---
library_name: peft
license: apache-2.0
base_model: mistralai/Mistral-7B-Instruct-v0.3
tags:
- generated_from_trainer
model-index:
- name: mistral-finetuned-toxicity3
  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-finetuned-toxicity3

This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7477

## 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: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step  | Validation Loss |
|:-------------:|:------:|:-----:|:---------------:|
| 0.9782        | 0.0185 | 200   | 0.9489          |
| 0.9499        | 0.0370 | 400   | 0.9159          |
| 0.8738        | 0.0555 | 600   | 0.9025          |
| 0.9153        | 0.0740 | 800   | 0.8785          |
| 0.8624        | 0.0925 | 1000  | 0.8715          |
| 0.8629        | 0.1110 | 1200  | 0.8655          |
| 0.8632        | 0.1294 | 1400  | 0.8521          |
| 0.838         | 0.1479 | 1600  | 0.8503          |
| 0.8168        | 0.1664 | 1800  | 0.8456          |
| 0.8198        | 0.1849 | 2000  | 0.8389          |
| 0.8243        | 0.2034 | 2200  | 0.8277          |
| 0.814         | 0.2219 | 2400  | 0.8315          |
| 0.8027        | 0.2404 | 2600  | 0.8229          |
| 0.8192        | 0.2589 | 2800  | 0.8173          |
| 0.8178        | 0.2774 | 3000  | 0.8161          |
| 0.7955        | 0.2959 | 3200  | 0.8132          |
| 0.786         | 0.3144 | 3400  | 0.8081          |
| 0.8196        | 0.3329 | 3600  | 0.8046          |
| 0.7996        | 0.3514 | 3800  | 0.8034          |
| 0.8236        | 0.3699 | 4000  | 0.7995          |
| 0.8192        | 0.3883 | 4200  | 0.7965          |
| 0.7898        | 0.4068 | 4400  | 0.7920          |
| 0.8018        | 0.4253 | 4600  | 0.7896          |
| 0.7837        | 0.4438 | 4800  | 0.7881          |
| 0.7802        | 0.4623 | 5000  | 0.7885          |
| 0.7856        | 0.4808 | 5200  | 0.7847          |
| 0.7873        | 0.4993 | 5400  | 0.7813          |
| 0.787         | 0.5178 | 5600  | 0.7806          |
| 0.7871        | 0.5363 | 5800  | 0.7781          |
| 0.7955        | 0.5548 | 6000  | 0.7787          |
| 0.7857        | 0.5733 | 6200  | 0.7745          |
| 0.7817        | 0.5918 | 6400  | 0.7729          |
| 0.7841        | 0.6103 | 6600  | 0.7735          |
| 0.7474        | 0.6288 | 6800  | 0.7683          |
| 0.7597        | 0.6472 | 7000  | 0.7707          |
| 0.7591        | 0.6657 | 7200  | 0.7666          |
| 0.7615        | 0.6842 | 7400  | 0.7646          |
| 0.7366        | 0.7027 | 7600  | 0.7647          |
| 0.7697        | 0.7212 | 7800  | 0.7611          |
| 0.7387        | 0.7397 | 8000  | 0.7599          |
| 0.7503        | 0.7582 | 8200  | 0.7577          |
| 0.7545        | 0.7767 | 8400  | 0.7566          |
| 0.7734        | 0.7952 | 8600  | 0.7540          |
| 0.7512        | 0.8137 | 8800  | 0.7532          |
| 0.7627        | 0.8322 | 9000  | 0.7512          |
| 0.7519        | 0.8507 | 9200  | 0.7520          |
| 0.7556        | 0.8692 | 9400  | 0.7489          |
| 0.7667        | 0.8877 | 9600  | 0.7472          |
| 0.7458        | 0.9061 | 9800  | 0.7465          |
| 0.7191        | 0.9246 | 10000 | 0.7457          |
| 0.7396        | 0.9431 | 10200 | 0.7423          |
| 0.7281        | 0.9616 | 10400 | 0.7426          |
| 0.7219        | 0.9801 | 10600 | 0.7416          |
| 0.7237        | 0.9986 | 10800 | 0.7389          |
| 0.589         | 1.0171 | 11000 | 0.7538          |
| 0.6071        | 1.0356 | 11200 | 0.7503          |
| 0.5696        | 1.0541 | 11400 | 0.7547          |
| 0.6019        | 1.0726 | 11600 | 0.7498          |
| 0.5741        | 1.0911 | 11800 | 0.7551          |
| 0.5922        | 1.1096 | 12000 | 0.7527          |
| 0.5721        | 1.1281 | 12200 | 0.7534          |
| 0.5856        | 1.1466 | 12400 | 0.7526          |
| 0.5775        | 1.1650 | 12600 | 0.7549          |
| 0.5911        | 1.1835 | 12800 | 0.7511          |
| 0.5983        | 1.2020 | 13000 | 0.7494          |
| 0.6213        | 1.2205 | 13200 | 0.7460          |
| 0.6006        | 1.2390 | 13400 | 0.7468          |
| 0.5658        | 1.2575 | 13600 | 0.7477          |


### Framework versions

- PEFT 0.13.2
- Transformers 4.46.3
- Pytorch 2.1.0
- Datasets 3.1.0
- Tokenizers 0.20.3