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distily_multi_experiment
distily/distily_multi_experiment
distily_multi_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 22, 2024
- Updated
- Aug 23, 2024
- SHA
- f3960385
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 | 43.25 | 61.25 | 11.6875 | 19.125 | |||||
| 0 | 0 | 2473901162496.0 | 170424302305280.0 | 22.7948 | 25.4866 | 98.091 | 12.281 | 4060086272.0 | 71468255805440.0 |
| 2500 | 0.0404 | 800.0 | 6240.0 | 2.9661 | 25.4278 | 98.318 | 12.309 | 470.0 | 5024.0 |
| 5000 | 0.0808 | 326.0 | 1480.0 | 2.1697 | 25.4996 | 98.041 | 12.275 | 247.0 | 278.0 |
| 7500 | 0.1212 | 224.0 | 804.0 | 1.8396 | 25.5448 | 97.867 | 12.253 | 185.0 | 190.0 |
| 10000 | 0.1616 | 171.0 | 608.0 | 1.6412 | 25.4672 | 98.165 | 12.29 | 145.0 | 166.0 |
| 12500 | 0.2020 | 127.0 | 482.0 | 1.3752 | 25.4897 | 98.079 | 12.279 | 111.0 | 141.0 |
| 15000 | 0.2424 | 104.5 | 436.0 | 1.2398 | 25.4711 | 98.15 | 12.288 | 93.5 | 99.5 |
| 17500 | 0.2828 | 90.5 | 346.0 | 1.1286 | 25.4723 | 98.146 | 12.288 | 74.0 | 147.0 |
| 20000 | 0.3232 | 81.5 | 312.0 | 1.0325 | 25.4627 | 98.183 | 12.293 | 69.5 | 111.0 |
| 22500 | 0.3636 | 73.0 | 236.0 | 0.9000 | 25.4791 | 98.12 | 12.285 | 59.75 | 100.0 |
| 25000 | 0.4040 | 67.0 | 209.0 | 0.8527 | 25.4728 | 98.144 | 12.288 | 53.0 | 183.0 |
| 27500 | 0.4444 | 64.0 | 228.0 | 0.8201 | 25.4859 | 98.094 | 12.281 | 48.0 | 105.5 |
| 30000 | 0.4848 | 64.5 | 225.0 | 0.8103 | 25.489 | 98.082 | 12.28 | 51.75 | 77.5 |
| 32500 | 0.5253 | 64.0 | 194.0 | 0.8016 | 25.4563 | 98.208 | 12.296 | 46.5 | 117.5 |
| 35000 | 0.5657 | 63.5 | 188.0 | 0.7395 | 25.4507 | 98.229 | 12.298 | 44.0 | 73.0 |
| 37500 | 0.6061 | 60.25 | 172.0 | 0.7164 | 25.411 | 98.382 | 12.317 | 45.5 | 68.5 |
| 40000 | 0.6465 | 59.5 | 180.0 | 0.7014 | 25.4454 | 98.25 | 12.301 | 41.25 | 94.5 |
| 42500 | 0.6869 | 58.25 | 168.0 | 0.6708 | 25.4719 | 98.147 | 12.288 | 42.0 | 65.5 |
| 45000 | 0.7273 | 53.75 | 158.0 | 0.5781 | 25.3987 | 98.43 | 12.323 | 35.25 | 67.5 |
| 47500 | 0.7677 | 54.0 | 136.0 | 0.5538 | 25.4465 | 98.245 | 12.3 | 34.0 | 41.75 |
| 50000 | 0.8081 | 52.25 | 136.0 | 0.5368 | 25.4472 | 98.243 | 12.3 | 33.0 | 41.0 |
| 52500 | 0.8485 | 50.75 | 131.0 | 0.5244 | 25.4589 | 98.198 | 12.294 | 33.25 | 38.25 |
| 55000 | 0.8889 | 50.0 | 128.0 | 0.5073 | 25.4565 | 98.207 | 12.295 | 32.0 | 35.5 |
| 57500 | 0.9293 | 49.75 | 127.0 | 0.5019 | 25.4729 | 98.143 | 12.288 | 31.75 | 33.5 |
| 60000 | 0.9697 | 49.75 | 126.5 | 0.4983 | 25.4379 | 98.279 | 12.304 | 31.5 | 33.75 |
| 61875 | 1.0 | 49.75 | 126.5 | 0.4979 | 25.4846 | 98.098 | 12.282 | 31.5 | 33.75 |
Resource Usage Comparison
- VRAM Use: 7.7851 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=kl, 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=kl, layer_mapper=layer-2)) - train_embeddings:
True - lr_scheduler:
<torch.optim.lr_scheduler.LambdaLR object at 0x7f0428288790> - 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 4.44.1
- Pytorch 2.5.0.dev20240821+cu121
- Datasets 2.21.0
- Library
- Distily
- Type
- gpt2
- License
- mit
- Created
- Aug 22, 2024
- Updated
- Aug 23, 2024