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[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
# mid-nids

This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0342

## 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: 0.0002
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- total_eval_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.0682        | 0.03  | 20   | 0.0982          |
| 0.0895        | 0.06  | 40   | 0.0792          |
| 0.015         | 0.09  | 60   | 0.0405          |
| 0.0376        | 0.11  | 80   | 0.0357          |
| 0.0196        | 0.14  | 100  | 0.0342          |
| 0.0219        | 0.17  | 120  | 0.0334          |
| 0.0188        | 0.2   | 140  | 0.0317          |
| 0.0147        | 0.23  | 160  | 0.0365          |
| 0.0224        | 0.26  | 180  | 0.0388          |
| 0.0116        | 0.28  | 200  | 0.0504          |
| 0.0158        | 0.31  | 220  | 0.0692          |
| 0.0193        | 0.34  | 240  | 0.0407          |
| 0.0181        | 0.37  | 260  | 0.0443          |
| 0.0124        | 0.4   | 280  | 0.0482          |
| 0.0094        | 0.43  | 300  | 0.0549          |
| 0.0081        | 0.46  | 320  | 0.0341          |
| 0.0188        | 0.48  | 340  | 0.0401          |
| 0.021         | 0.51  | 360  | 0.0508          |
| 0.0125        | 0.54  | 380  | 0.0409          |
| 0.0071        | 0.57  | 400  | 0.0424          |
| 0.0165        | 0.6   | 420  | 0.0566          |
| 0.0075        | 0.63  | 440  | 0.0537          |
| 0.0096        | 0.65  | 460  | 0.0338          |
| 0.012         | 0.68  | 480  | 0.0489          |
| 0.0041        | 0.71  | 500  | 0.0442          |
| 0.0012        | 0.74  | 520  | 0.0439          |
| 0.0096        | 0.77  | 540  | 0.0381          |
| 0.005         | 0.8   | 560  | 0.0449          |
| 0.0239        | 0.83  | 580  | 0.0452          |
| 0.0166        | 0.85  | 600  | 0.0383          |
| 0.0081        | 0.88  | 620  | 0.0249          |
| 0.0166        | 0.91  | 640  | 0.0442          |
| 0.0106        | 0.94  | 660  | 0.0327          |
| 0.0161        | 0.97  | 680  | 0.0386          |
| 0.0038        | 1.0   | 700  | 0.0377          |
| 0.0029        | 1.02  | 720  | 0.0367          |
| 0.0164        | 1.05  | 740  | 0.0276          |
| 0.0128        | 1.08  | 760  | 0.0259          |
| 0.0108        | 1.11  | 780  | 0.0294          |
| 0.026         | 1.14  | 800  | 0.0285          |
| 0.0104        | 1.17  | 820  | 0.0297          |
| 0.0102        | 1.19  | 840  | 0.0271          |
| 0.0111        | 1.22  | 860  | 0.0293          |
| 0.0088        | 1.25  | 880  | 0.0305          |
| 0.0116        | 1.28  | 900  | 0.0250          |
| 0.0066        | 1.31  | 920  | 0.0442          |
| 0.0061        | 1.34  | 940  | 0.0309          |
| 0.0173        | 1.37  | 960  | 0.0231          |
| 0.0032        | 1.39  | 980  | 0.0230          |
| 0.0119        | 1.42  | 1000 | 0.0401          |
| 0.0083        | 1.45  | 1020 | 0.0274          |
| 0.0047        | 1.48  | 1040 | 0.0359          |
| 0.0221        | 1.51  | 1060 | 0.0301          |
| 0.0038        | 1.54  | 1080 | 0.0280          |
| 0.0052        | 1.56  | 1100 | 0.0235          |
| 0.0084        | 1.59  | 1120 | 0.0323          |
| 0.012         | 1.62  | 1140 | 0.0320          |
| 0.0019        | 1.65  | 1160 | 0.0256          |
| 0.0175        | 1.68  | 1180 | 0.0300          |
| 0.0078        | 1.71  | 1200 | 0.0362          |
| 0.0088        | 1.74  | 1220 | 0.0310          |
| 0.0065        | 1.76  | 1240 | 0.0301          |
| 0.0059        | 1.79  | 1260 | 0.0348          |
| 0.0066        | 1.82  | 1280 | 0.0341          |
| 0.0015        | 1.85  | 1300 | 0.0280          |
| 0.0091        | 1.88  | 1320 | 0.0266          |
| 0.0053        | 1.91  | 1340 | 0.0350          |
| 0.0077        | 1.93  | 1360 | 0.0333          |
| 0.0081        | 1.96  | 1380 | 0.0320          |
| 0.0129        | 1.99  | 1400 | 0.0391          |
| 0.0082        | 2.02  | 1420 | 0.0388          |
| 0.008         | 2.05  | 1440 | 0.0212          |
| 0.0025        | 2.08  | 1460 | 0.0362          |
| 0.0006        | 2.11  | 1480 | 0.0289          |
| 0.0034        | 2.13  | 1500 | 0.0347          |
| 0.0115        | 2.16  | 1520 | 0.0313          |
| 0.0061        | 2.19  | 1540 | 0.0297          |
| 0.0065        | 2.22  | 1560 | 0.0335          |
| 0.0144        | 2.25  | 1580 | 0.0379          |
| 0.0075        | 2.28  | 1600 | 0.0300          |
| 0.0093        | 2.3   | 1620 | 0.0322          |
| 0.0091        | 2.33  | 1640 | 0.0313          |
| 0.0051        | 2.36  | 1660 | 0.0278          |
| 0.0046        | 2.39  | 1680 | 0.0294          |
| 0.0004        | 2.42  | 1700 | 0.0283          |
| 0.0054        | 2.45  | 1720 | 0.0296          |
| 0.0034        | 2.48  | 1740 | 0.0337          |
| 0.0065        | 2.5   | 1760 | 0.0341          |
| 0.0034        | 2.53  | 1780 | 0.0345          |
| 0.0114        | 2.56  | 1800 | 0.0371          |
| 0.0044        | 2.59  | 1820 | 0.0377          |
| 0.0086        | 2.62  | 1840 | 0.0344          |
| 0.0065        | 2.65  | 1860 | 0.0332          |
| 0.0051        | 2.67  | 1880 | 0.0344          |
| 0.008         | 2.7   | 1900 | 0.0355          |
| 0.0035        | 2.73  | 1920 | 0.0351          |
| 0.0065        | 2.76  | 1940 | 0.0352          |
| 0.0097        | 2.79  | 1960 | 0.0347          |
| 0.0034        | 2.82  | 1980 | 0.0347          |
| 0.0054        | 2.84  | 2000 | 0.0348          |
| 0.0045        | 2.87  | 2020 | 0.0344          |
| 0.0032        | 2.9   | 2040 | 0.0343          |
| 0.0072        | 2.93  | 2060 | 0.0342          |
| 0.0074        | 2.96  | 2080 | 0.0344          |
| 0.0111        | 2.99  | 2100 | 0.0342          |


### Framework versions

- Transformers 4.34.1
- Pytorch 2.0.1+cu117
- Datasets 2.14.6
- Tokenizers 0.14.1

{"tags": ["generated_from_trainer"], "base_model": "meta-llama/Llama-2-7b-hf", "model-index": [{"name": "mid-nids", "results": []}]}
transformers tensorboard llama text-generation generated_from_trainer base_model:meta-llama/Llama-2-7b-hf autotrain_compatible endpoints_compatible text-generation-inference 8-bit region:us