---
license: apache-2.0
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: mistralai/Mistral-7B-Instruct-v0.2
model-index:
- name: mistral-7b-scientific-mcq
  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-7b-scientific-mcq

This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7480

## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 3.0

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.9911        | 0.0581 | 100  | 0.8124          |
| 0.879         | 0.1162 | 200  | 0.7703          |
| 0.9359        | 0.1743 | 300  | 0.7576          |
| 0.7608        | 0.2325 | 400  | 0.7523          |
| 0.8144        | 0.2906 | 500  | 0.7469          |
| 0.8655        | 0.3487 | 600  | 0.7435          |
| 0.6748        | 0.4068 | 700  | 0.7390          |
| 0.7004        | 0.4649 | 800  | 0.7369          |
| 0.7561        | 0.5230 | 900  | 0.7351          |
| 0.7053        | 0.5811 | 1000 | 0.7317          |
| 0.7122        | 0.6393 | 1100 | 0.7294          |
| 0.7431        | 0.6974 | 1200 | 0.7279          |
| 0.6102        | 0.7555 | 1300 | 0.7255          |
| 0.7041        | 0.8136 | 1400 | 0.7244          |
| 0.7339        | 0.8717 | 1500 | 0.7227          |
| 0.6648        | 0.9298 | 1600 | 0.7207          |
| 0.5682        | 0.9879 | 1700 | 0.7192          |
| 0.6745        | 1.0461 | 1800 | 0.7242          |
| 0.6003        | 1.1042 | 1900 | 0.7258          |
| 0.6755        | 1.1623 | 2000 | 0.7273          |
| 0.6815        | 1.2204 | 2100 | 0.7265          |
| 0.5531        | 1.2785 | 2200 | 0.7253          |
| 0.5           | 1.3366 | 2300 | 0.7250          |
| 0.666         | 1.3947 | 2400 | 0.7236          |
| 0.518         | 1.4529 | 2500 | 0.7247          |
| 0.6223        | 1.5110 | 2600 | 0.7240          |
| 0.565         | 1.5691 | 2700 | 0.7234          |
| 0.5541        | 1.6272 | 2800 | 0.7220          |
| 0.7622        | 1.6853 | 2900 | 0.7220          |
| 0.5212        | 1.7434 | 3000 | 0.7223          |
| 0.6089        | 1.8015 | 3100 | 0.7205          |
| 0.6908        | 1.8597 | 3200 | 0.7210          |
| 0.6138        | 1.9178 | 3300 | 0.7204          |
| 0.6425        | 1.9759 | 3400 | 0.7199          |
| 0.4918        | 2.0340 | 3500 | 0.7416          |
| 0.5432        | 2.0921 | 3600 | 0.7468          |
| 0.6497        | 2.1502 | 3700 | 0.7463          |
| 0.5068        | 2.2083 | 3800 | 0.7448          |
| 0.5502        | 2.2665 | 3900 | 0.7475          |
| 0.4795        | 2.3246 | 4000 | 0.7482          |
| 0.5718        | 2.3827 | 4100 | 0.7486          |
| 0.5154        | 2.4408 | 4200 | 0.7474          |
| 0.6959        | 2.4989 | 4300 | 0.7479          |
| 0.5848        | 2.5570 | 4400 | 0.7473          |
| 0.5662        | 2.6151 | 4500 | 0.7479          |
| 0.4357        | 2.6733 | 4600 | 0.7482          |
| 0.5318        | 2.7314 | 4700 | 0.7476          |
| 0.4631        | 2.7895 | 4800 | 0.7480          |
| 0.5852        | 2.8476 | 4900 | 0.7481          |
| 0.5633        | 2.9057 | 5000 | 0.7480          |
| 0.5831        | 2.9638 | 5100 | 0.7480          |


### Framework versions

- PEFT 0.11.1
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1