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--- |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: tinystories_1layer_attn_mlp_C10k_k100 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# tinystories_1layer_attn_mlp_C10k_k100 |
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This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.8957 |
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- Accuracy: 0.5429 |
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- Multicode K: 1 |
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- Dead Code Fraction/layer0: 0.0 |
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- Mse/layer0: 611.1572 |
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- Input Norm/layer0: 31.9975 |
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- Output Norm/layer0: 15.0872 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0005 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 6 |
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- total_train_batch_size: 48 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.05 |
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- training_steps: 10000 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Multicode K | Dead Code Fraction/layer0 | Mse/layer0 | Input Norm/layer0 | Output Norm/layer0 | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:|:-----------:|:-------------------------:|:----------:|:-----------------:|:------------------:| |
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| 2.5072 | 0.05 | 500 | 2.4764 | 0.4579 | 1 | 0.0 | 841.1602 | 31.9977 | 4.9114 | |
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| 2.2285 | 0.1 | 1000 | 2.2265 | 0.4926 | 1 | 0.0 | 792.3023 | 31.9980 | 7.5524 | |
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| 2.1472 | 0.16 | 1500 | 2.1584 | 0.5025 | 1 | 0.0 | 761.8683 | 31.9980 | 8.9239 | |
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| 2.1144 | 0.21 | 2000 | 2.1128 | 0.5090 | 1 | 0.0 | 737.1843 | 31.9979 | 9.8992 | |
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| 2.0847 | 0.26 | 2500 | 2.0791 | 0.5142 | 1 | 0.0 | 716.9390 | 31.9979 | 10.6577 | |
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| 2.0439 | 0.31 | 3000 | 2.0482 | 0.5185 | 1 | 0.0 | 698.7266 | 31.9979 | 11.3599 | |
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| 2.0263 | 0.37 | 3500 | 2.0253 | 0.5224 | 1 | 0.0 | 682.2680 | 31.9979 | 12.0105 | |
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| 1.9906 | 0.42 | 4000 | 2.0066 | 0.5253 | 1 | 0.0 | 669.1965 | 31.9979 | 12.5568 | |
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| 1.9852 | 0.47 | 4500 | 1.9898 | 0.5279 | 1 | 0.0 | 657.5872 | 31.9979 | 13.0526 | |
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| 1.9687 | 0.52 | 5000 | 1.9757 | 0.5300 | 1 | 0.0 | 648.2462 | 31.9979 | 13.4496 | |
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| 1.9672 | 0.57 | 5500 | 1.9620 | 0.5321 | 1 | 0.0 | 640.0822 | 31.9978 | 13.8078 | |
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| 1.9441 | 0.63 | 6000 | 1.9513 | 0.5339 | 1 | 0.0 | 633.8831 | 31.9978 | 14.1018 | |
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| 1.9408 | 0.68 | 6500 | 1.9397 | 0.5358 | 1 | 0.0 | 628.0929 | 31.9977 | 14.3550 | |
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| 1.9256 | 0.73 | 7000 | 1.9302 | 0.5374 | 1 | 0.0 | 623.2726 | 31.9977 | 14.5534 | |
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| 1.9204 | 0.78 | 7500 | 1.9225 | 0.5381 | 1 | 0.0 | 619.4573 | 31.9977 | 14.7258 | |
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| 1.907 | 0.84 | 8000 | 1.9150 | 0.5393 | 1 | 0.0 | 616.4379 | 31.9976 | 14.8625 | |
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| 1.8931 | 0.89 | 8500 | 1.9076 | 0.5408 | 1 | 0.0 | 613.7874 | 31.9976 | 14.9685 | |
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| 1.9021 | 0.94 | 9000 | 1.9021 | 0.5417 | 1 | 0.0 | 612.0126 | 31.9975 | 15.0379 | |
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| 1.8967 | 0.99 | 9500 | 1.8970 | 0.5426 | 1 | 0.0 | 610.6121 | 31.9975 | 15.0932 | |
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| 1.8942 | 1.04 | 10000 | 1.8957 | 0.5429 | 1 | 0.0 | 611.1572 | 31.9975 | 15.0872 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.1 |
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