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distily_miles_projector_experiment
distily/distily_miles_projector_experiment
distily_miles_projector_experiment is a machine learning model from distily. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for Distily. The card lists the license as mit.
Distilled with Distily library using teacher model gpt2 on dataset wikimedia/wikipedia.
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At a glance
- Library
- Distily
- License
- mit
- Model type
- gpt2
- Access
- Public
- Created
- Aug 23, 2024
- Updated
- Aug 23, 2024
- SHA
- 4ad37fbf
Base models
Datasets
Model card
From the Hugging Face model README
Summary
Distilled with Distily library using teacher model gpt2 on dataset wikimedia/wikipedia.
<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. # Model description More information needed # Intended uses & limitations More information needed -->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 | 36.25 | 77.0 | 11.75 | 21.375 | |||||
| 0 | 0 | 10788957847552.0 | 93458488360960.0 | 23.9652 | 41.1128 | 60.808 | 7.613 | 3539992576.0 | 57174604644352.0 |
| 2500 | 0.0404 | 888.0 | 5536.0 | 3.2958 | 40.0823 | 62.372 | 7.809 | 492.0 | 4576.0 |
| 5000 | 0.0808 | 380.0 | 1448.0 | 2.4808 | 41.6839 | 59.975 | 7.509 | 255.0 | 400.0 |
| 7500 | 0.1212 | 250.0 | 748.0 | 2.1083 | 44.1725 | 56.596 | 7.086 | 197.0 | 233.0 |
| 10000 | 0.1616 | 189.0 | 616.0 | 1.8890 | 43.9453 | 56.889 | 7.122 | 156.0 | 216.0 |
| 12500 | 0.2020 | 140.0 | 488.0 | 1.6027 | 42.1657 | 59.29 | 7.423 | 119.0 | 178.0 |
| 15000 | 0.2424 | 113.5 | 434.0 | 1.4410 | 42.3062 | 59.093 | 7.398 | 94.0 | 183.0 |
| 17500 | 0.2828 | 92.5 | 340.0 | 1.3090 | 42.413 | 58.944 | 7.38 | 76.5 | 165.0 |
| 20000 | 0.3232 | 79.5 | 308.0 | 1.1661 | 40.1951 | 62.197 | 7.787 | 73.0 | 151.0 |
| 22500 | 0.3636 | 68.0 | 229.0 | 0.9997 | 41.1581 | 60.741 | 7.605 | 56.75 | 122.5 |
| 25000 | 0.4040 | 63.25 | 201.0 | 0.9359 | 40.9228 | 61.091 | 7.649 | 50.75 | 99.5 |
| 27500 | 0.4444 | 59.25 | 218.0 | 0.8936 | 40.1195 | 62.314 | 7.802 | 46.25 | 116.5 |
| 30000 | 0.4848 | 59.25 | 204.0 | 0.8841 | 42.297 | 59.106 | 7.4 | 49.75 | 87.0 |
| 32500 | 0.5253 | 57.5 | 184.0 | 0.8730 | 40.8597 | 61.185 | 7.66 | 44.25 | 101.5 |
| 35000 | 0.5657 | 56.0 | 177.0 | 0.8049 | 44.9443 | 55.624 | 6.964 | 39.75 | 62.25 |
| 37500 | 0.6061 | 55.0 | 163.0 | 0.7798 | 44.8966 | 55.684 | 6.972 | 43.5 | 93.5 |
| 40000 | 0.6465 | 52.0 | 166.0 | 0.7611 | 40.5252 | 61.69 | 7.724 | 37.25 | 73.5 |
| 42500 | 0.6869 | 51.5 | 159.0 | 0.7336 | 41.7519 | 59.878 | 7.497 | 38.5 | 70.0 |
| 45000 | 0.7273 | 46.25 | 143.0 | 0.6241 | 40.2456 | 62.119 | 7.777 | 32.25 | 54.5 |
| 47500 | 0.7677 | 45.75 | 136.0 | 0.5998 | 42.1189 | 59.356 | 7.431 | 31.5 | 43.75 |
| 50000 | 0.8081 | 45.25 | 135.0 | 0.5841 | 40.1272 | 62.302 | 7.8 | 31.0 | 43.75 |
| 52500 | 0.8485 | 44.25 | 128.0 | 0.5705 | 41.9206 | 59.637 | 7.466 | 31.25 | 43.25 |
| 55000 | 0.8889 | 43.5 | 125.5 | 0.5532 | 40.1106 | 62.328 | 7.803 | 29.875 | 38.25 |
| 57500 | 0.9293 | 43.5 | 125.5 | 0.5470 | 40.2997 | 62.035 | 7.767 | 29.875 | 38.0 |
| 60000 | 0.9697 | 43.5 | 126.0 | 0.5432 | 39.9729 | 62.542 | 7.83 | 29.625 | 37.5 |
| 61875 | 1.0 | 43.5 | 126.0 | 0.5426 | 41.9287 | 59.625 | 7.465 | 29.625 | 37.5 |
Resource Usage Comparison
- VRAM Use: 7.7831 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
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=raw_mse, layer_mapper=layer-2))
Hyperparameters
The following hyperparameters were used during training:
<details> <summary>Expand</summary>- 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:
linear - 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=raw_mse, layer_mapper=layer-2)) - train_embeddings:
True - lr_scheduler:
<torch.optim.lr_scheduler.LambdaLR object at 0x7fd7d547a6e0> - 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 0.2.0
- Transformers 4.44.2
- Pytorch 2.3.0
- Datasets 2.21.0
- Library
- Distily
- Type
- gpt2
- License
- mit
- Created
- Aug 23, 2024
- Updated
- Aug 23, 2024