---
license: apache-2.0
base_model: mistralai/Mistral-7B-v0.1
tags:
- generated_from_trainer
model-index:
- name: pippa-lora
  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. -->

[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
# pippa-lora

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: 1.3494

## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 40
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.7313        | 0.05  | 100  | 1.7044          |
| 1.68          | 0.11  | 200  | 1.6176          |
| 1.5642        | 0.16  | 300  | 1.5538          |
| 1.6617        | 0.22  | 400  | 1.4986          |
| 1.4733        | 0.27  | 500  | 1.4723          |
| 1.4916        | 0.33  | 600  | 1.4427          |
| 1.5036        | 0.38  | 700  | 1.4271          |
| 1.2385        | 0.44  | 800  | 1.4109          |
| 1.4094        | 0.49  | 900  | 1.3968          |
| 1.4042        | 0.55  | 1000 | 1.3848          |
| 1.3946        | 0.6   | 1100 | 1.3771          |
| 1.2523        | 0.66  | 1200 | 1.3692          |
| 1.2932        | 0.71  | 1300 | 1.3648          |
| 1.346         | 0.77  | 1400 | 1.3609          |
| 1.1163        | 0.82  | 1500 | 1.3565          |
| 1.4656        | 0.88  | 1600 | 1.3495          |
| 1.2698        | 0.93  | 1700 | 1.3484          |
| 1.2019        | 0.99  | 1800 | 1.3454          |
| 1.3685        | 1.04  | 1900 | 1.3477          |
| 1.2248        | 1.1   | 2000 | 1.3488          |
| 1.2162        | 1.15  | 2100 | 1.3479          |
| 1.0443        | 1.21  | 2200 | 1.3491          |
| 1.2445        | 1.26  | 2300 | 1.3460          |
| 1.3229        | 1.32  | 2400 | 1.3476          |
| 1.3464        | 1.37  | 2500 | 1.3439          |
| 1.2651        | 1.43  | 2600 | 1.3439          |
| 1.516         | 1.48  | 2700 | 1.3424          |
| 1.4323        | 1.54  | 2800 | 1.3413          |
| 1.08          | 1.59  | 2900 | 1.3436          |
| 1.289         | 1.64  | 3000 | 1.3379          |
| 1.1221        | 1.7   | 3100 | 1.3384          |
| 1.1895        | 1.75  | 3200 | 1.3376          |
| 1.3138        | 1.81  | 3300 | 1.3358          |
| 1.3907        | 1.86  | 3400 | 1.3343          |
| 1.4544        | 1.92  | 3500 | 1.3351          |
| 1.25          | 1.97  | 3600 | 1.3334          |
| 1.2682        | 2.03  | 3700 | 1.3452          |
| 1.3107        | 2.08  | 3800 | 1.3471          |
| 1.2096        | 2.14  | 3900 | 1.3496          |
| 1.4503        | 2.19  | 4000 | 1.3503          |
| 1.142         | 2.25  | 4100 | 1.3485          |
| 0.8439        | 2.3   | 4200 | 1.3490          |
| 1.2749        | 2.36  | 4300 | 1.3508          |
| 0.9578        | 2.41  | 4400 | 1.3502          |
| 1.2203        | 2.47  | 4500 | 1.3496          |
| 0.9451        | 2.52  | 4600 | 1.3498          |
| 0.9602        | 2.58  | 4700 | 1.3491          |
| 0.9501        | 2.63  | 4800 | 1.3491          |
| 1.2062        | 2.69  | 4900 | 1.3496          |
| 1.1728        | 2.74  | 5000 | 1.3491          |
| 1.2506        | 2.8   | 5100 | 1.3494          |
| 1.4052        | 2.85  | 5200 | 1.3494          |
| 1.2012        | 2.91  | 5300 | 1.3494          |
| 1.3141        | 2.96  | 5400 | 1.3494          |


### Framework versions

- Transformers 4.34.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.0