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reverse_add_replicate_eval17_small_nl

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Safetensors
nanogpt
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  1. README.md +216 -0
  2. config.json +19 -0
  3. generation_config.json +4 -0
  4. model.safetensors +3 -0
  5. training_args.bin +3 -0
README.md ADDED
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+ ---
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+ library_name: transformers
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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: reverse_add_replicate_eval17_small_nl
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+ results: []
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+ ---
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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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+
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+ # reverse_add_replicate_eval17_small_nl
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+
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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: 2.3825
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+ - Accuracy: 0.0
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 0.001
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+ - train_batch_size: 128
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+ - eval_batch_size: 128
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+ - seed: 7658372
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+ - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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+ - lr_scheduler_type: cosine
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+ - lr_scheduler_warmup_ratio: 0.1
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+ - num_epochs: 1
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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+ |:-------------:|:------:|:-----:|:---------------:|:--------:|
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+ | No log | 0 | 0 | 2.6391 | 0.0 |
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+ | 2.6391 | 0.0064 | 100 | 2.6390 | 0.0 |
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+ | 2.6385 | 0.0128 | 200 | 2.6384 | 0.0 |
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+ | 2.6353 | 0.0192 | 300 | 2.6351 | 0.0 |
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+ | 2.6261 | 0.0256 | 400 | 2.6256 | 0.0 |
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+ | 2.6073 | 0.032 | 500 | 2.6062 | 0.0 |
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+ | 2.576 | 0.0384 | 600 | 2.5748 | 0.0 |
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+ | 2.5355 | 0.0448 | 700 | 2.5339 | 0.0 |
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+ | 2.4934 | 0.0512 | 800 | 2.4914 | 0.0 |
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+ | 2.457 | 0.0576 | 900 | 2.4564 | 0.0 |
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+ | 2.4351 | 0.064 | 1000 | 2.4327 | 0.0 |
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+ | 2.4207 | 0.0704 | 1100 | 2.4176 | 0.0 |
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+ | 2.4105 | 0.0768 | 1200 | 2.4090 | 0.0 |
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+ | 2.4052 | 0.0832 | 1300 | 2.4026 | 0.0 |
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+ | 2.4006 | 0.0896 | 1400 | 2.3981 | 0.0 |
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+ | 2.3985 | 0.096 | 1500 | 2.3970 | 0.0 |
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+ | 2.3983 | 0.1024 | 1600 | 2.3959 | 0.0 |
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+ | 2.3977 | 0.1088 | 1700 | 2.3960 | 0.0 |
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+ | 2.3984 | 0.1152 | 1800 | 2.3955 | 0.0 |
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+ | 2.3984 | 0.1216 | 1900 | 2.3959 | 0.0 |
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+ | 2.3984 | 0.128 | 2000 | 2.3956 | 0.0 |
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+ | 2.3976 | 0.1344 | 2100 | 2.3951 | 0.0 |
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+ | 2.3979 | 0.1408 | 2200 | 2.3953 | 0.0 |
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+ | 2.3984 | 0.1472 | 2300 | 2.3947 | 0.0 |
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+ | 2.3975 | 0.1536 | 2400 | 2.3951 | 0.0 |
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+ | 2.3959 | 0.16 | 2500 | 2.3948 | 0.0 |
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+ | 2.3973 | 0.1664 | 2600 | 2.3945 | 0.0 |
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+ | 2.3973 | 0.1728 | 2700 | 2.3952 | 0.0 |
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+ | 2.3956 | 0.1792 | 2800 | 2.3949 | 0.0 |
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+ | 2.3973 | 0.1856 | 2900 | 2.3953 | 0.0 |
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+ | 2.3989 | 0.192 | 3000 | 2.3948 | 0.0 |
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+ | 2.3964 | 0.1984 | 3100 | 2.3944 | 0.0 |
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+ | 2.3974 | 0.2048 | 3200 | 2.3942 | 0.0 |
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+ | 2.3964 | 0.2112 | 3300 | 2.3944 | 0.0 |
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+ | 2.3971 | 0.2176 | 3400 | 2.3944 | 0.0 |
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+ | 2.3967 | 0.224 | 3500 | 2.3939 | 0.0 |
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+ | 2.3973 | 0.2304 | 3600 | 2.3944 | 0.0 |
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+ | 2.396 | 0.2368 | 3700 | 2.3947 | 0.0 |
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+ | 2.3979 | 0.2432 | 3800 | 2.3950 | 0.0 |
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+ | 2.3965 | 0.2496 | 3900 | 2.3943 | 0.0 |
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+ | 2.3985 | 0.256 | 4000 | 2.3947 | 0.0 |
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+ | 2.3959 | 0.2624 | 4100 | 2.3943 | 0.0 |
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+ | 2.3968 | 0.2688 | 4200 | 2.3948 | 0.0 |
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+ | 2.3982 | 0.2752 | 4300 | 2.3946 | 0.0 |
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+ | 2.3961 | 0.2816 | 4400 | 2.3939 | 0.0 |
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+ | 2.3987 | 0.288 | 4500 | 2.3945 | 0.0 |
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+ | 2.3979 | 0.2944 | 4600 | 2.3944 | 0.0 |
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+ | 2.3962 | 0.3008 | 4700 | 2.3944 | 0.0 |
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+ | 2.3971 | 0.3072 | 4800 | 2.3939 | 0.0 |
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+ | 2.3963 | 0.3136 | 4900 | 2.3946 | 0.0 |
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+ | 2.3963 | 0.32 | 5000 | 2.3943 | 0.0 |
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+ | 2.3955 | 0.3264 | 5100 | 2.3937 | 0.0 |
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+ | 2.3979 | 0.3328 | 5200 | 2.3940 | 0.0 |
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+ | 2.3972 | 0.3392 | 5300 | 2.3936 | 0.0 |
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+ | 2.3971 | 0.3456 | 5400 | 2.3945 | 0.0 |
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+ | 2.3963 | 0.352 | 5500 | 2.3940 | 0.0 |
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+ | 2.3965 | 0.3584 | 5600 | 2.3939 | 0.0 |
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+ | 2.3976 | 0.3648 | 5700 | 2.3936 | 0.0 |
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+ | 2.3964 | 0.3712 | 5800 | 2.3934 | 0.0 |
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+ | 2.3971 | 0.3776 | 5900 | 2.3928 | 0.0 |
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+ | 2.3963 | 0.384 | 6000 | 2.3930 | 0.0 |
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+ | 2.396 | 0.3904 | 6100 | 2.3922 | 0.0 |
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+ | 2.3951 | 0.3968 | 6200 | 2.3918 | 0.0 |
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+ | 2.3962 | 0.4032 | 6300 | 2.3914 | 0.0 |
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+ | 2.3963 | 0.4096 | 6400 | 2.3910 | 0.0 |
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+ | 2.3955 | 0.416 | 6500 | 2.3907 | 0.0 |
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+ | 2.3949 | 0.4224 | 6600 | 2.3898 | 0.0 |
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+ | 2.3947 | 0.4288 | 6700 | 2.3891 | 0.0 |
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+ | 2.395 | 0.4352 | 6800 | 2.3882 | 0.0 |
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+ | 2.3928 | 0.4416 | 6900 | 2.3875 | 0.0 |
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+ | 2.3927 | 0.448 | 7000 | 2.3864 | 0.0 |
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+ | 2.392 | 0.4544 | 7100 | 2.3859 | 0.0 |
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+ | 2.3908 | 0.4608 | 7200 | 2.3852 | 0.0 |
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+ | 2.3911 | 0.4672 | 7300 | 2.3849 | 0.0 |
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+ | 2.3897 | 0.4736 | 7400 | 2.3850 | 0.0 |
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+ | 2.3879 | 0.48 | 7500 | 2.3842 | 0.0 |
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+ | 2.3898 | 0.4864 | 7600 | 2.3840 | 0.0 |
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+ | 2.3877 | 0.4928 | 7700 | 2.3837 | 0.0 |
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+ | 2.3881 | 0.4992 | 7800 | 2.3834 | 0.0 |
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+ | 2.3871 | 0.5056 | 7900 | 2.3835 | 0.0 |
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+ | 2.3869 | 0.512 | 8000 | 2.3832 | 0.0 |
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+ | 2.3883 | 0.5184 | 8100 | 2.3832 | 0.0 |
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+ | 2.3863 | 0.5248 | 8200 | 2.3831 | 0.0 |
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+ | 2.3867 | 0.5312 | 8300 | 2.3832 | 0.0 |
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+ | 2.388 | 0.5376 | 8400 | 2.3834 | 0.0 |
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+ | 2.3867 | 0.544 | 8500 | 2.3833 | 0.0 |
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+ | 2.3855 | 0.5504 | 8600 | 2.3831 | 0.0 |
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+ | 2.3854 | 0.5568 | 8700 | 2.3830 | 0.0 |
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+ | 2.3859 | 0.5632 | 8800 | 2.3825 | 0.0 |
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+ | 2.3857 | 0.5696 | 8900 | 2.3832 | 0.0 |
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+ | 2.386 | 0.576 | 9000 | 2.3829 | 0.0 |
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+ | 2.3849 | 0.5824 | 9100 | 2.3826 | 0.0 |
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+ | 2.3853 | 0.6336 | 9900 | 2.3825 | 0.0 |
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+ | 2.3854 | 0.64 | 10000 | 2.3827 | 0.0 |
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+ | 2.3837 | 0.6464 | 10100 | 2.3825 | 0.0 |
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+ | 2.3846 | 0.6528 | 10200 | 2.3824 | 0.0 |
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+ | 2.3852 | 0.6592 | 10300 | 2.3824 | 0.0 |
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+ | 2.3861 | 0.6656 | 10400 | 2.3826 | 0.0 |
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+ | 2.3851 | 0.672 | 10500 | 2.3824 | 0.0 |
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+ | 2.3846 | 0.6784 | 10600 | 2.3829 | 0.0 |
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+ | 2.3854 | 0.6912 | 10800 | 2.3827 | 0.0 |
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+ | 2.3854 | 0.6976 | 10900 | 2.3827 | 0.0 |
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+ | 2.3844 | 0.704 | 11000 | 2.3824 | 0.0 |
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+ | 2.3827 | 0.736 | 11500 | 2.3827 | 0.0 |
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+ | 2.3839 | 0.7488 | 11700 | 2.3827 | 0.0 |
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+ | 2.3846 | 0.7552 | 11800 | 2.3826 | 0.0 |
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+ | 2.3856 | 0.7616 | 11900 | 2.3825 | 0.0 |
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+ | 2.3835 | 0.7744 | 12100 | 2.3826 | 0.0 |
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+ | 2.3847 | 0.7808 | 12200 | 2.3826 | 0.0 |
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+ | 2.3844 | 0.7872 | 12300 | 2.3825 | 0.0 |
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+ | 2.3841 | 0.7936 | 12400 | 2.3825 | 0.0 |
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+ | 2.3854 | 0.8 | 12500 | 2.3823 | 0.0 |
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+ | 2.3844 | 0.8064 | 12600 | 2.3826 | 0.0 |
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+ | 2.3833 | 0.8128 | 12700 | 2.3825 | 0.0 |
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+ | 2.3853 | 0.8192 | 12800 | 2.3824 | 0.0 |
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+ | 2.384 | 0.8256 | 12900 | 2.3823 | 0.0 |
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+ | 2.3834 | 0.832 | 13000 | 2.3825 | 0.0 |
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+ | 2.3832 | 0.8384 | 13100 | 2.3827 | 0.0 |
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+ | 2.383 | 0.8448 | 13200 | 2.3826 | 0.0 |
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+ | 2.3842 | 0.8512 | 13300 | 2.3825 | 0.0 |
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+ | 2.3839 | 0.8576 | 13400 | 2.3827 | 0.0 |
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+ | 2.3831 | 0.864 | 13500 | 2.3826 | 0.0 |
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+ | 2.384 | 0.8704 | 13600 | 2.3824 | 0.0 |
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+ | 2.3836 | 0.8768 | 13700 | 2.3825 | 0.0 |
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+ | 2.3837 | 0.8832 | 13800 | 2.3824 | 0.0 |
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+ | 2.3846 | 0.8896 | 13900 | 2.3827 | 0.0 |
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+ | 2.3843 | 0.896 | 14000 | 2.3825 | 0.0 |
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+ | 2.385 | 0.9024 | 14100 | 2.3825 | 0.0 |
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+ | 2.384 | 0.9088 | 14200 | 2.3825 | 0.0 |
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+ | 2.3838 | 0.9152 | 14300 | 2.3825 | 0.0 |
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+ | 2.3848 | 0.928 | 14500 | 2.3825 | 0.0 |
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+ | 2.383 | 0.9472 | 14800 | 2.3826 | 0.0 |
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+ | 2.3835 | 0.9856 | 15400 | 2.3825 | 0.0 |
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+ | 2.3835 | 0.992 | 15500 | 2.3825 | 0.0 |
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+ | 2.3837 | 0.9984 | 15600 | 2.3825 | 0.0 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.46.0
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+ - Pytorch 2.5.1
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+ - Datasets 3.1.0
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+ - Tokenizers 0.20.1
config.json ADDED
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+ {
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+ "architectures": [
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+ "NanoGPT"
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+ ],
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+ "bias": true,
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+ "block_size": 256,
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+ "dropout": 0.0,
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+ "mlp_dim": 1,
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+ "model_type": "nanogpt",
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+ "n_embd": 1,
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+ "n_head": 1,
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+ "n_layer": 1,
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+ "nonlinearity": "RELU",
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.46.0",
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+ "use_NoPE": true,
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+ "use_layernorm": false,
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+ "vocab_size": 14
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+ }
generation_config.json ADDED
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+ {
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+ "_from_model_config": true,
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+ "transformers_version": "4.46.0"
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+ }
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