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--- |
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license: apache-2.0 |
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base_model: mistralai/Mistral-7B-v0.1 |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: sparse_mistral_7b_refined_web_50p_2024-04-13 |
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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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# sparse_mistral_7b_refined_web_50p_2024-04-13 |
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This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.1985 |
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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: 1e-05 |
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- train_batch_size: 1 |
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- eval_batch_size: 4 |
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- seed: 0 |
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- distributed_type: multi-GPU |
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- num_devices: 4 |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 32 |
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- total_eval_batch_size: 16 |
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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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- training_steps: 2350 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 2.3391 | 0.01 | 25 | 2.4196 | |
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| 2.2711 | 0.02 | 50 | 2.3577 | |
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| 2.3054 | 0.02 | 75 | 2.3158 | |
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| 2.2795 | 0.03 | 100 | 2.2966 | |
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| 2.3175 | 0.04 | 125 | 2.2846 | |
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| 2.2388 | 0.05 | 150 | 2.2766 | |
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| 2.1679 | 0.06 | 175 | 2.2705 | |
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| 2.2996 | 0.06 | 200 | 2.2678 | |
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| 2.2788 | 0.07 | 225 | 2.2647 | |
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| 2.2448 | 0.08 | 250 | 2.2637 | |
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| 2.1837 | 0.09 | 275 | 2.2624 | |
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| 2.2089 | 0.1 | 300 | 2.2621 | |
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| 2.2686 | 0.1 | 325 | 2.2601 | |
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| 2.2254 | 0.11 | 350 | 2.2593 | |
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| 2.162 | 0.12 | 375 | 2.2590 | |
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| 2.2687 | 0.13 | 400 | 2.2563 | |
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| 2.2595 | 0.14 | 425 | 2.2571 | |
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| 2.186 | 0.14 | 450 | 2.2564 | |
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| 2.2689 | 0.15 | 475 | 2.2580 | |
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| 2.2472 | 0.16 | 500 | 2.2554 | |
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| 2.2005 | 0.17 | 525 | 2.2553 | |
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| 2.1983 | 0.18 | 550 | 2.2552 | |
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| 2.2388 | 0.18 | 575 | 2.2547 | |
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| 2.1443 | 0.19 | 600 | 2.2555 | |
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| 2.2198 | 0.2 | 625 | 2.2534 | |
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| 2.3008 | 0.21 | 650 | 2.2536 | |
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| 2.179 | 0.22 | 675 | 2.2521 | |
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| 2.2069 | 0.22 | 700 | 2.2531 | |
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| 2.1819 | 0.23 | 725 | 2.2526 | |
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| 2.1218 | 0.24 | 750 | 2.2536 | |
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| 2.1845 | 0.25 | 775 | 2.2515 | |
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| 2.2167 | 0.26 | 800 | 2.2510 | |
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| 2.2252 | 0.26 | 825 | 2.2520 | |
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| 2.1664 | 0.27 | 850 | 2.2519 | |
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| 2.1853 | 0.28 | 875 | 2.2530 | |
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| 2.1499 | 0.29 | 900 | 2.2513 | |
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| 2.2763 | 0.3 | 925 | 2.2517 | |
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| 2.2528 | 0.3 | 950 | 2.2518 | |
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| 2.2505 | 0.31 | 975 | 2.2500 | |
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| 2.1683 | 0.32 | 1000 | 2.2502 | |
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| 2.2177 | 0.33 | 1025 | 2.2501 | |
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| 2.238 | 0.34 | 1050 | 2.2516 | |
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| 2.193 | 0.34 | 1075 | 2.2507 | |
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| 2.2025 | 0.35 | 1100 | 2.2502 | |
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| 2.0944 | 0.36 | 1125 | 2.2512 | |
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| 2.2272 | 0.37 | 1150 | 2.2508 | |
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| 2.2264 | 0.38 | 1175 | 2.2500 | |
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| 2.1837 | 0.38 | 1200 | 2.2507 | |
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| 2.1444 | 0.39 | 1225 | 2.2489 | |
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| 2.2464 | 0.4 | 1250 | 2.2499 | |
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| 2.1388 | 0.41 | 1275 | 2.2508 | |
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| 2.193 | 0.42 | 1300 | 2.2492 | |
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| 2.2376 | 0.42 | 1325 | 2.2506 | |
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| 2.2212 | 0.43 | 1350 | 2.2478 | |
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| 2.2002 | 0.44 | 1375 | 2.2488 | |
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| 2.2729 | 0.45 | 1400 | 2.2484 | |
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| 2.2329 | 0.46 | 1425 | 2.2473 | |
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| 2.1919 | 0.46 | 1450 | 2.2481 | |
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| 2.2102 | 0.47 | 1475 | 2.2475 | |
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| 2.1466 | 0.48 | 1500 | 2.2473 | |
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| 2.1819 | 0.49 | 1525 | 2.2478 | |
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| 2.2558 | 0.5 | 1550 | 2.2468 | |
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| 2.2137 | 0.5 | 1575 | 2.2463 | |
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| 2.2288 | 0.51 | 1600 | 2.2466 | |
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| 2.1479 | 0.52 | 1625 | 2.2468 | |
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| 2.1726 | 0.53 | 1650 | 2.2471 | |
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| 2.1805 | 0.54 | 1675 | 2.2454 | |
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| 2.1505 | 0.54 | 1700 | 2.2470 | |
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| 2.1337 | 0.55 | 1725 | 2.2465 | |
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| 2.2413 | 0.56 | 1750 | 2.2460 | |
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| 2.152 | 0.57 | 1775 | 2.2478 | |
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| 2.2669 | 0.58 | 1800 | 2.2471 | |
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| 2.2925 | 0.58 | 1825 | 2.2465 | |
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| 2.222 | 0.59 | 1850 | 2.2457 | |
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| 2.1308 | 0.6 | 1875 | 2.2466 | |
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| 2.201 | 0.61 | 1900 | 2.2456 | |
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| 2.2247 | 0.62 | 1925 | 2.2460 | |
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| 2.2426 | 0.62 | 1950 | 2.2463 | |
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| 2.2312 | 0.63 | 1975 | 2.2465 | |
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| 2.2679 | 0.64 | 2000 | 2.2464 | |
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| 2.1928 | 0.65 | 2025 | 2.2463 | |
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| 2.2087 | 0.66 | 2050 | 2.2455 | |
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| 2.1792 | 0.66 | 2075 | 2.2470 | |
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| 2.252 | 0.67 | 2100 | 2.2468 | |
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| 2.2018 | 0.68 | 2125 | 2.2456 | |
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| 2.2006 | 0.69 | 2150 | 2.2451 | |
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| 2.2076 | 0.7 | 2175 | 2.2449 | |
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| 2.2436 | 0.7 | 2200 | 2.2460 | |
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| 2.2156 | 0.71 | 2225 | 2.2477 | |
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| 2.1348 | 0.72 | 2250 | 2.2455 | |
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| 2.1338 | 0.73 | 2275 | 2.2450 | |
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| 2.2147 | 0.74 | 2300 | 2.2455 | |
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| 2.2766 | 0.74 | 2325 | 2.2444 | |
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| 2.204 | 0.75 | 2350 | 2.2458 | |
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### Framework versions |
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- Transformers 4.36.2 |
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- Pytorch 2.1.2+cu121 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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