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