---
base_model: mistralai/Mistral-7B-v0.1
library_name: peft
license: apache-2.0
metrics:
- accuracy
tags:
- trl
- reward-trainer
- generated_from_trainer
model-index:
- name: pairwise-reward-sft-zephyr-7b-sft-qlora-ultrafeedback-ultrafeedback-binarized-20241013-124646
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# pairwise-reward-sft-zephyr-7b-sft-qlora-ultrafeedback-ultrafeedback-binarized-20241013-124646

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: 0.4739
- Accuracy: 0.7592

## 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: 1.5e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1.0

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.6209        | 0.0526 | 100  | 0.6427          | 0.6784   |
| 0.6346        | 0.1052 | 200  | 0.5829          | 0.7165   |
| 0.5945        | 0.1578 | 300  | 0.5333          | 0.7351   |
| 0.5258        | 0.2104 | 400  | 0.5169          | 0.7461   |
| 0.4914        | 0.2630 | 500  | 0.5209          | 0.7346   |
| 0.4995        | 0.3155 | 600  | 0.5056          | 0.7536   |
| 0.5272        | 0.3681 | 700  | 0.5041          | 0.7541   |
| 0.4993        | 0.4207 | 800  | 0.4943          | 0.7471   |
| 0.5317        | 0.4733 | 900  | 0.4970          | 0.7602   |
| 0.5193        | 0.5259 | 1000 | 0.4850          | 0.7597   |
| 0.4534        | 0.5785 | 1100 | 0.4931          | 0.7582   |
| 0.4828        | 0.6311 | 1200 | 0.4808          | 0.7582   |
| 0.5432        | 0.6837 | 1300 | 0.4836          | 0.7491   |
| 0.4343        | 0.7363 | 1400 | 0.4797          | 0.7582   |
| 0.4287        | 0.7889 | 1500 | 0.4794          | 0.7612   |
| 0.5117        | 0.8414 | 1600 | 0.4799          | 0.7587   |
| 0.4369        | 0.8940 | 1700 | 0.4770          | 0.7582   |
| 0.4537        | 0.9466 | 1800 | 0.4750          | 0.7566   |
| 0.451         | 0.9992 | 1900 | 0.4739          | 0.7592   |


### Framework versions

- PEFT 0.12.0
- Transformers 4.45.2
- Pytorch 2.4.0+cu121
- Datasets 2.20.0
- Tokenizers 0.20.0