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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_C25k_k16_mse_weighted
results:
- task:
name: Causal Language Modeling
type: text-generation
dataset:
name: roneneldan/TinyStories
type: roneneldan/TinyStories
metrics:
- name: Accuracy
type: accuracy
value: 0.5193506309245984
---
<!-- 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_C25k_k16_mse_weighted
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: 2.0353
- Accuracy: 0.5194
- Multicode K: 1
- Dead Code Fraction/layer0: 0.1640
- Mse/layer0: 501.8128
- Input Norm/layer0: 31.9989
- Output Norm/layer0: 22.8009
## 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: 0.005
- 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_with_warmup
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:-----------:|:-------------------------:|:----------:|:-----------------:|:------------------:|
| 2.8364 | 0.05 | 500 | 2.7649 | 0.4227 | 1 | 0.3619 | 634.8932 | 31.9979 | 18.0819 |
| 2.3611 | 0.1 | 1000 | 2.3705 | 0.4712 | 1 | 0.3607 | 568.7264 | 31.9979 | 20.6630 |
| 2.2395 | 0.15 | 1500 | 2.2531 | 0.4866 | 1 | 0.3266 | 550.3311 | 31.9979 | 21.3297 |
| 2.1999 | 0.2 | 2000 | 2.1908 | 0.4955 | 1 | 0.3048 | 539.0150 | 31.9980 | 21.7663 |
| 2.1688 | 0.25 | 2500 | 2.1551 | 0.5006 | 1 | 0.2949 | 530.4651 | 31.9980 | 22.0228 |
| 2.1108 | 0.3 | 3000 | 2.1269 | 0.5051 | 1 | 0.2809 | 524.9530 | 31.9981 | 22.2071 |
| 2.1045 | 0.35 | 3500 | 2.1130 | 0.5079 | 1 | 0.2735 | 523.0844 | 31.9982 | 22.3519 |
| 2.0944 | 0.4 | 4000 | 2.0996 | 0.5089 | 1 | 0.2655 | 519.8852 | 31.9983 | 22.3930 |
| 2.1314 | 0.45 | 4500 | 2.0860 | 0.5115 | 1 | 0.2567 | 517.0385 | 31.9983 | 22.4720 |
| 2.0685 | 1.02 | 5000 | 2.0770 | 0.5131 | 1 | 0.2497 | 514.3712 | 31.9984 | 22.4943 |
| 2.0496 | 1.07 | 5500 | 2.0730 | 0.5137 | 1 | 0.2381 | 513.7823 | 31.9985 | 22.5625 |
| 2.1002 | 1.12 | 6000 | 2.0667 | 0.5144 | 1 | 0.2305 | 510.7876 | 31.9986 | 22.5882 |
| 2.0723 | 1.17 | 6500 | 2.0632 | 0.5148 | 1 | 0.2206 | 510.5624 | 31.9986 | 22.6133 |
| 2.023 | 1.22 | 7000 | 2.0574 | 0.5157 | 1 | 0.2110 | 509.9878 | 31.9987 | 22.6544 |
| 2.0791 | 1.27 | 7500 | 2.0513 | 0.5168 | 1 | 0.2033 | 507.1514 | 31.9987 | 22.7018 |
| 2.0252 | 1.32 | 8000 | 2.0463 | 0.5173 | 1 | 0.1953 | 505.2723 | 31.9988 | 22.7108 |
| 2.0432 | 1.37 | 8500 | 2.0423 | 0.5183 | 1 | 0.1875 | 502.9395 | 31.9988 | 22.7562 |
| 2.0549 | 1.42 | 9000 | 2.0394 | 0.5188 | 1 | 0.1797 | 502.9016 | 31.9988 | 22.7722 |
| 2.0087 | 1.47 | 9500 | 2.0365 | 0.5193 | 1 | 0.1704 | 504.0088 | 31.9989 | 22.7990 |
| 2.0569 | 2.04 | 10000 | 2.0353 | 0.5194 | 1 | 0.1640 | 501.8128 | 31.9989 | 22.8009 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
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