---
base_model: roneneldan/TinyStories-1Layer-21M
tags:
- generated_from_trainer
datasets:
- roneneldan/TinyStories
metrics:
- accuracy
model-index:
- name: tinystories_1layer_attn_mlp_C10k_k16
  results:
  - task:
      name: Causal Language Modeling
      type: text-generation
    dataset:
      name: roneneldan/TinyStories
      type: roneneldan/TinyStories
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.5091345939349958
---

<!-- 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. -->

# tinystories_1layer_attn_mlp_C10k_k16

This model is a fine-tuned version of [roneneldan/TinyStories-1Layer-21M](https://huggingface.co/roneneldan/TinyStories-1Layer-21M) on the roneneldan/TinyStories dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1329
- Accuracy: 0.5091
- Multicode K: 1
- Dead Code Fraction/layer0: 0.1880
- Mse/layer0: 604.5097
- Input Norm/layer0: 31.9987
- Output Norm/layer0: 19.3897

## 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:
- learning_rate: 0.0005
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 6
- total_train_batch_size: 96
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.05
- training_steps: 10000

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | Multicode K | Dead Code Fraction/layer0 | Mse/layer0 | Input Norm/layer0 | Output Norm/layer0 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:-----------:|:-------------------------:|:----------:|:-----------------:|:------------------:|
| 3.0494        | 0.05  | 500   | 2.9927          | 0.4177   | 1           | 0.0                       | 805.1676   | 31.9986           | 10.3600            |
| 2.6986        | 0.1   | 1000  | 2.7080          | 0.4472   | 1           | 0.0084                    | 739.3244   | 31.9985           | 12.7165            |
| 2.5145        | 0.15  | 1500  | 2.5252          | 0.4637   | 1           | 0.0546                    | 697.1179   | 31.9984           | 14.4889            |
| 2.4197        | 0.2   | 2000  | 2.4093          | 0.4758   | 1           | 0.0988                    | 670.0254   | 31.9983           | 15.7288            |
| 2.3541        | 0.25  | 2500  | 2.3404          | 0.4837   | 1           | 0.1337                    | 651.1297   | 31.9983           | 16.6602            |
| 2.2742        | 0.3   | 3000  | 2.2907          | 0.4903   | 1           | 0.1499                    | 642.6360   | 31.9983           | 17.3243            |
| 2.2488        | 0.35  | 3500  | 2.2565          | 0.4945   | 1           | 0.1575                    | 640.3158   | 31.9983           | 17.7566            |
| 2.2287        | 0.4   | 4000  | 2.2333          | 0.4967   | 1           | 0.1613                    | 638.8423   | 31.9983           | 18.0223            |
| 2.2576        | 0.45  | 4500  | 2.2155          | 0.4992   | 1           | 0.1676                    | 639.7464   | 31.9983           | 18.1919            |
| 2.1901        | 1.02  | 5000  | 2.2026          | 0.5014   | 1           | 0.1696                    | 638.1766   | 31.9984           | 18.3119            |
| 2.1686        | 1.07  | 5500  | 2.1935          | 0.5026   | 1           | 0.1716                    | 638.6084   | 31.9984           | 18.4013            |
| 2.2158        | 1.12  | 6000  | 2.1833          | 0.5037   | 1           | 0.1779                    | 632.9326   | 31.9985           | 18.5149            |
| 2.1843        | 1.17  | 6500  | 2.1760          | 0.5039   | 1           | 0.1797                    | 631.2925   | 31.9985           | 18.5986            |
| 2.1339        | 1.22  | 7000  | 2.1696          | 0.5048   | 1           | 0.1819                    | 627.9791   | 31.9985           | 18.7053            |
| 2.187         | 1.27  | 7500  | 2.1584          | 0.5063   | 1           | 0.1867                    | 622.1227   | 31.9986           | 18.8338            |
| 2.1302        | 1.32  | 8000  | 2.1508          | 0.5071   | 1           | 0.1875                    | 617.7162   | 31.9986           | 18.9493            |
| 2.1471        | 1.37  | 8500  | 2.1444          | 0.5082   | 1           | 0.1885                    | 613.7248   | 31.9986           | 19.0666            |
| 2.1556        | 1.42  | 9000  | 2.1392          | 0.5087   | 1           | 0.1880                    | 610.3757   | 31.9987           | 19.1817            |
| 2.1067        | 1.47  | 9500  | 2.1351          | 0.5091   | 1           | 0.1875                    | 608.6866   | 31.9987           | 19.2836            |
| 2.1536        | 2.04  | 10000 | 2.1329          | 0.5091   | 1           | 0.1880                    | 604.5097   | 31.9987           | 19.3897            |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1