Downloads · 30 days
15
6% of all-time downloads
distily_projector_experiment
distily/distily_projector_experiment
distily_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.
All-time downloads
267
Public
Parameters
124M
78.5 GB on disk
Likes
0
Public
Repo makeup
Click a slice to open those files.
.bin1 GB · 64%
At a glance
- Library
- Distily
- License
- mit
- Model type
- gpt2
- Access
- Public
- Created
- Aug 21, 2024
- Updated
- Aug 22, 2024
- SHA
- 60db2910
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 | 30.7740 | 30.0939 | 83.073 | 10.401 | 4060086272.0 | 71468255805440.0 |
| 2500 | 0.0404 | 1192.0 | 11840.0 | 9.8250 | 30.1508 | 82.916 | 10.381 | 772.0 | 15040.0 |
| 5000 | 0.0808 | 412.0 | 2240.0 | 8.3978 | 30.1808 | 82.834 | 10.371 | 290.0 | 438.0 |
| 7500 | 0.1212 | 245.0 | 908.0 | 7.6620 | 30.1603 | 82.891 | 10.378 | 219.0 | 198.0 |
| 10000 | 0.1616 | 182.0 | 672.0 | 7.2415 | 30.2587 | 82.621 | 10.344 | 165.0 | 204.0 |
| 12500 | 0.2020 | 132.0 | 504.0 | 6.6895 | 30.1682 | 82.869 | 10.375 | 115.0 | 155.0 |
| 15000 | 0.2424 | 113.0 | 436.0 | 6.4127 | 30.186 | 82.82 | 10.369 | 89.5 | 137.0 |
| 17500 | 0.2828 | 92.5 | 340.0 | 6.1945 | 30.108 | 83.035 | 10.396 | 71.0 | 132.0 |
| 20000 | 0.3232 | 74.0 | 278.0 | 5.9293 | 30.1455 | 82.931 | 10.383 | 63.25 | 134.0 |
| 22500 | 0.3636 | 66.0 | 215.0 | 5.6606 | 30.0869 | 83.093 | 10.403 | 50.5 | 81.5 |
| 25000 | 0.4040 | 63.25 | 189.0 | 5.5592 | 30.1385 | 82.95 | 10.385 | 44.0 | 72.5 |
| 27500 | 0.4444 | 59.0 | 202.0 | 5.4963 | 30.1334 | 82.964 | 10.387 | 40.5 | 79.0 |
| 30000 | 0.4848 | 59.75 | 198.0 | 5.4789 | 30.1924 | 82.802 | 10.367 | 42.25 | 63.75 |
| 32500 | 0.5253 | 58.75 | 177.0 | 5.4552 | 30.1133 | 83.02 | 10.394 | 40.25 | 56.5 |
| 35000 | 0.5657 | 57.5 | 167.0 | 5.3773 | 30.1179 | 83.007 | 10.393 | 36.0 | 51.0 |
| 37500 | 0.6061 | 57.5 | 161.0 | 5.3443 | 30.1249 | 82.988 | 10.39 | 37.75 | 53.25 |
| 40000 | 0.6465 | 54.5 | 159.0 | 5.3258 | 30.1211 | 82.998 | 10.391 | 34.25 | 59.0 |
| 42500 | 0.6869 | 55.25 | 150.0 | 5.2937 | 30.1886 | 82.813 | 10.368 | 35.75 | 50.75 |
| 45000 | 0.7273 | 50.5 | 132.0 | 5.1564 | 30.1176 | 83.008 | 10.393 | 30.125 | 42.75 |
| 47500 | 0.7677 | 50.75 | 123.0 | 5.1254 | 30.0774 | 83.119 | 10.406 | 29.375 | 37.5 |
| 50000 | 0.8081 | 50.0 | 123.5 | 5.1100 | 30.1068 | 83.038 | 10.396 | 28.75 | 39.0 |
| 52500 | 0.8485 | 49.0 | 120.0 | 5.0958 | 30.1022 | 83.05 | 10.398 | 29.125 | 35.0 |
| 55000 | 0.8889 | 48.75 | 117.5 | 5.0753 | 30.968 | 80.728 | 10.107 | 28.125 | 35.75 |
| 57500 | 0.9293 | 48.25 | 117.0 | 5.0696 | 30.0872 | 83.092 | 10.403 | 28.0 | 33.25 |
| 60000 | 0.9697 | 48.25 | 117.0 | 5.0655 | 30.1265 | 82.983 | 10.39 | 28.0 | 33.0 |
| 61875 | 1.0 | 48.25 | 117.0 | 5.0651 | 30.1098 | 83.03 | 10.395 | 28.0 | 33.25 |
Resource Usage Comparison
- VRAM Use: 7.7843 GB
`# Distillation (Teacher -> Student) Architecture Difference:
- Architecture:
GPT2LMHeadModel->GPT2LMHeadModel - Total Parameters: 124,439,808 -> 124,439,808
- Data Type (dtype): 124439808 -> 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=10.0, loss_fn=cos, 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=10.0, loss_fn=cos, layer_mapper=layer-2)) - train_embeddings:
True - lr_scheduler:
<torch.optim.lr_scheduler.LambdaLR object at 0x7f010c102dd0> - 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 4.44.0
- Pytorch 2.3.0
- Datasets 2.21.0
- Library
- Distily
- Type
- gpt2
- License
- mit
- Created
- Aug 21, 2024
- Updated
- Aug 22, 2024