---
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: 1.0421

## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 0.9108        | 0.0261 | 4    | 0.9707          |
| 0.9882        | 0.0522 | 8    | 1.0348          |
| 1.0325        | 0.0783 | 12   | 1.0383          |
| 1.0044        | 0.1044 | 16   | 1.0488          |
| 1.0614        | 0.1305 | 20   | 1.0597          |
| 0.9711        | 0.1566 | 24   | 1.0676          |
| 0.979         | 0.1827 | 28   | 1.0751          |
| 1.1009        | 0.2088 | 32   | 1.0812          |
| 1.014         | 0.2349 | 36   | 1.0818          |
| 1.057         | 0.2610 | 40   | 1.0841          |
| 1.1137        | 0.2871 | 44   | 1.0848          |
| 1.0856        | 0.3132 | 48   | 1.0879          |
| 1.0044        | 0.3393 | 52   | 1.0875          |
| 1.0199        | 0.3654 | 56   | 1.0862          |
| 1.0503        | 0.3915 | 60   | 1.0861          |
| 1.0973        | 0.4176 | 64   | 1.0831          |
| 1.0846        | 0.4437 | 68   | 1.0824          |
| 1.051         | 0.4698 | 72   | 1.0805          |
| 1.0629        | 0.4959 | 76   | 1.0797          |
| 1.0294        | 0.5220 | 80   | 1.0759          |
| 1.0989        | 0.5481 | 84   | 1.0724          |
| 1.0112        | 0.5742 | 88   | 1.0708          |
| 1.0885        | 0.6003 | 92   | 1.0696          |
| 1.0212        | 0.6264 | 96   | 1.0651          |
| 1.1114        | 0.6525 | 100  | 1.0617          |
| 0.994         | 0.6786 | 104  | 1.0602          |
| 1.0346        | 0.7047 | 108  | 1.0566          |
| 1.0053        | 0.7308 | 112  | 1.0539          |
| 0.9673        | 0.7569 | 116  | 1.0506          |
| 0.9526        | 0.7830 | 120  | 1.0489          |
| 1.0196        | 0.8091 | 124  | 1.0475          |
| 1.0054        | 0.8352 | 128  | 1.0456          |
| 1.0743        | 0.8613 | 132  | 1.0439          |
| 1.011         | 0.8874 | 136  | 1.0430          |
| 1.0417        | 0.9135 | 140  | 1.0426          |
| 0.9953        | 0.9396 | 144  | 1.0422          |
| 1.0592        | 0.9657 | 148  | 1.0420          |
| 1.019         | 0.9918 | 152  | 1.0421          |


### Framework versions

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