---
license: apache-2.0
library_name: peft
tags:
- unsloth
- generated_from_trainer
base_model: unsloth/mistral-7b-v0.3-bnb-4bit
model-index:
- name: mistral_7b_v_Magiccoder_evol_10k_qlora_ortho
  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_v_Magiccoder_evol_10k_qlora_ortho

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: 1.1813

## 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_steps: 0.02
- num_epochs: 1

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.2034        | 0.0262 | 4    | 1.2458          |
| 1.1597        | 0.0523 | 8    | 1.2035          |
| 1.1977        | 0.0785 | 12   | 1.2045          |
| 1.1152        | 0.1047 | 16   | 1.2144          |
| 1.1623        | 0.1308 | 20   | 1.2207          |
| 1.0816        | 0.1570 | 24   | 1.1929          |
| 1.2421        | 0.1832 | 28   | 1.2018          |
| 1.1908        | 0.2093 | 32   | 1.2023          |
| 1.1187        | 0.2355 | 36   | 1.1926          |
| 1.2034        | 0.2617 | 40   | 1.1915          |
| 1.2092        | 0.2878 | 44   | 1.1850          |
| 1.1567        | 0.3140 | 48   | 1.2156          |
| 1.1722        | 0.3401 | 52   | 1.1912          |
| 1.162         | 0.3663 | 56   | 1.2044          |
| 1.1497        | 0.3925 | 60   | 1.1980          |
| 1.2205        | 0.4186 | 64   | 1.1945          |
| 1.0966        | 0.4448 | 68   | 1.1971          |
| 1.123         | 0.4710 | 72   | 1.1945          |
| 1.1222        | 0.4971 | 76   | 1.1951          |
| 1.2472        | 0.5233 | 80   | 1.2024          |
| 1.1078        | 0.5495 | 84   | 1.1941          |
| 1.1993        | 0.5756 | 88   | 1.2111          |
| 1.2313        | 0.6018 | 92   | 1.1870          |
| 1.2431        | 0.6280 | 96   | 1.2047          |
| 1.1563        | 0.6541 | 100  | 1.1774          |
| 1.169         | 0.6803 | 104  | 1.2005          |
| 1.1873        | 0.7065 | 108  | 1.1957          |
| 1.0478        | 0.7326 | 112  | 1.1760          |
| 1.1245        | 0.7588 | 116  | 1.1628          |
| 1.1261        | 0.7850 | 120  | 1.1827          |
| 1.1876        | 0.8111 | 124  | 1.1869          |
| 1.1743        | 0.8373 | 128  | 1.1761          |
| 1.1865        | 0.8635 | 132  | 1.1744          |
| 1.1202        | 0.8896 | 136  | 1.1768          |
| 1.2158        | 0.9158 | 140  | 1.1790          |
| 1.0798        | 0.9419 | 144  | 1.1802          |
| 1.0996        | 0.9681 | 148  | 1.1814          |
| 1.2424        | 0.9943 | 152  | 1.1813          |


### Framework versions

- PEFT 0.7.1
- Transformers 4.40.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1