metadata
license: apache-2.0
base_model: mistralai/Mistral-7B-v0.1
tags:
- generated_from_trainer
model-index:
- name: Mistral_Sparse_refined_web_relu_2024-03-11
results: []
Mistral_Sparse_refined_web_relu_2024-03-11
This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 2.3931
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: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 0
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- total_eval_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 2100
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 8.7876 | 0.0 | 25 | 8.7120 |
| 8.1837 | 0.01 | 50 | 8.1741 |
| 7.7529 | 0.01 | 75 | 7.8037 |
| 7.5336 | 0.02 | 100 | 7.6036 |
| 7.1718 | 0.02 | 125 | 7.1058 |
| 4.7663 | 0.02 | 150 | 4.7163 |
| 3.7548 | 0.03 | 175 | 3.8001 |
| 3.2625 | 0.03 | 200 | 3.4192 |
| 3.0886 | 0.04 | 225 | 3.2355 |
| 2.9928 | 0.04 | 250 | 3.1153 |
| 2.8555 | 0.04 | 275 | 3.0392 |
| 2.8289 | 0.05 | 300 | 2.9870 |
| 2.7933 | 0.05 | 325 | 2.9505 |
| 2.6073 | 0.06 | 350 | 2.9160 |
| 2.7488 | 0.06 | 375 | 2.8941 |
| 2.7795 | 0.06 | 400 | 2.8722 |
| 2.7317 | 0.07 | 425 | 2.8537 |
| 2.6982 | 0.07 | 450 | 2.8407 |
| 2.5823 | 0.08 | 475 | 2.8258 |
| 2.6419 | 0.08 | 500 | 2.8164 |
| 2.7195 | 0.08 | 525 | 2.8082 |
| 2.6239 | 0.09 | 550 | 2.7980 |
| 2.7273 | 0.09 | 575 | 2.7869 |
| 2.5436 | 0.1 | 600 | 2.7809 |
| 2.6159 | 0.1 | 625 | 2.7761 |
| 2.6563 | 0.1 | 650 | 2.7666 |
| 2.6728 | 0.11 | 675 | 2.7573 |
| 2.6047 | 0.11 | 700 | 2.7509 |
| 2.6237 | 0.12 | 725 | 2.7493 |
| 2.5305 | 0.12 | 750 | 2.7458 |
| 2.5329 | 0.12 | 775 | 2.7392 |
| 2.6538 | 0.13 | 800 | 2.7359 |
| 2.6076 | 0.13 | 825 | 2.7310 |
| 2.5928 | 0.14 | 850 | 2.7279 |
| 2.455 | 0.14 | 875 | 2.7246 |
| 2.5579 | 0.14 | 900 | 2.7252 |
| 2.4948 | 0.15 | 925 | 2.7194 |
| 2.6219 | 0.15 | 950 | 2.7181 |
| 2.5387 | 0.16 | 975 | 2.7139 |
| 2.5734 | 0.16 | 1000 | 2.7134 |
| 2.6012 | 0.16 | 1025 | 2.7115 |
| 2.63 | 0.17 | 1050 | 2.7076 |
| 2.6361 | 0.17 | 1075 | 2.7045 |
| 2.5534 | 0.18 | 1100 | 2.7046 |
| 2.5756 | 0.18 | 1125 | 2.7031 |
| 2.5632 | 0.18 | 1150 | 2.6989 |
| 2.5971 | 0.19 | 1175 | 2.6960 |
| 2.4719 | 0.19 | 1200 | 2.6963 |
| 2.5377 | 0.2 | 1225 | 2.6944 |
| 2.552 | 0.2 | 1250 | 2.6907 |
| 2.5748 | 0.2 | 1275 | 2.6894 |
| 2.5799 | 0.21 | 1300 | 2.6877 |
| 2.5569 | 0.21 | 1325 | 2.6834 |
| 2.4413 | 0.22 | 1350 | 2.6822 |
| 2.5232 | 0.22 | 1375 | 2.6822 |
| 2.5346 | 0.22 | 1400 | 2.6806 |
| 2.479 | 0.23 | 1425 | 2.6791 |
| 2.4585 | 0.23 | 1450 | 2.6803 |
| 2.4104 | 0.24 | 1475 | 2.6776 |
| 2.4961 | 0.24 | 1500 | 2.6792 |
| 2.4219 | 0.24 | 1525 | 2.6770 |
| 2.4658 | 0.25 | 1550 | 2.6736 |
| 2.5875 | 0.25 | 1575 | 2.6755 |
| 2.5376 | 0.26 | 1600 | 2.6705 |
| 2.5466 | 0.26 | 1625 | 2.6726 |
| 2.5889 | 0.26 | 1650 | 2.6704 |
| 2.4973 | 0.27 | 1675 | 2.6667 |
| 2.5409 | 0.27 | 1700 | 2.6681 |
| 2.5386 | 0.28 | 1725 | 2.6658 |
| 2.5234 | 0.28 | 1750 | 2.6666 |
| 2.5066 | 0.28 | 1775 | 2.6619 |
| 2.4283 | 0.29 | 1800 | 2.6629 |
| 2.5253 | 0.29 | 1825 | 2.6623 |
| 2.5179 | 0.3 | 1850 | 2.6599 |
| 2.5023 | 0.3 | 1875 | 2.6608 |
| 2.5253 | 0.3 | 1900 | 2.6602 |
| 2.5788 | 0.31 | 1925 | 2.6602 |
| 2.5307 | 0.31 | 1950 | 2.6596 |
| 2.5108 | 0.32 | 1975 | 2.6593 |
| 2.462 | 0.32 | 2000 | 2.6597 |
| 2.5028 | 0.32 | 2025 | 2.6577 |
| 2.48 | 0.33 | 2050 | 2.6538 |
| 2.4742 | 0.33 | 2075 | 2.6534 |
| 2.554 | 0.34 | 2100 | 2.6544 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0