---
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: 0.3297
- Accuracy: 0.8700

## 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: 2
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4632        | 0.05  | 50   | 0.4840          | 0.7958   |
| 0.3453        | 0.1   | 100  | 0.3888          | 0.8226   |
| 0.2722        | 0.15  | 150  | 0.3590          | 0.8396   |
| 0.3266        | 0.2   | 200  | 0.3811          | 0.8459   |
| 0.3699        | 0.25  | 250  | 0.3534          | 0.8438   |
| 0.3554        | 0.3   | 300  | 0.3378          | 0.8565   |
| 0.1229        | 0.35  | 350  | 0.3368          | 0.8643   |
| 0.3522        | 0.4   | 400  | 0.3424          | 0.8643   |
| 0.2548        | 0.45  | 450  | 0.3467          | 0.8664   |
| 0.2119        | 0.5   | 500  | 0.3439          | 0.8714   |
| 0.2113        | 0.55  | 550  | 0.3518          | 0.8657   |
| 0.2122        | 0.6   | 600  | 0.3110          | 0.8770   |
| 0.3251        | 0.65  | 650  | 0.3323          | 0.8728   |
| 0.2904        | 0.7   | 700  | 0.3152          | 0.8792   |
| 0.6366        | 0.75  | 750  | 0.3502          | 0.8763   |
| 0.4161        | 0.8   | 800  | 0.3250          | 0.8806   |
| 0.1605        | 0.85  | 850  | 0.3258          | 0.8834   |
| 0.271         | 0.9   | 900  | 0.3330          | 0.8848   |


### Framework versions

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