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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_graceful_True |
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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_graceful_True |
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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: 2.3402 |
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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: 2500 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 3.7252 | 0.01 | 50 | 2.3893 | |
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| 2.2531 | 0.02 | 100 | 2.4723 | |
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| 2.32 | 0.02 | 150 | 2.4385 | |
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| 2.2363 | 0.03 | 200 | 2.4210 | |
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| 2.3078 | 0.04 | 250 | 2.4118 | |
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| 2.2389 | 0.05 | 300 | 2.4025 | |
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| 2.0902 | 0.06 | 350 | 2.3984 | |
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| 2.2878 | 0.06 | 400 | 2.3965 | |
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| 2.2485 | 0.07 | 450 | 2.3924 | |
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| 2.2375 | 0.08 | 500 | 2.3895 | |
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| 2.1901 | 0.09 | 550 | 2.3909 | |
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| 2.1128 | 0.1 | 600 | 2.3886 | |
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| 2.2983 | 0.1 | 650 | 2.3892 | |
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| 2.2547 | 0.11 | 700 | 2.3873 | |
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| 2.1322 | 0.12 | 750 | 2.3861 | |
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| 2.2715 | 0.13 | 800 | 2.3827 | |
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| 2.263 | 0.14 | 850 | 2.3845 | |
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| 2.2066 | 0.14 | 900 | 2.3836 | |
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| 2.2781 | 0.15 | 950 | 2.3837 | |
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| 2.2597 | 0.16 | 1000 | 2.3778 | |
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| 2.2642 | 0.17 | 1050 | 2.3764 | |
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| 2.2296 | 0.18 | 1100 | 2.3805 | |
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| 2.2289 | 0.18 | 1150 | 2.3784 | |
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| 2.1372 | 0.19 | 1200 | 2.3773 | |
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| 2.2059 | 0.2 | 1250 | 2.3732 | |
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| 2.2847 | 0.21 | 1300 | 2.3719 | |
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| 2.1404 | 0.22 | 1350 | 2.3739 | |
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| 2.2261 | 0.22 | 1400 | 2.3752 | |
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| 2.1713 | 0.23 | 1450 | 2.3750 | |
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| 2.1787 | 0.24 | 1500 | 2.3732 | |
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| 2.1866 | 0.25 | 1550 | 2.3759 | |
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| 2.2471 | 0.26 | 1600 | 2.3760 | |
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| 2.307 | 0.26 | 1650 | 2.3745 | |
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| 2.2457 | 0.27 | 1700 | 2.3746 | |
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| 2.2265 | 0.28 | 1750 | 2.3775 | |
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| 2.163 | 0.29 | 1800 | 2.3797 | |
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| 2.2411 | 0.3 | 1850 | 2.3760 | |
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| 2.247 | 0.3 | 1900 | 2.3770 | |
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| 2.2449 | 0.31 | 1950 | 2.3749 | |
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| 2.1884 | 0.32 | 2000 | 2.3728 | |
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| 2.1909 | 0.33 | 2050 | 2.3770 | |
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| 2.2813 | 0.34 | 2100 | 2.3773 | |
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| 2.2306 | 0.34 | 2150 | 2.3755 | |
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| 2.2158 | 0.35 | 2200 | 2.3777 | |
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| 2.1557 | 0.36 | 2250 | 2.3783 | |
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| 2.2715 | 0.37 | 2300 | 2.3704 | |
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| 2.2053 | 0.38 | 2350 | 2.3729 | |
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| 2.2541 | 0.38 | 2400 | 2.3715 | |
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| 2.0971 | 0.39 | 2450 | 2.3747 | |
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| 2.2791 | 0.4 | 2500 | 2.3727 | |
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### Framework versions |
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- Transformers 4.37.2 |
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- Pytorch 2.1.1+cu121 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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