metadata
license: apache-2.0
base_model: mistralai/Mistral-7B-v0.1
tags:
- generated_from_trainer
model-index:
- name: sparse_mistral_7b_refined_web_50p_2024-04-13
results: []
sparse_mistral_7b_refined_web_50p_2024-04-13
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: 2.2023
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: 4
- seed: 0
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1350
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 2.3391 | 0.01 | 25 | 2.4196 |
| 2.2711 | 0.02 | 50 | 2.3577 |
| 2.3054 | 0.02 | 75 | 2.3158 |
| 2.2795 | 0.03 | 100 | 2.2966 |
| 2.3175 | 0.04 | 125 | 2.2846 |
| 2.2388 | 0.05 | 150 | 2.2766 |
| 2.1679 | 0.06 | 175 | 2.2705 |
| 2.2996 | 0.06 | 200 | 2.2678 |
| 2.2788 | 0.07 | 225 | 2.2647 |
| 2.2448 | 0.08 | 250 | 2.2637 |
| 2.1837 | 0.09 | 275 | 2.2624 |
| 2.2089 | 0.1 | 300 | 2.2621 |
| 2.2686 | 0.1 | 325 | 2.2601 |
| 2.2254 | 0.11 | 350 | 2.2593 |
| 2.162 | 0.12 | 375 | 2.2590 |
| 2.2687 | 0.13 | 400 | 2.2563 |
| 2.2595 | 0.14 | 425 | 2.2571 |
| 2.186 | 0.14 | 450 | 2.2564 |
| 2.2689 | 0.15 | 475 | 2.2580 |
| 2.2472 | 0.16 | 500 | 2.2554 |
| 2.2005 | 0.17 | 525 | 2.2553 |
| 2.1983 | 0.18 | 550 | 2.2552 |
| 2.2388 | 0.18 | 575 | 2.2547 |
| 2.1443 | 0.19 | 600 | 2.2555 |
| 2.2198 | 0.2 | 625 | 2.2534 |
| 2.3008 | 0.21 | 650 | 2.2536 |
| 2.179 | 0.22 | 675 | 2.2521 |
| 2.2069 | 0.22 | 700 | 2.2531 |
| 2.1819 | 0.23 | 725 | 2.2526 |
| 2.1218 | 0.24 | 750 | 2.2536 |
| 2.1845 | 0.25 | 775 | 2.2515 |
| 2.2167 | 0.26 | 800 | 2.2510 |
| 2.2252 | 0.26 | 825 | 2.2520 |
| 2.1664 | 0.27 | 850 | 2.2519 |
| 2.1853 | 0.28 | 875 | 2.2530 |
| 2.1499 | 0.29 | 900 | 2.2513 |
| 2.2763 | 0.3 | 925 | 2.2517 |
| 2.2528 | 0.3 | 950 | 2.2518 |
| 2.2505 | 0.31 | 975 | 2.2500 |
| 2.1683 | 0.32 | 1000 | 2.2502 |
| 2.2177 | 0.33 | 1025 | 2.2501 |
| 2.238 | 0.34 | 1050 | 2.2516 |
| 2.193 | 0.34 | 1075 | 2.2507 |
| 2.2025 | 0.35 | 1100 | 2.2502 |
| 2.0944 | 0.36 | 1125 | 2.2512 |
| 2.2272 | 0.37 | 1150 | 2.2508 |
| 2.2264 | 0.38 | 1175 | 2.2500 |
| 2.1837 | 0.38 | 1200 | 2.2507 |
| 2.1444 | 0.39 | 1225 | 2.2489 |
| 2.2464 | 0.4 | 1250 | 2.2499 |
| 2.1398 | 0.41 | 1275 | 2.2512 |
| 2.1929 | 0.42 | 1300 | 2.2502 |
| 2.2384 | 0.42 | 1325 | 2.2498 |
| 2.223 | 0.43 | 1350 | 2.2481 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0