peft safetensors trl sft generated_from_trainer base_model:mistralai/Mistral-7B-v0.1 license:apache-2.0 region:us
---
license: apache-2.0
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: mistralai/Mistral-7B-v0.1
model-index:
- name: results
  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. -->

# results

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: 0.4597

## 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: 0.00025
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 25

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.9748        | 0.12  | 50   | 0.8738          |
| 0.7514        | 0.23  | 100  | 0.7808          |
| 0.7228        | 0.35  | 150  | 0.7320          |
| 0.6811        | 0.47  | 200  | 0.6688          |
| 0.6477        | 0.59  | 250  | 0.6679          |
| 0.6606        | 0.7   | 300  | 0.6215          |
| 0.5836        | 0.82  | 350  | 0.5981          |
| 0.6127        | 0.94  | 400  | 0.5961          |
| 0.4759        | 1.05  | 450  | 0.5341          |
| 0.496         | 1.17  | 500  | 0.5321          |
| 0.47          | 1.29  | 550  | 0.5168          |
| 0.4519        | 1.41  | 600  | 0.5090          |
| 0.4619        | 1.52  | 650  | 0.5004          |
| 0.4579        | 1.64  | 700  | 0.5164          |
| 0.437         | 1.76  | 750  | 0.4887          |
| 0.4274        | 1.87  | 800  | 0.4798          |
| 0.4651        | 1.99  | 850  | 0.4784          |
| 0.3846        | 2.11  | 900  | 0.4999          |
| 0.3846        | 2.22  | 950  | 0.4896          |
| 0.3732        | 2.34  | 1000 | 0.4792          |
| 0.3815        | 2.46  | 1050 | 0.4799          |
| 0.403         | 2.58  | 1100 | 0.4777          |
| 0.3593        | 2.69  | 1150 | 0.4875          |
| 0.381         | 2.81  | 1200 | 0.4763          |
| 0.4044        | 2.93  | 1250 | 0.4504          |
| 0.3381        | 3.04  | 1300 | 0.4543          |
| 0.3111        | 3.16  | 1350 | 0.4597          |
| 0.322         | 3.28  | 1400 | 0.4796          |
| 0.3211        | 3.4   | 1450 | 0.4634          |
| 0.3339        | 3.51  | 1500 | 0.4936          |
| 0.329         | 3.63  | 1550 | 0.4479          |
| 0.3454        | 3.75  | 1600 | 0.4520          |
| 0.337         | 3.86  | 1650 | 0.4395          |
| 0.3305        | 3.98  | 1700 | 0.4373          |
| 0.2809        | 4.1   | 1750 | 0.4747          |
| 0.2918        | 4.22  | 1800 | 0.4705          |
| 0.2994        | 4.33  | 1850 | 0.4778          |
| 0.3006        | 4.45  | 1900 | 0.4759          |
| 0.3048        | 4.57  | 1950 | 0.4615          |
| 0.3183        | 4.68  | 2000 | 0.4494          |
| 0.3125        | 4.8   | 2050 | 0.4584          |
| 0.3112        | 4.92  | 2100 | 0.4461          |
| 0.279         | 5.04  | 2150 | 0.4692          |
| 0.2686        | 5.15  | 2200 | 0.4563          |
| 0.2794        | 5.27  | 2250 | 0.4467          |
| 0.2774        | 5.39  | 2300 | 0.4541          |
| 0.2927        | 5.5   | 2350 | 0.4529          |
| 0.2899        | 5.62  | 2400 | 0.4458          |
| 0.2865        | 5.74  | 2450 | 0.4469          |
| 0.2965        | 5.85  | 2500 | 0.4469          |
| 0.2901        | 5.97  | 2550 | 0.4471          |
| 0.2381        | 6.09  | 2600 | 0.4777          |
| 0.257         | 6.21  | 2650 | 0.4677          |
| 0.256         | 6.32  | 2700 | 0.4847          |
| 0.2658        | 6.44  | 2750 | 0.4820          |
| 0.2617        | 6.56  | 2800 | 0.4602          |
| 0.2761        | 6.67  | 2850 | 0.4597          |


### Framework versions

- PEFT 0.7.1
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0