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--- |
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license: apache-2.0 |
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base_model: mistralai/Mistral-7B-Instruct-v0.1 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- openwebtext |
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model-index: |
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- name: sparse_sparse_80_percent_pretraining_warmup_20K_steps_5k |
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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_sparse_80_percent_pretraining_warmup_20K_steps_5k |
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This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) on the openwebtext dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.7590 |
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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: 8 |
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- eval_batch_size: 16 |
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- seed: 0 |
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- distributed_type: multi-GPU |
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- num_devices: 6 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 96 |
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- total_eval_batch_size: 96 |
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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: 5000 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 1.2712 | 0.05 | 50 | 1.2374 | |
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| 1.0533 | 0.1 | 100 | 1.0529 | |
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| 0.9603 | 0.15 | 150 | 0.9668 | |
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| 0.9102 | 0.19 | 200 | 0.9145 | |
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| 0.8754 | 0.24 | 250 | 0.8775 | |
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| 0.8514 | 0.29 | 300 | 0.8503 | |
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| 0.8417 | 0.34 | 350 | 0.8298 | |
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| 0.8217 | 0.39 | 400 | 0.8146 | |
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| 0.8019 | 0.44 | 450 | 0.8026 | |
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| 0.7902 | 0.48 | 500 | 0.7914 | |
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| 0.7856 | 0.53 | 550 | 0.7819 | |
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| 0.7599 | 0.58 | 600 | 0.7734 | |
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| 0.7646 | 0.63 | 650 | 0.7689 | |
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| 0.7542 | 0.68 | 700 | 0.7635 | |
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| 0.7529 | 0.73 | 750 | 0.7581 | |
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| 0.7594 | 0.78 | 800 | 0.7533 | |
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| 0.7489 | 0.82 | 850 | 0.7493 | |
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| 0.7494 | 0.87 | 900 | 0.7452 | |
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| 0.7441 | 0.92 | 950 | 0.7472 | |
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| 0.7467 | 0.97 | 1000 | 0.7442 | |
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| 0.728 | 1.02 | 1050 | 0.7413 | |
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| 0.7263 | 1.07 | 1100 | 0.7384 | |
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| 0.7206 | 1.11 | 1150 | 0.7362 | |
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| 0.7223 | 1.16 | 1200 | 0.7343 | |
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| 0.7362 | 1.21 | 1250 | 0.7421 | |
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| 0.7374 | 1.26 | 1300 | 0.7401 | |
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| 0.7284 | 1.31 | 1350 | 0.7378 | |
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| 0.7309 | 1.36 | 1400 | 0.7356 | |
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| 0.724 | 1.41 | 1450 | 0.7339 | |
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| 0.72 | 1.45 | 1500 | 0.7317 | |
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| 0.73 | 1.5 | 1550 | 0.7509 | |
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| 0.7464 | 1.55 | 1600 | 0.7489 | |
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| 0.742 | 1.6 | 1650 | 0.7461 | |
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| 0.7378 | 1.65 | 1700 | 0.7447 | |
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| 0.7328 | 1.7 | 1750 | 0.7433 | |
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| 0.7433 | 1.75 | 1800 | 0.7411 | |
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### Framework versions |
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- Transformers 4.35.2 |
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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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