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---
base_model: roneneldan/TinyStories-1Layer-21M
tags:
- generated_from_trainer
datasets:
- roneneldan/TinyStories
metrics:
- accuracy
model-index:
- name: tinystories_1layer_attn_mlp_C10k_k100
results:
- task:
name: Causal Language Modeling
type: text-generation
dataset:
name: roneneldan/TinyStories
type: roneneldan/TinyStories
metrics:
- name: Accuracy
type: accuracy
value: 0.5429091526514649
---
<!-- 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_k100
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: 1.8957
- Accuracy: 0.5429
- Multicode K: 1
- Dead Code Fraction/layer0: 0.0
- Mse/layer0: 611.1572
- Input Norm/layer0: 31.9975
- Output Norm/layer0: 15.0872
## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 6
- total_train_batch_size: 48
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:-----------:|:-------------------------:|:----------:|:-----------------:|:------------------:|
| 2.5072 | 0.05 | 500 | 2.4764 | 0.4579 | 1 | 0.0 | 841.1602 | 31.9977 | 4.9114 |
| 2.2285 | 0.1 | 1000 | 2.2265 | 0.4926 | 1 | 0.0 | 792.3023 | 31.9980 | 7.5524 |
| 2.1472 | 0.16 | 1500 | 2.1584 | 0.5025 | 1 | 0.0 | 761.8683 | 31.9980 | 8.9239 |
| 2.1144 | 0.21 | 2000 | 2.1128 | 0.5090 | 1 | 0.0 | 737.1843 | 31.9979 | 9.8992 |
| 2.0847 | 0.26 | 2500 | 2.0791 | 0.5142 | 1 | 0.0 | 716.9390 | 31.9979 | 10.6577 |
| 2.0439 | 0.31 | 3000 | 2.0482 | 0.5185 | 1 | 0.0 | 698.7266 | 31.9979 | 11.3599 |
| 2.0263 | 0.37 | 3500 | 2.0253 | 0.5224 | 1 | 0.0 | 682.2680 | 31.9979 | 12.0105 |
| 1.9906 | 0.42 | 4000 | 2.0066 | 0.5253 | 1 | 0.0 | 669.1965 | 31.9979 | 12.5568 |
| 1.9852 | 0.47 | 4500 | 1.9898 | 0.5279 | 1 | 0.0 | 657.5872 | 31.9979 | 13.0526 |
| 1.9687 | 0.52 | 5000 | 1.9757 | 0.5300 | 1 | 0.0 | 648.2462 | 31.9979 | 13.4496 |
| 1.9672 | 0.57 | 5500 | 1.9620 | 0.5321 | 1 | 0.0 | 640.0822 | 31.9978 | 13.8078 |
| 1.9441 | 0.63 | 6000 | 1.9513 | 0.5339 | 1 | 0.0 | 633.8831 | 31.9978 | 14.1018 |
| 1.9408 | 0.68 | 6500 | 1.9397 | 0.5358 | 1 | 0.0 | 628.0929 | 31.9977 | 14.3550 |
| 1.9256 | 0.73 | 7000 | 1.9302 | 0.5374 | 1 | 0.0 | 623.2726 | 31.9977 | 14.5534 |
| 1.9204 | 0.78 | 7500 | 1.9225 | 0.5381 | 1 | 0.0 | 619.4573 | 31.9977 | 14.7258 |
| 1.907 | 0.84 | 8000 | 1.9150 | 0.5393 | 1 | 0.0 | 616.4379 | 31.9976 | 14.8625 |
| 1.8931 | 0.89 | 8500 | 1.9076 | 0.5408 | 1 | 0.0 | 613.7874 | 31.9976 | 14.9685 |
| 1.9021 | 0.94 | 9000 | 1.9021 | 0.5417 | 1 | 0.0 | 612.0126 | 31.9975 | 15.0379 |
| 1.8967 | 0.99 | 9500 | 1.8970 | 0.5426 | 1 | 0.0 | 610.6121 | 31.9975 | 15.0932 |
| 1.8942 | 1.04 | 10000 | 1.8957 | 0.5429 | 1 | 0.0 | 611.1572 | 31.9975 | 15.0872 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
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