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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: Mistral_Sparse_refined_web_70p_2024-03-12 |
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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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# Mistral_Sparse_refined_web_70p_2024-03-12 |
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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: 2.1411 |
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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: 1 |
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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: 4 |
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- total_train_batch_size: 16 |
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- total_eval_batch_size: 4 |
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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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| 2.7221 | 0.0 | 25 | 2.8218 | |
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| 2.4266 | 0.01 | 50 | 2.6972 | |
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| 2.4153 | 0.01 | 75 | 2.6181 | |
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| 2.3588 | 0.02 | 100 | 2.5695 | |
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| 2.3274 | 0.02 | 125 | 2.5427 | |
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| 2.4054 | 0.02 | 150 | 2.5244 | |
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| 2.3274 | 0.03 | 175 | 2.5144 | |
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| 2.3042 | 0.03 | 200 | 2.4995 | |
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| 2.3296 | 0.04 | 225 | 2.4898 | |
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| 2.3621 | 0.04 | 250 | 2.4844 | |
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| 2.2825 | 0.04 | 275 | 2.4756 | |
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| 2.2932 | 0.05 | 300 | 2.4704 | |
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| 2.3015 | 0.05 | 325 | 2.4693 | |
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| 2.139 | 0.06 | 350 | 2.4612 | |
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| 2.2953 | 0.06 | 375 | 2.4553 | |
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| 2.3358 | 0.06 | 400 | 2.4546 | |
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| 2.3302 | 0.07 | 425 | 2.4506 | |
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| 2.2814 | 0.07 | 450 | 2.4506 | |
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| 2.2014 | 0.08 | 475 | 2.4455 | |
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| 2.266 | 0.08 | 500 | 2.4434 | |
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| 2.3309 | 0.08 | 525 | 2.4430 | |
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| 2.2278 | 0.09 | 550 | 2.4417 | |
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| 2.3621 | 0.09 | 575 | 2.4384 | |
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| 2.1614 | 0.1 | 600 | 2.4385 | |
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| 2.2504 | 0.1 | 625 | 2.4370 | |
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| 2.3301 | 0.1 | 650 | 2.4350 | |
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| 2.3177 | 0.11 | 675 | 2.4331 | |
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| 2.2784 | 0.11 | 700 | 2.4307 | |
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| 2.2681 | 0.12 | 725 | 2.4305 | |
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| 2.1777 | 0.12 | 750 | 2.4314 | |
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| 2.2164 | 0.12 | 775 | 2.4321 | |
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| 2.3068 | 0.13 | 800 | 2.4292 | |
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| 2.3131 | 0.13 | 825 | 2.4267 | |
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| 2.2971 | 0.14 | 850 | 2.4256 | |
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| 2.1623 | 0.14 | 875 | 2.4231 | |
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| 2.2308 | 0.14 | 900 | 2.4246 | |
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| 2.1772 | 0.15 | 925 | 2.4259 | |
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| 2.3114 | 0.15 | 950 | 2.4226 | |
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| 2.2434 | 0.16 | 975 | 2.4268 | |
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| 2.2852 | 0.16 | 1000 | 2.4259 | |
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| 2.2924 | 0.16 | 1025 | 2.4262 | |
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| 2.3095 | 0.17 | 1050 | 2.4231 | |
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| 2.3378 | 0.17 | 1075 | 2.4225 | |
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| 2.265 | 0.18 | 1100 | 2.4181 | |
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| 2.2893 | 0.18 | 1125 | 2.4237 | |
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| 2.2577 | 0.18 | 1150 | 2.4176 | |
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| 2.3088 | 0.19 | 1175 | 2.4166 | |
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| 2.1623 | 0.19 | 1200 | 2.4139 | |
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| 2.2576 | 0.2 | 1225 | 2.4177 | |
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| 2.2411 | 0.2 | 1250 | 2.4160 | |
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| 2.28 | 0.2 | 1275 | 2.4171 | |
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| 2.3077 | 0.21 | 1300 | 2.4176 | |
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| 2.2814 | 0.21 | 1325 | 2.4186 | |
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| 2.1772 | 0.22 | 1350 | 2.4199 | |
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| 2.2554 | 0.22 | 1375 | 2.4190 | |
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| 2.2665 | 0.22 | 1400 | 2.4182 | |
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| 2.2058 | 0.23 | 1425 | 2.4171 | |
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| 2.1881 | 0.23 | 1450 | 2.4209 | |
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| 2.1567 | 0.24 | 1475 | 2.4186 | |
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| 2.2146 | 0.24 | 1500 | 2.4210 | |
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| 2.1493 | 0.24 | 1525 | 2.4207 | |
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| 2.2145 | 0.25 | 1550 | 2.4167 | |
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| 2.3312 | 0.25 | 1575 | 2.4187 | |
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| 2.2897 | 0.26 | 1600 | 2.4193 | |
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| 2.2592 | 0.26 | 1625 | 2.4170 | |
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| 2.3402 | 0.26 | 1650 | 2.4137 | |
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| 2.2354 | 0.27 | 1675 | 2.4165 | |
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| 2.2839 | 0.27 | 1700 | 2.4173 | |
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| 2.2681 | 0.28 | 1725 | 2.4177 | |
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| 2.2501 | 0.28 | 1750 | 2.4154 | |
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| 2.232 | 0.28 | 1775 | 2.4138 | |
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| 2.1882 | 0.29 | 1800 | 2.4142 | |
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| 2.2668 | 0.29 | 1825 | 2.4136 | |
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| 2.2641 | 0.3 | 1850 | 2.4110 | |
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| 2.2536 | 0.3 | 1875 | 2.4148 | |
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| 2.2732 | 0.3 | 1900 | 2.4159 | |
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| 2.3244 | 0.31 | 1925 | 2.4129 | |
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| 2.2639 | 0.31 | 1950 | 2.4135 | |
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| 2.2876 | 0.32 | 1975 | 2.4149 | |
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| 2.2108 | 0.32 | 2000 | 2.4116 | |
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| 2.233 | 0.32 | 2025 | 2.4163 | |
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| 2.2177 | 0.33 | 2050 | 2.4141 | |
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| 2.2132 | 0.33 | 2075 | 2.4143 | |
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| 2.3103 | 0.34 | 2100 | 2.4161 | |
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| 2.3486 | 0.34 | 2125 | 2.4129 | |
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| 2.2573 | 0.34 | 2150 | 2.4170 | |
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| 2.2096 | 0.35 | 2175 | 2.4110 | |
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| 2.241 | 0.35 | 2200 | 2.4135 | |
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| 2.1914 | 0.36 | 2225 | 2.4148 | |
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| 2.1867 | 0.36 | 2250 | 2.4132 | |
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| 2.3178 | 0.36 | 2275 | 2.4120 | |
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| 2.2948 | 0.37 | 2300 | 2.4071 | |
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| 2.1932 | 0.37 | 2325 | 2.4067 | |
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| 2.2373 | 0.38 | 2350 | 2.4121 | |
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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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