peft safetensors generated_from_trainer base_model:mistralai/Mistral-7B-v0.1 license:apache-2.0 region:us
---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
metrics:
- accuracy
- matthews_correlation
base_model: mistralai/Mistral-7B-v0.1
model-index:
- name: Mistral-7B-v0.1_cola_sparse_swiglu_ignore_0_1
  results: []
---

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# Mistral-7B-v0.1_cola_sparse_swiglu_ignore_0_1

This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4277
- Accuracy: {'accuracy': 0.8212616822429907}
- Matthews Correlation: 0.5699

## 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: 8
- eval_batch_size: 16
- seed: 2
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy                         | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------:|:--------------------:|
| 1.6028        | 0.17  | 20   | 1.5539          | {'accuracy': 0.5417066155321189} | 0.0764               |
| 0.9736        | 0.33  | 40   | 0.9708          | {'accuracy': 0.6864813039309684} | 0.1852               |
| 0.7146        | 0.5   | 60   | 0.7850          | {'accuracy': 0.713326941514861}  | 0.3141               |
| 0.6892        | 0.66  | 80   | 0.6674          | {'accuracy': 0.7238734419942474} | 0.3498               |
| 0.6792        | 0.83  | 100  | 0.6401          | {'accuracy': 0.7411313518696069} | 0.3977               |
| 0.6233        | 1.0   | 120  | 0.6104          | {'accuracy': 0.7574304889741131} | 0.3784               |
| 0.4778        | 1.16  | 140  | 0.5641          | {'accuracy': 0.7948226270373921} | 0.4874               |
| 0.4792        | 1.33  | 160  | 0.5961          | {'accuracy': 0.7746883988494727} | 0.4284               |
| 0.5573        | 1.49  | 180  | 0.5210          | {'accuracy': 0.8034515819750719} | 0.5126               |
| 0.4464        | 1.66  | 200  | 0.5716          | {'accuracy': 0.7871524448705657} | 0.5601               |
| 0.4541        | 1.83  | 220  | 0.5130          | {'accuracy': 0.8015340364333653} | 0.5046               |
| 0.4989        | 1.99  | 240  | 0.4648          | {'accuracy': 0.8149568552253116} | 0.5452               |
| 0.3891        | 2.16  | 260  | 0.4566          | {'accuracy': 0.8207094918504314} | 0.5856               |
| 0.336         | 2.32  | 280  | 0.4516          | {'accuracy': 0.822627037392138}  | 0.5657               |
| 0.3854        | 2.49  | 300  | 0.4224          | {'accuracy': 0.8322147651006712} | 0.6066               |
| 0.3917        | 2.66  | 320  | 0.4247          | {'accuracy': 0.837967401725791}  | 0.6125               |
| 0.3779        | 2.82  | 340  | 0.4177          | {'accuracy': 0.8302972195589645} | 0.5897               |
| 0.3462        | 2.99  | 360  | 0.4649          | {'accuracy': 0.8207094918504314} | 0.5584               |
| 0.3448        | 3.15  | 380  | 0.4182          | {'accuracy': 0.8293384467881112} | 0.5837               |
| 0.3894        | 3.32  | 400  | 0.4388          | {'accuracy': 0.8302972195589645} | 0.5893               |


### Framework versions

- PEFT 0.7.1
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0