End of training

README.md

@@ -0,0 +1,161 @@
+---
+license: apache-2.0
+base_model: mistralai/Mistral-7B-v0.1
+tags:
+- generated_from_trainer
+model-index:
+- name: Mistral_Sparse_refined_web_50p_graceful_True
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# Mistral_Sparse_refined_web_50p_graceful_True
+
+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.
+It achieves the following results on the evaluation set:
+- Loss: 2.3416
+
+## 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: 2500
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss |
+|:-------------:|:-----:|:----:|:---------------:|
+| 3.7953        | 0.0   | 25   | 2.4029          |
+| 3.7258        | 0.01  | 50   | 2.3901          |
+| 2.3692        | 0.01  | 75   | 2.5472          |
+| 2.2497        | 0.02  | 100  | 2.4712          |
+| 2.2464        | 0.02  | 125  | 2.4471          |
+| 2.3202        | 0.02  | 150  | 2.4350          |
+| 2.258         | 0.03  | 175  | 2.4273          |
+| 2.2392        | 0.03  | 200  | 2.4204          |
+| 2.2739        | 0.04  | 225  | 2.4164          |
+| 2.309         | 0.04  | 250  | 2.4092          |
+| 2.2275        | 0.04  | 275  | 2.4080          |
+| 2.2396        | 0.05  | 300  | 2.4046          |
+| 2.2505        | 0.05  | 325  | 2.4010          |
+| 2.0898        | 0.06  | 350  | 2.4008          |
+| 2.2486        | 0.06  | 375  | 2.3971          |
+| 2.2873        | 0.06  | 400  | 2.3964          |
+| 2.2835        | 0.07  | 425  | 2.3950          |
+| 2.2461        | 0.07  | 450  | 2.3905          |
+| 2.16          | 0.08  | 475  | 2.3920          |
+| 2.2393        | 0.08  | 500  | 2.3910          |
+| 2.2949        | 0.08  | 525  | 2.3917          |
+| 2.1896        | 0.09  | 550  | 2.3885          |
+| 2.3139        | 0.09  | 575  | 2.3877          |
+| 2.1122        | 0.1   | 600  | 2.3899          |
+| 2.2103        | 0.1   | 625  | 2.3861          |
+| 2.2984        | 0.1   | 650  | 2.3888          |
+| 2.2784        | 0.11  | 675  | 2.3899          |
+| 2.2538        | 0.11  | 700  | 2.3870          |
+| 2.2308        | 0.12  | 725  | 2.3863          |
+| 2.1337        | 0.12  | 750  | 2.3856          |
+| 2.1904        | 0.12  | 775  | 2.3844          |
+| 2.2724        | 0.13  | 800  | 2.3845          |
+| 2.2887        | 0.13  | 825  | 2.3804          |
+| 2.2641        | 0.14  | 850  | 2.3818          |
+| 2.1342        | 0.14  | 875  | 2.3860          |
+| 2.2084        | 0.14  | 900  | 2.3813          |
+| 2.1455        | 0.15  | 925  | 2.3814          |
+| 2.2802        | 0.15  | 950  | 2.3849          |
+| 2.2011        | 0.16  | 975  | 2.3809          |
+| 2.2608        | 0.16  | 1000 | 2.3787          |
+| 2.2656        | 0.16  | 1025 | 2.3785          |
+| 2.2678        | 0.17  | 1050 | 2.3781          |
+| 2.3052        | 0.17  | 1075 | 2.3785          |
+| 2.2295        | 0.18  | 1100 | 2.3780          |
+| 2.2712        | 0.18  | 1125 | 2.3767          |
+| 2.2241        | 0.18  | 1150 | 2.3774          |
+| 2.2835        | 0.19  | 1175 | 2.3795          |
+| 2.1356        | 0.19  | 1200 | 2.3764          |
+| 2.2406        | 0.2   | 1225 | 2.3746          |
+| 2.2031        | 0.2   | 1250 | 2.3744          |
+| 2.257         | 0.2   | 1275 | 2.3738          |
+| 2.286         | 0.21  | 1300 | 2.3733          |
+| 2.2533        | 0.21  | 1325 | 2.3733          |
+| 2.143         | 0.22  | 1350 | 2.3726          |
+| 2.2535        | 0.22  | 1375 | 2.3740          |
+| 2.2282        | 0.22  | 1400 | 2.3758          |
+| 2.185         | 0.23  | 1425 | 2.3764          |
+| 2.1717        | 0.23  | 1450 | 2.3713          |
+| 2.1359        | 0.24  | 1475 | 2.3744          |
+| 2.1768        | 0.24  | 1500 | 2.3736          |
+| 2.1134        | 0.24  | 1525 | 2.3756          |
+| 2.1844        | 0.25  | 1550 | 2.3767          |
+| 2.3107        | 0.25  | 1575 | 2.3754          |
+| 2.2483        | 0.26  | 1600 | 2.3726          |
+| 2.2205        | 0.26  | 1625 | 2.3733          |
+| 2.3059        | 0.26  | 1650 | 2.3756          |
+| 2.218         | 0.27  | 1675 | 2.3723          |
+| 2.248         | 0.27  | 1700 | 2.3737          |
+| 2.2446        | 0.28  | 1725 | 2.3763          |
+| 2.2273        | 0.28  | 1750 | 2.3767          |
+| 2.2123        | 0.28  | 1775 | 2.3770          |
+| 2.1618        | 0.29  | 1800 | 2.3757          |
+| 2.2374        | 0.29  | 1825 | 2.3766          |
+| 2.2444        | 0.3   | 1850 | 2.3727          |
+| 2.2242        | 0.3   | 1875 | 2.3763          |
+| 2.2479        | 0.3   | 1900 | 2.3761          |
+| 2.2978        | 0.31  | 1925 | 2.3744          |
+| 2.2458        | 0.31  | 1950 | 2.3750          |
+| 2.2714        | 0.32  | 1975 | 2.3767          |
+| 2.187         | 0.32  | 2000 | 2.3766          |
+| 2.2036        | 0.32  | 2025 | 2.3757          |
+| 2.1915        | 0.33  | 2050 | 2.3756          |
+| 2.1981        | 0.33  | 2075 | 2.3755          |
+| 2.2788        | 0.34  | 2100 | 2.3768          |
+| 2.3118        | 0.34  | 2125 | 2.3762          |
+| 2.2293        | 0.34  | 2150 | 2.3747          |
+| 2.1811        | 0.35  | 2175 | 2.3745          |
+| 2.2163        | 0.35  | 2200 | 2.3749          |
+| 2.1632        | 0.36  | 2225 | 2.3740          |
+| 2.1539        | 0.36  | 2250 | 2.3778          |
+| 2.2845        | 0.36  | 2275 | 2.3722          |
+| 2.2686        | 0.37  | 2300 | 2.3708          |
+| 2.1612        | 0.37  | 2325 | 2.3716          |
+| 2.2057        | 0.38  | 2350 | 2.3709          |
+| 2.2451        | 0.38  | 2375 | 2.3687          |
+| 2.2544        | 0.38  | 2400 | 2.3698          |
+| 2.1076        | 0.39  | 2425 | 2.3721          |
+| 2.0986        | 0.39  | 2450 | 2.3684          |
+| 2.1876        | 0.4   | 2475 | 2.3712          |
+| 2.2792        | 0.4   | 2500 | 2.3749          |
+
+
+### Framework versions
+
+- Transformers 4.37.2
+- Pytorch 2.1.1+cu121
+- Datasets 2.15.0
+- Tokenizers 0.15.0

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