moe_hom_100m
This model is a fine-tuned version of on the arrow dataset. It achieves the following results on the evaluation set:
- Loss: 4.7001
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: 0.0001
- train_batch_size: 8
- eval_batch_size: 32
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-06 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 66788
- training_steps: 667880
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 8.474 | 0.1497 | 10000 | 8.4434 |
| 7.3705 | 0.2995 | 20000 | 7.3390 |
| 6.4743 | 0.4492 | 30000 | 6.4405 |
| 5.8985 | 0.5989 | 40000 | 5.8659 |
| 5.5978 | 0.7486 | 50000 | 5.5786 |
| 5.4382 | 0.8984 | 60000 | 5.4064 |
| 5.2974 | 1.0481 | 70000 | 5.2621 |
| 5.165 | 1.1978 | 80000 | 5.1229 |
| 5.0265 | 1.3475 | 90000 | 5.0155 |
| 4.9568 | 1.4973 | 100000 | 4.9374 |
| 4.872 | 1.6470 | 110000 | 4.8702 |
| 4.814 | 1.7967 | 120000 | 4.8172 |
| 4.759 | 1.9465 | 130000 | 4.7736 |
| 4.5891 | 2.0962 | 140000 | 4.7425 |
| 4.5934 | 2.2459 | 150000 | 4.7119 |
| 4.5623 | 2.3956 | 160000 | 4.6824 |
| 4.5499 | 2.5454 | 170000 | 4.6549 |
| 4.5505 | 2.6951 | 180000 | 4.6280 |
| 4.5343 | 2.8448 | 190000 | 4.6039 |
| 4.5138 | 2.9945 | 200000 | 4.5791 |
| 4.3003 | 3.1443 | 210000 | 4.5858 |
| 4.324 | 3.2940 | 220000 | 4.5730 |
| 4.3332 | 3.4437 | 230000 | 4.5554 |
| 4.3436 | 3.5934 | 240000 | 4.5378 |
| 4.3339 | 3.7432 | 250000 | 4.5219 |
| 4.3071 | 3.8929 | 260000 | 4.5056 |
| 4.0475 | 4.0426 | 270000 | 4.5268 |
| 4.0936 | 4.1923 | 280000 | 4.5278 |
| 4.1204 | 4.3421 | 290000 | 4.5152 |
| 4.1392 | 4.4918 | 300000 | 4.5024 |
| 4.1562 | 4.6415 | 310000 | 4.4896 |
| 4.1714 | 4.7912 | 320000 | 4.4752 |
| 4.1695 | 4.9410 | 330000 | 4.4641 |
| 3.8707 | 5.0907 | 340000 | 4.5161 |
| 3.917 | 5.2404 | 350000 | 4.5157 |
| 3.9432 | 5.3901 | 360000 | 4.5082 |
| 3.9817 | 5.5399 | 370000 | 4.4955 |
| 3.9956 | 5.6896 | 380000 | 4.4823 |
| 3.9952 | 5.8393 | 390000 | 4.4715 |
| 4.015 | 5.9891 | 400000 | 4.4593 |
| 3.7174 | 6.1388 | 410000 | 4.5393 |
| 3.7456 | 6.2885 | 420000 | 4.5367 |
| 3.7796 | 6.4382 | 430000 | 4.5265 |
| 3.8109 | 6.5879 | 440000 | 4.5160 |
| 3.8051 | 6.7377 | 450000 | 4.5069 |
| 3.8109 | 6.8874 | 460000 | 4.4948 |
| 3.5003 | 7.0371 | 470000 | 4.5652 |
| 3.5381 | 7.1868 | 480000 | 4.5890 |
| 3.5896 | 7.3366 | 490000 | 4.5854 |
| 3.6105 | 7.4863 | 500000 | 4.5757 |
| 3.6093 | 7.6360 | 510000 | 4.5676 |
| 3.6397 | 7.7858 | 520000 | 4.5572 |
| 3.6258 | 7.9355 | 530000 | 4.5470 |
| 3.353 | 8.0852 | 540000 | 4.6341 |
| 3.3623 | 8.2349 | 550000 | 4.6469 |
| 3.4051 | 8.3846 | 560000 | 4.6475 |
| 3.4427 | 8.5344 | 570000 | 4.6403 |
| 3.4094 | 8.6841 | 580000 | 4.6347 |
| 3.4441 | 8.8338 | 590000 | 4.6281 |
| 3.437 | 8.9836 | 600000 | 4.6232 |
| 3.1943 | 9.1333 | 610000 | 4.6993 |
| 3.2034 | 9.2830 | 620000 | 4.7052 |
| 3.2252 | 9.4327 | 630000 | 4.7053 |
| 3.2246 | 9.5825 | 640000 | 4.7055 |
| 3.2353 | 9.7322 | 650000 | 4.7018 |
| 3.2326 | 9.8819 | 660000 | 4.7006 |
Framework versions
- Transformers 4.51.0
- Pytorch 2.7.0+cu126
- Datasets 3.6.0
- Tokenizers 0.21.1
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