---
license: apache-2.0
---
# LimaRP Persona-Scenario Generator (v5, Alpaca)

A previously unpublished LoRA adapter for [Yarn-Llama-2-7B-64k](https://huggingface.co./NousResearch/Yarn-Llama-2-7b-64k) made
for internal use. Its primary purpose is generating Persona and Scenario (summary) from LimaRP `yaml` source data.
To some extent it can work with different text types, however.

## Prompt format
```
### Input:
{Your text here}

### Response:
Charactername's Persona: {output goes here}
```

Replace `Charactername` with the name of the character you want to infer a Persona for.
By default this LoRA looks for the placeholder names `<FIRST>` and `<SECOND>` (in this
respective order) but it can work with proper names as well.

## Example
This image shows what would happen (red box) after adding data in the format shown in the left pane.

![img](https://files.catbox.moe/rfwlpp.png)

In practice the results would be double-checked and manually tweaked to diversify the
outputs and adding character quirks, peculiarities or traits that the model couldn't catch.

## Known issues
- While the scenario/summary is often remarkably accurate, personas don't show a very high accuracy and can be repetitive.
- Persona and Scenario may exhibit `gpt`-isms.
- Peculiar character quirks may not be observed by the model.
- The LoRA hasn't been extensively tested with different input formats.
- There are apparently issues with the EOS token getting generated too early. It's suggested to disable it.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.00025
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 8
- total_eval_batch_size: 8
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 1.992         | 0.06  | 15   | 1.8884          |
| 1.8026        | 0.12  | 30   | 1.8655          |
| 1.7713        | 0.19  | 45   | 1.8539          |
| 1.7145        | 0.25  | 60   | 1.8502          |
| 1.6686        | 0.31  | 75   | 1.8507          |
| 1.8409        | 0.37  | 90   | 1.8469          |
| 1.7741        | 0.44  | 105  | 1.8434          |
| 1.7384        | 0.5   | 120  | 1.8407          |
| 1.7562        | 0.56  | 135  | 1.8390          |
| 1.7392        | 0.62  | 150  | 1.8373          |
| 1.8735        | 0.68  | 165  | 1.8381          |
| 1.8406        | 0.75  | 180  | 1.8377          |
| 1.6602        | 0.81  | 195  | 1.8350          |
| 1.7803        | 0.87  | 210  | 1.8341          |
| 1.7212        | 0.93  | 225  | 1.8329          |
| 1.8126        | 1.0   | 240  | 1.8330          |
| 1.8776        | 1.06  | 255  | 1.8314          |
| 1.7892        | 1.12  | 270  | 1.8328          |
| 1.7029        | 1.18  | 285  | 1.8338          |
| 1.7094        | 1.24  | 300  | 1.8322          |
| 1.7921        | 1.31  | 315  | 1.8310          |
| 1.8309        | 1.37  | 330  | 1.8316          |
| 1.7373        | 1.43  | 345  | 1.8309          |
| 1.7873        | 1.49  | 360  | 1.8313          |
| 1.7151        | 1.56  | 375  | 1.8306          |
| 1.7529        | 1.62  | 390  | 1.8300          |
| 1.7516        | 1.68  | 405  | 1.8293          |
| 1.7704        | 1.74  | 420  | 1.8294          |
| 1.6351        | 1.8   | 435  | 1.8290          |
| 1.6186        | 1.87  | 450  | 1.8291          |
| 1.7086        | 1.93  | 465  | 1.8295          |
| 1.6595        | 1.99  | 480  | 1.8290          |


### Framework versions

- Transformers 4.34.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.0