---
license: apache-2.0
base_model: mistralai/Mistral-7B-Instruct-v0.1
tags:
- trl
- sft
- generated_from_trainer
datasets:
- super_glue
metrics:
- accuracy
model-index:
- name: original_glue_boolq
  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. -->

# original_glue_boolq

This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) on the super_glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3115
- Accuracy: 0.8735

## 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: 2
- eval_batch_size: 4
- seed: 1
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.369         | 0.05  | 50   | 0.4328          | 0.8120   |
| 0.2708        | 0.1   | 100  | 0.4051          | 0.8283   |
| 0.6276        | 0.15  | 150  | 0.4020          | 0.8452   |
| 0.4395        | 0.2   | 200  | 0.3671          | 0.8452   |
| 0.3282        | 0.25  | 250  | 0.3746          | 0.8445   |
| 0.2967        | 0.3   | 300  | 0.3557          | 0.8523   |
| 0.2483        | 0.35  | 350  | 0.3862          | 0.8622   |
| 0.384         | 0.4   | 400  | 0.3765          | 0.8565   |
| 0.334         | 0.45  | 450  | 0.3628          | 0.8601   |
| 0.2671        | 0.5   | 500  | 0.3290          | 0.8664   |
| 0.2478        | 0.55  | 550  | 0.3421          | 0.8650   |
| 0.1814        | 0.6   | 600  | 0.3233          | 0.8693   |
| 0.3332        | 0.65  | 650  | 0.3451          | 0.8728   |
| 0.2063        | 0.7   | 700  | 0.3709          | 0.8678   |
| 0.2614        | 0.75  | 750  | 0.3530          | 0.8763   |
| 0.4273        | 0.8   | 800  | 0.3383          | 0.8721   |
| 0.1319        | 0.85  | 850  | 0.3360          | 0.8735   |
| 0.196         | 0.9   | 900  | 0.3096          | 0.8735   |
| 0.3564        | 0.95  | 950  | 0.3354          | 0.8770   |
| 0.3145        | 1.0   | 1000 | 0.3421          | 0.8784   |
| 0.1344        | 1.05  | 1050 | 0.4273          | 0.8735   |
| 0.4227        | 1.1   | 1100 | 0.3555          | 0.8707   |
| 0.1696        | 1.15  | 1150 | 0.3399          | 0.8742   |
| 0.5423        | 1.2   | 1200 | 0.3405          | 0.8813   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0