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: 7.7868 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 145,714,513 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, norm=batchnorm, projector=orthogonal))
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, norm=batchnorm, projector=orthogonal))
- train_embeddings:
True
- lr_scheduler:
<torch.optim.lr_scheduler.LambdaLR object at 0x7fc4addc8820>
- 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:
100
- warmup_ratio:
0.5
- warmup_steps:
0
- gradient_checkpointing:
True
Framework Versions
- Distily 0.4.1
- Transformers 4.44.1
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
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