---
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