---
base_model: unsloth/mistral-7b-v0.3-bnb-4bit
library_name: peft
license: apache-2.0
tags:
- unsloth
- generated_from_trainer
model-index:
- name: mistralai_mistral_7b_v0.3_imdatta0_Magiccoder_evol_10k_reverse
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# mistralai_mistral_7b_v0.3_imdatta0_Magiccoder_evol_10k_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: 1.1504

## 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: 0.0001
- 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 |
|:-------------:|:------:|:----:|:---------------:|
| 1.1815        | 0.0262 | 4    | 1.2461          |
| 1.1779        | 0.0523 | 8    | 1.2277          |
| 1.2145        | 0.0785 | 12   | 1.2208          |
| 1.1589        | 0.1047 | 16   | 1.2399          |
| 1.2113        | 0.1308 | 20   | 1.2424          |
| 1.1171        | 0.1570 | 24   | 1.2347          |
| 1.2649        | 0.1832 | 28   | 1.2280          |
| 1.2005        | 0.2093 | 32   | 1.2154          |
| 1.1418        | 0.2355 | 36   | 1.2183          |
| 1.1896        | 0.2617 | 40   | 1.2063          |
| 1.2135        | 0.2878 | 44   | 1.2015          |
| 1.1641        | 0.3140 | 48   | 1.2015          |
| 1.1855        | 0.3401 | 52   | 1.2107          |
| 1.1493        | 0.3663 | 56   | 1.1929          |
| 1.168         | 0.3925 | 60   | 1.1938          |
| 1.2119        | 0.4186 | 64   | 1.2076          |
| 1.1207        | 0.4448 | 68   | 1.2077          |
| 1.1249        | 0.4710 | 72   | 1.1969          |
| 1.1242        | 0.4971 | 76   | 1.1923          |
| 1.2203        | 0.5233 | 80   | 1.1874          |
| 1.1168        | 0.5495 | 84   | 1.1766          |
| 1.1781        | 0.5756 | 88   | 1.1852          |
| 1.2153        | 0.6018 | 92   | 1.1785          |
| 1.213         | 0.6280 | 96   | 1.1682          |
| 1.1424        | 0.6541 | 100  | 1.1693          |
| 1.1577        | 0.6803 | 104  | 1.1702          |
| 1.1586        | 0.7065 | 108  | 1.1736          |
| 1.0325        | 0.7326 | 112  | 1.1546          |
| 1.1151        | 0.7588 | 116  | 1.1556          |
| 1.1153        | 0.7850 | 120  | 1.1539          |
| 1.1471        | 0.8111 | 124  | 1.1512          |
| 1.1408        | 0.8373 | 128  | 1.1488          |
| 1.1676        | 0.8635 | 132  | 1.1485          |
| 1.1049        | 0.8896 | 136  | 1.1489          |
| 1.1905        | 0.9158 | 140  | 1.1494          |
| 1.0539        | 0.9419 | 144  | 1.1500          |
| 1.0729        | 0.9681 | 148  | 1.1503          |
| 1.2164        | 0.9943 | 152  | 1.1504          |


### Framework versions

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