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tinystories_1layer_attn_mlp_C10k_k16
empty-michael/tinystories_1layer_attn_mlp_C10k_k16
tinystories_1layer_attn_mlp_C10k_k16 is a machine learning model from empty-michael. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Library
- transformers
- Model type
- codebook
- Access
- Public
- Created
- Feb 11, 2024
- Updated
- Feb 11, 2024
- SHA
- dd10feb1
Base models
Datasets
Model card
From the Hugging Face model README
<!-- 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 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
- Library
- transformers
- Type
- codebook
- Created
- Feb 11, 2024
- Updated
- Feb 11, 2024