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---
base_model: roneneldan/TinyStories-33M
library_name: Distily
tags:
- generated_from_trainer
model-index:
- name: distily_bench_obj_cross_v2
  results: []
---

# distily_bench_obj_cross_v2

This student model is distilled from the teacher model [roneneldan/TinyStories-33M](https://huggingface.co/roneneldan/TinyStories-33M) using the dataset (unspecified).

The [Distily](https://github.com/lapp0/distily) library was used for this distillation.

It achieves the following results on the evaluation set:
- eval_enwikippl: 5868.0605
- eval_frwikippl: 32990.6758
- eval_zhwikippl: 54785.7930
- eval_tinystoriesppl: 2293.1941
- eval_loss: 4.9180
- eval_runtime: 13.0935
- eval_samples_per_second: 76.374
- eval_steps_per_second: 9.547

<!-- 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

## Training and evaluation data

More information needed
-->

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- distillation_objective: DistillationObjective(logits_loss_component=LossComponent(label=logits, weight=1, loss_fn=kl, layer_mapper=None, projector=None), hs_loss_component=LossComponent(label=hs, weight=2.0, loss_fn=mse, layer_mapper=None, projector=None), attn_loss_component=LossComponent(label=attn, weight=0, loss_fn=None, layer_mapper=None, projector=None))
- train_embeddings: True
- learning_rate: 0.0004
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- num_epochs: 1.0

### Resource Usage
Peak GPU Memory: 8.1729 GB

### Eval-Phase Metrics
| step | epoch | enwikippl | frwikippl | loss | runtime | samples_per_second | steps_per_second | tinystoriesppl | zhwikippl |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| **teacher eval** |  | 169.9865 | 47377.9414 |  |  |  |  | 3.9789 | 4998.1294 |
| 0 | 0 | 34961.5352 | 67685.8906 | 6.4082 | 13.0484 | 76.638 | 9.58 | 22307.2852 | 64899.9219 |
| 1000 | 0.0808 | 5926.0762 | 32934.9414 | 4.9183 | 13.0602 | 76.568 | 9.571 | 2332.1941 | 55049.4961 |
| 2000 | 0.1616 | 5852.6240 | 32990.6758 | 4.9180 | 13.0475 | 76.643 | 9.58 | 2287.5139 | 54785.7930 |
| 3000 | 0.2424 | 5843.5669 | 32990.6758 | 4.9177 | 13.045 | 76.658 | 9.582 | 2281.4705 | 54785.7930 |
| 4000 | 0.3232 | 5878.9780 | 32990.6758 | 4.9180 | 13.0627 | 76.554 | 9.569 | 2303.0730 | 54815.0078 |
| 5000 | 0.4040 | 5868.0605 | 32990.6758 | 4.9180 | 13.0226 | 76.789 | 9.599 | 2295.0898 | 54815.0078 |
| 6000 | 0.4848 | 5867.1484 | 32990.6758 | 4.9180 | 13.0139 | 76.841 | 9.605 | 2291.6780 | 54785.7930 |
| 7000 | 0.5657 | 5869.8799 | 32990.6758 | 4.9177 | 13.0183 | 76.815 | 9.602 | 2297.7485 | 54815.0078 |
| 8000 | 0.6465 | 5868.0605 | 32990.6758 | 4.9180 | 13.084 | 76.429 | 9.554 | 2294.3315 | 54815.0078 |
| 9000 | 0.7273 | 5868.0605 | 32990.6758 | 4.9180 | 13.0935 | 76.374 | 9.547 | 2293.1941 | 54785.7930 |
| 10000 | 0.8081 | 5845.3784 | 32990.6758 | 4.9177 | 13.0045 | 76.896 | 9.612 | 2282.6021 | 54785.7930 |
| 11000 | 0.8889 | 5848.9976 | 32990.6758 | 4.9177 | 13.0015 | 76.914 | 9.614 | 2284.8682 | 54785.7930 |
| 12000 | 0.9697 | 5868.0605 | 32990.6758 | 4.9183 | 13.0386 | 76.696 | 9.587 | 2296.9883 | 54815.0078 |
| 12375 | 1.0 | 5868.0605 | 32990.6758 | 4.9183 | 13.0038 | 76.9 | 9.613 | 2296.9883 | 54815.0078 |

### Framework versions
- Distily 0.2.0
- Transformers 4.44.0
- Pytorch 2.3.0
- Datasets 2.21.0