peft safetensors trl sft generated_from_trainer dataset:generator base_model:mistralai/Mixtral-8x7B-Instruct-v0.1 license:apache-2.0 region:us
---
license: apache-2.0
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
datasets:
- generator
base_model: mistralai/Mixtral-8x7B-Instruct-v0.1
model-index:
- name: Mixtral-Finetune-Output
  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. -->

# Mixtral-Finetune-Output

This model is a fine-tuned version of [mistralai/Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3062

## 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: 0.0001
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 0.03
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.5745        | 0.01  | 10   | 1.4701          |
| 1.5372        | 0.02  | 20   | 1.4541          |
| 1.4147        | 0.03  | 30   | 1.4433          |
| 1.423         | 0.04  | 40   | 1.4366          |
| 1.5318        | 0.05  | 50   | 1.4326          |
| 1.334         | 0.06  | 60   | 1.4296          |
| 1.364         | 0.07  | 70   | 1.4244          |
| 1.332         | 0.08  | 80   | 1.4194          |
| 1.3742        | 0.09  | 90   | 1.4163          |
| 1.4497        | 0.1   | 100  | 1.4124          |
| 1.4145        | 0.1   | 110  | 1.4098          |
| 1.4224        | 0.11  | 120  | 1.4050          |
| 1.4013        | 0.12  | 130  | 1.4017          |
| 1.547         | 0.13  | 140  | 1.4020          |
| 1.4969        | 0.14  | 150  | 1.3967          |
| 1.5716        | 0.15  | 160  | 1.3943          |
| 1.3677        | 0.16  | 170  | 1.3915          |
| 1.3789        | 0.17  | 180  | 1.3901          |
| 1.3188        | 0.18  | 190  | 1.3869          |
| 1.3317        | 0.19  | 200  | 1.3846          |
| 1.2552        | 0.2   | 210  | 1.3809          |
| 1.2584        | 0.21  | 220  | 1.3788          |
| 1.3958        | 0.22  | 230  | 1.3776          |
| 1.3345        | 0.23  | 240  | 1.3755          |
| 1.3562        | 0.24  | 250  | 1.3723          |
| 1.343         | 0.25  | 260  | 1.3726          |
| 1.3705        | 0.26  | 270  | 1.3695          |
| 1.5719        | 0.27  | 280  | 1.3687          |
| 1.3634        | 0.28  | 290  | 1.3652          |
| 1.4465        | 0.29  | 300  | 1.3668          |
| 1.3949        | 0.29  | 310  | 1.3642          |
| 1.3147        | 0.3   | 320  | 1.3631          |
| 1.368         | 0.31  | 330  | 1.3613          |
| 1.3482        | 0.32  | 340  | 1.3603          |
| 1.3143        | 0.33  | 350  | 1.3591          |
| 1.4717        | 0.34  | 360  | 1.3568          |
| 1.2089        | 0.35  | 370  | 1.3555          |
| 1.4223        | 0.36  | 380  | 1.3529          |
| 1.3895        | 0.37  | 390  | 1.3523          |
| 1.309         | 0.38  | 400  | 1.3504          |
| 1.3698        | 0.39  | 410  | 1.3487          |
| 1.2834        | 0.4   | 420  | 1.3468          |
| 1.2747        | 0.41  | 430  | 1.3471          |
| 1.3167        | 0.42  | 440  | 1.3460          |
| 1.3232        | 0.43  | 450  | 1.3438          |
| 1.3628        | 0.44  | 460  | 1.3422          |
| 1.3828        | 0.45  | 470  | 1.3417          |
| 1.3756        | 0.46  | 480  | 1.3412          |
| 1.385         | 0.47  | 490  | 1.3418          |
| 1.3622        | 0.48  | 500  | 1.3392          |
| 1.3322        | 0.49  | 510  | 1.3381          |
| 1.368         | 0.49  | 520  | 1.3365          |
| 1.3373        | 0.5   | 530  | 1.3355          |
| 1.4931        | 0.51  | 540  | 1.3354          |
| 1.3986        | 0.52  | 550  | 1.3333          |
| 1.3053        | 0.53  | 560  | 1.3312          |
| 1.2736        | 0.54  | 570  | 1.3297          |
| 1.2903        | 0.55  | 580  | 1.3298          |
| 1.328         | 0.56  | 590  | 1.3290          |
| 1.4081        | 0.57  | 600  | 1.3290          |
| 1.2852        | 0.58  | 610  | 1.3279          |
| 1.3636        | 0.59  | 620  | 1.3268          |
| 1.3448        | 0.6   | 630  | 1.3265          |
| 1.2061        | 0.61  | 640  | 1.3252          |
| 1.3519        | 0.62  | 650  | 1.3244          |
| 1.3632        | 0.63  | 660  | 1.3248          |
| 1.3784        | 0.64  | 670  | 1.3238          |
| 1.3349        | 0.65  | 680  | 1.3216          |
| 1.2603        | 0.66  | 690  | 1.3215          |
| 1.3566        | 0.67  | 700  | 1.3224          |
| 1.316         | 0.68  | 710  | 1.3208          |
| 1.1818        | 0.69  | 720  | 1.3203          |
| 1.3631        | 0.69  | 730  | 1.3190          |
| 1.3234        | 0.7   | 740  | 1.3184          |
| 1.2759        | 0.71  | 750  | 1.3177          |
| 1.3332        | 0.72  | 760  | 1.3177          |
| 1.2764        | 0.73  | 770  | 1.3165          |
| 1.2056        | 0.74  | 780  | 1.3155          |
| 1.4285        | 0.75  | 790  | 1.3158          |
| 1.3733        | 0.76  | 800  | 1.3150          |
| 1.2735        | 0.77  | 810  | 1.3143          |
| 1.3502        | 0.78  | 820  | 1.3137          |
| 1.093         | 0.79  | 830  | 1.3130          |
| 1.3451        | 0.8   | 840  | 1.3123          |
| 1.2942        | 0.81  | 850  | 1.3119          |
| 1.3258        | 0.82  | 860  | 1.3117          |
| 1.2139        | 0.83  | 870  | 1.3114          |
| 1.2773        | 0.84  | 880  | 1.3109          |
| 1.2324        | 0.85  | 890  | 1.3101          |
| 1.4134        | 0.86  | 900  | 1.3097          |
| 1.3464        | 0.87  | 910  | 1.3095          |
| 1.2972        | 0.88  | 920  | 1.3090          |
| 1.3305        | 0.88  | 930  | 1.3086          |
| 1.3394        | 0.89  | 940  | 1.3082          |
| 1.3666        | 0.9   | 950  | 1.3078          |
| 1.3703        | 0.91  | 960  | 1.3077          |
| 1.3019        | 0.92  | 970  | 1.3077          |
| 1.2618        | 0.93  | 980  | 1.3073          |
| 1.2808        | 0.94  | 990  | 1.3071          |
| 1.2927        | 0.95  | 1000 | 1.3069          |
| 1.2688        | 0.96  | 1010 | 1.3067          |
| 1.3312        | 0.97  | 1020 | 1.3065          |
| 1.2406        | 0.98  | 1030 | 1.3064          |
| 1.3341        | 0.99  | 1040 | 1.3062          |
| 1.3531        | 1.0   | 1050 | 1.3062          |


### Framework versions

- PEFT 0.7.1
- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0