---
base_model: unsloth/mistral-7b-v0.3-bnb-4bit
library_name: peft
license: apache-2.0
tags:
- unsloth
- generated_from_trainer
model-index:
- name: Mistral-7B-v0.3_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. -->

# Mistral-7B-v0.3_magiccoder_reverse

This model is a fine-tuned version of [unsloth/mistral-7b-v0.3-bnb-4bit](https://huggingface.co/unsloth/mistral-7b-v0.3-bnb-4bit) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 7.2545

## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 1.6277        | 0.0262 | 4    | 2.6769          |
| 9.6471        | 0.0523 | 8    | 9.0033          |
| 10.5155       | 0.0785 | 12   | 8.9102          |
| 8.2554        | 0.1047 | 16   | 8.4103          |
| 7.9575        | 0.1308 | 20   | 7.8467          |
| 7.7793        | 0.1570 | 24   | 7.7779          |
| 7.8343        | 0.1832 | 28   | 7.8376          |
| 7.7751        | 0.2093 | 32   | 7.7518          |
| 7.7596        | 0.2355 | 36   | 7.8731          |
| 7.8576        | 0.2617 | 40   | 7.7542          |
| 7.8192        | 0.2878 | 44   | 7.6664          |
| 7.6969        | 0.3140 | 48   | 7.6550          |
| 7.6456        | 0.3401 | 52   | 7.6300          |
| 7.5219        | 0.3663 | 56   | 7.5777          |
| 7.5785        | 0.3925 | 60   | 7.5343          |
| 7.5603        | 0.4186 | 64   | 7.5427          |
| 7.6511        | 0.4448 | 68   | 7.4908          |
| 7.5751        | 0.4710 | 72   | 7.4379          |
| 7.5561        | 0.4971 | 76   | 7.5841          |
| 7.4865        | 0.5233 | 80   | 7.5991          |
| 7.4538        | 0.5495 | 84   | 7.4216          |
| 7.4582        | 0.5756 | 88   | 7.3826          |
| 7.5413        | 0.6018 | 92   | 7.3876          |
| 7.4509        | 0.6280 | 96   | 7.3721          |
| 7.4923        | 0.6541 | 100  | 7.4695          |
| 7.365         | 0.6803 | 104  | 7.4247          |
| 7.3943        | 0.7065 | 108  | 7.3939          |
| 7.3449        | 0.7326 | 112  | 7.3569          |
| 7.2922        | 0.7588 | 116  | 7.3034          |
| 7.3824        | 0.7850 | 120  | 7.2675          |
| 7.4081        | 0.8111 | 124  | 7.3202          |
| 7.3249        | 0.8373 | 128  | 7.2621          |
| 7.3576        | 0.8635 | 132  | 7.2639          |
| 7.2845        | 0.8896 | 136  | 7.2773          |
| 7.2098        | 0.9158 | 140  | 7.2565          |
| 7.2525        | 0.9419 | 144  | 7.2417          |
| 7.2333        | 0.9681 | 148  | 7.2520          |
| 7.2556        | 0.9943 | 152  | 7.2545          |


### Framework versions

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