generated_from_trainer base_model:mistralai/Mistral-7B-v0.1 license:apache-2.0 region:us
---
license: apache-2.0
base_model: mistralai/Mistral-7B-v0.1
tags:
- generated_from_trainer
model-index:
- name: mistral_7B_KoFLAN_ver2_QLoRA
  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_KoFLAN_ver2_QLoRA

This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3025

## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 32
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 2.0

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.017         | 0.03  | 100  | 1.3341          |
| 1.7547        | 0.05  | 200  | 1.3018          |
| 1.5125        | 0.08  | 300  | 1.2914          |
| 1.4717        | 0.11  | 400  | 1.2833          |
| 1.4686        | 0.14  | 500  | 1.2790          |
| 1.4038        | 0.16  | 600  | 1.2751          |
| 1.4086        | 0.19  | 700  | 1.2803          |
| 1.4371        | 0.22  | 800  | 1.2843          |
| 21.1904       | 0.24  | 900  | 12.5749         |
| 30.0185       | 0.27  | 1000 | 5.5864          |
| 12.0201       | 0.3   | 1100 | 2.9353          |
| 5.9096        | 0.32  | 1200 | 2.1182          |
| 7.0684        | 0.35  | 1300 | 2.4859          |
| 6.7228        | 0.38  | 1400 | 2.2694          |
| 4.4835        | 0.41  | 1500 | 1.9221          |
| 5.2842        | 0.43  | 1600 | 1.9740          |
| 3.7658        | 0.46  | 1700 | 1.7739          |
| 3.5637        | 0.49  | 1800 | 1.7292          |
| 3.1788        | 0.51  | 1900 | 1.6524          |
| 2.8849        | 0.54  | 2000 | 1.5946          |
| 2.628         | 0.57  | 2100 | 1.5482          |
| 2.2247        | 0.59  | 2200 | 1.5133          |
| 2.1339        | 0.62  | 2300 | 1.4882          |
| 2.0574        | 0.65  | 2400 | 1.4656          |
| 1.9366        | 0.68  | 2500 | 1.4454          |
| 1.8554        | 0.7   | 2600 | 1.4273          |
| 1.7418        | 0.73  | 2700 | 1.4147          |
| 1.7082        | 0.76  | 2800 | 1.3997          |
| 1.6028        | 0.78  | 2900 | 1.3867          |
| 1.5355        | 0.81  | 3000 | 1.3747          |
| 1.485         | 0.84  | 3100 | 1.3644          |
| 1.4463        | 0.86  | 3200 | 1.3573          |
| 1.3714        | 0.89  | 3300 | 1.3526          |
| 1.4356        | 0.92  | 3400 | 1.3461          |
| 1.3017        | 0.95  | 3500 | 1.3405          |
| 1.4453        | 0.97  | 3600 | 1.3362          |
| 1.3736        | 1.0   | 3700 | 1.3326          |
| 1.2048        | 1.03  | 3800 | 1.3297          |
| 1.2779        | 1.05  | 3900 | 1.3274          |
| 1.3065        | 1.08  | 4000 | 1.3256          |
| 1.2698        | 1.11  | 4100 | 1.3229          |
| 1.237         | 1.13  | 4200 | 1.3215          |
| 1.1412        | 1.16  | 4300 | 1.3203          |
| 1.234         | 1.19  | 4400 | 1.3186          |
| 1.194         | 1.22  | 4500 | 1.3174          |
| 1.2338        | 1.24  | 4600 | 1.3149          |
| 1.2936        | 1.27  | 4700 | 1.3143          |
| 1.2571        | 1.3   | 4800 | 1.3135          |
| 1.2435        | 1.32  | 4900 | 1.3120          |
| 1.2681        | 1.35  | 5000 | 1.3107          |
| 1.2248        | 1.38  | 5100 | 1.3097          |
| 1.1332        | 1.4   | 5200 | 1.3088          |
| 1.1791        | 1.43  | 5300 | 1.3075          |
| 1.1646        | 1.46  | 5400 | 1.3069          |
| 1.2292        | 1.49  | 5500 | 1.3062          |
| 1.2554        | 1.51  | 5600 | 1.3054          |
| 1.1609        | 1.54  | 5700 | 1.3052          |
| 1.176         | 1.57  | 5800 | 1.3043          |
| 1.1948        | 1.59  | 5900 | 1.3042          |
| 1.1441        | 1.62  | 6000 | 1.3036          |
| 1.1674        | 1.65  | 6100 | 1.3035          |
| 1.1119        | 1.67  | 6200 | 1.3034          |
| 1.1469        | 1.7   | 6300 | 1.3032          |
| 1.1834        | 1.73  | 6400 | 1.3033          |
| 1.0901        | 1.76  | 6500 | 1.3031          |
| 1.1771        | 1.78  | 6600 | 1.3030          |
| 1.2767        | 1.81  | 6700 | 1.3028          |
| 1.1978        | 1.84  | 6800 | 1.3027          |
| 1.1495        | 1.86  | 6900 | 1.3026          |
| 1.1291        | 1.89  | 7000 | 1.3026          |
| 1.1783        | 1.92  | 7100 | 1.3026          |
| 1.2189        | 1.94  | 7200 | 1.3025          |
| 1.3167        | 1.97  | 7300 | 1.3025          |
| 1.1293        | 2.0   | 7400 | 1.3025          |


### Framework versions

- Transformers 4.34.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
