---
library_name: transformers
license: apache-2.0
base_model: alignment-handbook/zephyr-7b-sft-full
tags:
- alignment-handbook
- trl
- sft
- generated_from_trainer
- trl
- sft
- generated_from_trainer
datasets:
- EunsuKim/MATH
- EunsuKim/GSM8K
model-index:
- name: zephyr-7b-math-case-4
  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. -->

# zephyr-7b-math-case-4

This model is a fine-tuned version of [alignment-handbook/zephyr-7b-sft-full](https://huggingface.co/alignment-handbook/zephyr-7b-sft-full) on the EunsuKim/MATH and the EunsuKim/GSM8K datasets.
It achieves the following results on the evaluation set:
- Loss: 0.0151

## 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: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 32
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.9831        | 1.0   | 9    | 0.6522          |
| 0.601         | 2.0   | 18   | 0.4056          |
| 0.3464        | 3.0   | 27   | 0.1887          |
| 0.1559        | 4.0   | 36   | 0.0765          |
| 0.0714        | 5.0   | 45   | 0.0478          |
| 0.0477        | 6.0   | 54   | 0.0351          |
| 0.0345        | 7.0   | 63   | 0.0256          |
| 0.0252        | 8.0   | 72   | 0.0192          |
| 0.0181        | 9.0   | 81   | 0.0158          |
| 0.0153        | 10.0  | 90   | 0.0151          |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1