---
license: apache-2.0
base_model: mistralai/Mistral-7B-v0.1
tags:
- generated_from_trainer
model-index:
- name: Mistral_Sparse_refined_web_50p_2024-02-16
  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_2024-02-16

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: 2.1260

## 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: 3
- gradient_accumulation_steps: 3
- total_train_batch_size: 9
- total_eval_batch_size: 3
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1000

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.5975        | 0.01  | 25   | 2.6362          |
| 2.3082        | 0.01  | 50   | 2.5659          |
| 2.4024        | 0.02  | 75   | 2.5151          |
| 2.3358        | 0.02  | 100  | 2.4817          |
| 2.2267        | 0.03  | 125  | 2.4660          |
| 2.271         | 0.04  | 150  | 2.4456          |
| 2.1709        | 0.04  | 175  | 2.4413          |
| 2.2549        | 0.05  | 200  | 2.4306          |
| 2.2536        | 0.05  | 225  | 2.4243          |
| 2.2234        | 0.06  | 250  | 2.4212          |
| 2.2516        | 0.07  | 275  | 2.4202          |
| 2.2827        | 0.07  | 300  | 2.4146          |
| 2.1774        | 0.08  | 325  | 2.4156          |
| 2.278         | 0.08  | 350  | 2.4094          |
| 2.204         | 0.09  | 375  | 2.4088          |
| 2.1987        | 0.1   | 400  | 2.4073          |
| 2.1985        | 0.1   | 425  | 2.4041          |
| 2.2198        | 0.11  | 450  | 2.4069          |
| 2.2555        | 0.11  | 475  | 2.4014          |
| 2.1567        | 0.12  | 500  | 2.4017          |
| 2.2918        | 0.13  | 525  | 2.3998          |
| 2.2559        | 0.13  | 550  | 2.3959          |
| 2.2234        | 0.14  | 575  | 2.3978          |
| 2.2001        | 0.14  | 600  | 2.3944          |
| 2.1409        | 0.15  | 625  | 2.3957          |
| 2.2034        | 0.16  | 650  | 2.3981          |
| 2.1863        | 0.16  | 675  | 2.3941          |
| 2.2372        | 0.17  | 700  | 2.3936          |
| 2.2438        | 0.17  | 725  | 2.3953          |
| 2.2172        | 0.18  | 750  | 2.3943          |
| 2.1917        | 0.19  | 775  | 2.3921          |
| 2.1137        | 0.19  | 800  | 2.3912          |
| 2.0766        | 0.07  | 825  | 2.3935          |
| 2.1926        | 0.08  | 850  | 2.3913          |
| 2.2948        | 0.08  | 875  | 2.3915          |
| 2.1349        | 0.08  | 900  | 2.3917          |
| 2.2446        | 0.08  | 925  | 2.3876          |
| 2.253         | 0.09  | 950  | 2.3880          |
| 2.0729        | 0.09  | 975  | 2.3890          |
| 2.1965        | 0.09  | 1000 | 2.3873          |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0