---
base_model: unsloth/qwen2-7b-bnb-4bit
library_name: peft
license: apache-2.0
tags:
- unsloth
- generated_from_trainer
model-index:
- name: Qwen2-7B_magiccoder_reverse
  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. -->

# Qwen2-7B_magiccoder_reverse

This model is a fine-tuned version of [unsloth/qwen2-7b-bnb-4bit](https://huggingface.co./unsloth/qwen2-7b-bnb-4bit) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0907

## 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: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.02
- num_epochs: 1

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.9111        | 0.0261 | 4    | 0.9716          |
| 1.0695        | 0.0522 | 8    | 1.1827          |
| 1.1409        | 0.0783 | 12   | 1.1378          |
| 1.1017        | 0.1044 | 16   | 1.1270          |
| 1.1579        | 0.1305 | 20   | 1.1441          |
| 1.0234        | 0.1566 | 24   | 1.1212          |
| 1.0225        | 0.1827 | 28   | 1.1567          |
| 1.1587        | 0.2088 | 32   | 1.1425          |
| 1.0519        | 0.2349 | 36   | 1.1214          |
| 1.1006        | 0.2610 | 40   | 1.1195          |
| 1.1443        | 0.2871 | 44   | 1.1159          |
| 1.1088        | 0.3132 | 48   | 1.1123          |
| 1.0303        | 0.3393 | 52   | 1.1125          |
| 1.0499        | 0.3654 | 56   | 1.1232          |
| 1.082         | 0.3915 | 60   | 1.1239          |
| 1.1319        | 0.4176 | 64   | 1.1299          |
| 1.1228        | 0.4437 | 68   | 1.1170          |
| 1.0796        | 0.4698 | 72   | 1.1129          |
| 1.0974        | 0.4959 | 76   | 1.1162          |
| 1.0566        | 0.5220 | 80   | 1.1066          |
| 1.1243        | 0.5481 | 84   | 1.1036          |
| 1.0449        | 0.5742 | 88   | 1.1075          |
| 1.1215        | 0.6003 | 92   | 1.1022          |
| 1.0506        | 0.6264 | 96   | 1.0941          |
| 1.1367        | 0.6525 | 100  | 1.0924          |
| 1.03          | 0.6786 | 104  | 1.1014          |
| 1.0844        | 0.7047 | 108  | 1.1160          |
| 1.0575        | 0.7308 | 112  | 1.1058          |
| 1.0169        | 0.7569 | 116  | 1.1061          |
| 1.002         | 0.7830 | 120  | 1.1091          |
| 1.0741        | 0.8091 | 124  | 1.1094          |
| 1.0651        | 0.8352 | 128  | 1.1032          |
| 1.1222        | 0.8613 | 132  | 1.0976          |
| 1.0595        | 0.8874 | 136  | 1.0952          |
| 1.0879        | 0.9135 | 140  | 1.0931          |
| 1.0433        | 0.9396 | 144  | 1.0917          |
| 1.1012        | 0.9657 | 148  | 1.0908          |
| 1.0587        | 0.9918 | 152  | 1.0907          |


### Framework versions

- PEFT 0.12.0
- Transformers 4.44.0
- Pytorch 2.4.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1