---
tags:
- generated_from_trainer
model-index:
- name: out
  results: []
language:
- en
---

Hesperus-v1 - A trained 8-bit LoRA for RP & General Purposes.
<br>Trained on the base 13B Llama 2 model.

Dataset Entry Rows:
<br>RP: 8.95K
<br>MED: 10.5K
<br>General: 8.7K
<br>Total: 28.15K

This is after heavy filtering of >300K Rows and Entries.
<br> V2 will see this further reduced down to 14K after I do a second round of cleaning.

***

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should probably proofread and complete it, then remove this comment. -->

[<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)
# out

This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5134

## 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: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 16
- total_train_batch_size: 256
- 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: 10
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.5513        | 0.05  | 1    | 1.6200          |
| 1.5555        | 0.11  | 2    | 1.6200          |
| 1.5558        | 0.22  | 4    | 1.6180          |
| 1.5195        | 0.33  | 6    | 1.6109          |
| 1.5358        | 0.44  | 8    | 1.5929          |
| 1.5124        | 0.55  | 10   | 1.5740          |
| 1.4938        | 0.66  | 12   | 1.5591          |
| 1.4881        | 0.77  | 14   | 1.5495          |
| 1.4639        | 0.88  | 16   | 1.5427          |
| 1.4824        | 0.99  | 18   | 1.5373          |
| 1.4752        | 1.1   | 20   | 1.5318          |
| 1.4768        | 1.21  | 22   | 1.5278          |
| 1.4482        | 1.32  | 24   | 1.5236          |
| 1.4444        | 1.42  | 26   | 1.5209          |
| 1.4381        | 1.53  | 28   | 1.5192          |
| 1.4415        | 1.64  | 30   | 1.5166          |
| 1.4412        | 1.75  | 32   | 1.5150          |
| 1.4263        | 1.86  | 34   | 1.5146          |
| 1.4608        | 1.97  | 36   | 1.5134          |


### Framework versions

- Transformers 4.34.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1