) | 0.522 | 0.501 | 0.512 |
| rte (acc) | 0.56 | 0.549 | 0.516 |
| sst2 (acc) | 0.498 | 0.545 | 0.511 |
| wikitext (bits_per_byte) | 1.118 | 1.127 | 0.98 |
| wikitext (byte_perplexity) | 2.17 | 2.184 | 1.973 |
| wikitext (word_perplexity) | 63.05 | 65.25 | 37.82 |
| wnli (acc) | 0.408 | 0.451 | 0.451 |
Resource Usage Comparison
- VRAM Use: 8.2855 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 145,724,804 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=5, loss_fn=cos, layer_mapper=all))
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=5, loss_fn=cos, layer_mapper=all)) - train_embeddings:
True - lr_scheduler:
<torch.optim.lr_scheduler.LambdaLR object at 0x7f05c40e2050> - 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 - student_model_compile:
False - dropout:
None - teacher_model_name_or_path:
gpt2 - teacher_load_in_8bit:
False - 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:
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 4.44.0
- Pytorch 2.3.0
- Datasets 2.21.0
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Model tree for distily/distily_multi_attn_experiment_ortho
Base model
openai-community/gpt2