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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_50p_2024-03-22 |
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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_50p_2024-03-22 |
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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: 2.1289 |
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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: 3 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 12 |
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- total_eval_batch_size: 3 |
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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: 1251 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 2.4177 | 0.0 | 25 | 2.6401 | |
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| 2.5407 | 0.01 | 50 | 2.5820 | |
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| 2.3887 | 0.01 | 75 | 2.5299 | |
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| 2.2849 | 0.01 | 100 | 2.4991 | |
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| 2.2042 | 0.01 | 125 | 2.4802 | |
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| 2.2574 | 0.02 | 150 | 2.4609 | |
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| 2.2353 | 0.02 | 175 | 2.4473 | |
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| 2.3355 | 0.02 | 200 | 2.4449 | |
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| 2.3044 | 0.03 | 225 | 2.4381 | |
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| 2.2664 | 0.03 | 250 | 2.4348 | |
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| 2.1999 | 0.03 | 275 | 2.4263 | |
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| 2.2631 | 0.04 | 300 | 2.4247 | |
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| 2.2918 | 0.04 | 325 | 2.4184 | |
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| 2.1426 | 0.04 | 350 | 2.4185 | |
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| 2.149 | 0.04 | 375 | 2.4158 | |
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| 2.1937 | 0.05 | 400 | 2.4129 | |
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| 2.2372 | 0.05 | 425 | 2.4134 | |
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| 2.1997 | 0.05 | 450 | 2.4123 | |
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| 2.2937 | 0.06 | 475 | 2.4086 | |
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| 2.3067 | 0.06 | 500 | 2.4052 | |
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| 2.312 | 0.06 | 525 | 2.4060 | |
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| 2.257 | 0.07 | 550 | 2.4056 | |
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| 2.2729 | 0.07 | 575 | 2.4051 | |
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| 2.1952 | 0.07 | 600 | 2.4065 | |
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| 2.1225 | 0.07 | 625 | 2.3999 | |
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| 2.2168 | 0.08 | 650 | 2.4039 | |
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| 2.1682 | 0.08 | 675 | 2.4006 | |
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| 2.3027 | 0.08 | 700 | 2.4028 | |
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| 2.2077 | 0.09 | 725 | 2.4006 | |
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| 2.2119 | 0.09 | 750 | 2.3980 | |
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| 2.2539 | 0.09 | 775 | 2.3997 | |
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| 2.1323 | 0.1 | 800 | 2.3973 | |
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| 2.3612 | 0.1 | 825 | 2.4018 | |
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| 2.1674 | 0.1 | 850 | 2.3991 | |
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| 2.4429 | 0.1 | 875 | 2.4005 | |
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| 2.2753 | 0.11 | 900 | 2.3941 | |
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| 2.2058 | 0.11 | 925 | 2.3968 | |
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| 2.261 | 0.11 | 950 | 2.3983 | |
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| 2.1146 | 0.12 | 975 | 2.3945 | |
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| 2.1637 | 0.12 | 1000 | 2.3920 | |
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| 2.1595 | 0.12 | 1025 | 2.3933 | |
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| 2.3698 | 0.13 | 1050 | 2.3932 | |
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| 2.2472 | 0.13 | 1075 | 2.3914 | |
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| 2.2437 | 0.13 | 1100 | 2.3902 | |
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| 2.2625 | 0.13 | 1125 | 2.3911 | |
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| 2.2165 | 0.14 | 1150 | 2.3881 | |
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| 2.1428 | 0.14 | 1175 | 2.3888 | |
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| 2.1683 | 0.14 | 1200 | 2.3908 | |
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| 2.1771 | 0.15 | 1225 | 2.3923 | |
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| 2.153 | 0.15 | 1250 | 2.3900 | |
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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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