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---
base_model: gpt2
datasets:
- wikimedia/wikipedia
library_name: Distily
license: mit
tags:
- bitnet
- 1.58b
- generated_from_trainer
model-index:
- name: distily_test_attn_miles
results: []
---
# Summary
Distilled with [Distily](https://github.com/lapp0/distily) library
using teacher model [gpt2](https://huggingface.co/gpt2)
on dataset [wikimedia/wikipedia](https://huggingface.co/datasets/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
# Benchmark Metrics Comparison
| Metric | attn_layer_mapper=all, attn_loss_fn=logsum, attn_projector=miles | attn_layer_mapper=all, attn_loss_fn=raw_mse, attn_projector=miles | teacher |
| :--- | :--- | :--- | :--- |
| ai2_arc (acc) | 0.228 | 0.256 | 0.304 |
| ai2_arc (acc_norm) | 0.258 | 0.267 | 0.309 |
| arc_challenge (acc) | 0.186 | 0.177 | 0.184 |
| arc_challenge (acc_norm) | 0.227 | 0.202 | 0.214 |
| arc_easy (acc) | 0.27 | 0.335 | 0.424 |
| arc_easy (acc_norm) | 0.288 | 0.332 | 0.405 |
| boolq (acc) | 0.375 | 0.377 | 0.541 |
| cola (mcc) | 0.0 | 0.0 | 0.009 |
| glue (acc) | 0.454 | 0.444 | 0.41 |
| glue (f1) | 0.0 | 0.279 | 0.526 |
| glue (mcc) | 0.0 | 0.0 | 0.009 |
| hellaswag (acc) | 0.282 | 0.302 | 0.337 |
| hellaswag (acc_norm) | 0.275 | 0.308 | 0.384 |
| mnli (acc) | 0.326 | 0.331 | 0.323 |
| mnli_mismatch (acc) | 0.295 | 0.367 | 0.344 |
| mrpc (acc) | 0.316 | 0.336 | 0.515 |
| mrpc (f1) | 0.0 | 0.075 | 0.631 |
| qnli (acc) | 0.527 | 0.519 | 0.472 |
| qqp (acc) | 0.673 | 0.515 | 0.34 |
| qqp (f1) | 0.0 | 0.363 | 0.483 |
| rte (acc) | 0.52 | 0.57 | 0.516 |
| sst2 (acc) | 0.492 | 0.498 | 0.511 |
| wikitext (bits_per_byte) | 1.888 | 1.273 | 0.98 |
| wikitext (byte_perplexity) | 3.701 | 2.416 | 1.973 |
| wikitext (word_perplexity) | 1094.0 | 111.9 | 37.82 |
| wnli (acc) | 0.437 | 0.521 | 0.451 |
# Resource Usage Comparison
- VRAM Use: 7.7871 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
<details>
<summary>Module Diff Details</summary>
```diff
```
</details>
<br/>
# Train Dataset
Trained on 145,744,973 tokens from the [wikimedia/wikipedia](https://huggingface.co/datasets/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, projector=miles))
```
# 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: `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, projector=miles))`
- train_embeddings: `True`
- lr_scheduler: `<torch.optim.lr_scheduler.LambdaLR object at 0x7fae8845cd00>`
- 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: `1.0`
- warmup_ratio: `0.5`
- warmup_steps: `0`
- gradient_checkpointing: `True`
</details>
<br/>
# Framework Versions
- Distily 0.3.0
- Transformers 4.44.0
- Pytorch 2.3.0
- Datasets 2.21.0
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