---
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_defaule
  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_defaule

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: 1.1508

## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- 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 |
|:-------------:|:------:|:----:|:---------------:|
| 1.1667        | 0.0261 | 4    | 1.1657          |
| 1.168         | 0.0523 | 8    | 1.1853          |
| 1.1834        | 0.0784 | 12   | 1.1752          |
| 1.0949        | 0.1046 | 16   | 1.1765          |
| 1.1669        | 0.1307 | 20   | 1.1847          |
| 1.06          | 0.1569 | 24   | 1.1693          |
| 1.1873        | 0.1830 | 28   | 1.1557          |
| 1.124         | 0.2092 | 32   | 1.1566          |
| 1.0828        | 0.2353 | 36   | 1.1538          |
| 1.1584        | 0.2614 | 40   | 1.1528          |
| 1.1773        | 0.2876 | 44   | 1.1493          |
| 1.1151        | 0.3137 | 48   | 1.1615          |
| 1.1327        | 0.3399 | 52   | 1.1592          |
| 1.094         | 0.3660 | 56   | 1.1487          |
| 1.1477        | 0.3922 | 60   | 1.1672          |
| 1.156         | 0.4183 | 64   | 1.1475          |
| 1.0724        | 0.4444 | 68   | 1.1658          |
| 1.0879        | 0.4706 | 72   | 1.1466          |
| 1.0652        | 0.4967 | 76   | 1.1522          |
| 1.1747        | 0.5229 | 80   | 1.1557          |
| 1.0867        | 0.5490 | 84   | 1.1524          |
| 1.1416        | 0.5752 | 88   | 1.1699          |
| 1.1987        | 0.6013 | 92   | 1.1498          |
| 1.1849        | 0.6275 | 96   | 1.1516          |
| 1.1133        | 0.6536 | 100  | 1.1447          |
| 1.136         | 0.6797 | 104  | 1.1526          |
| 1.1579        | 0.7059 | 108  | 1.1694          |
| 1.0263        | 0.7320 | 112  | 1.1502          |
| 1.093         | 0.7582 | 116  | 1.1325          |
| 1.0904        | 0.7843 | 120  | 1.1447          |
| 1.1481        | 0.8105 | 124  | 1.1550          |
| 1.1437        | 0.8366 | 128  | 1.1556          |
| 1.1645        | 0.8627 | 132  | 1.1541          |
| 1.0964        | 0.8889 | 136  | 1.1502          |
| 1.1825        | 0.9150 | 140  | 1.1487          |
| 1.0579        | 0.9412 | 144  | 1.1495          |
| 1.0728        | 0.9673 | 148  | 1.1504          |
| 1.2134        | 0.9935 | 152  | 1.1508          |


### Framework versions

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