---
license: mit
tags:
- osu
- music-generation
- beatmap-generation
- lora
base_model:
- OliBomby/Mapperatorinator-v30
---
# LoRA: Aim Control Style for Mapperatorinator

This is a LoRA (Low-Rank Adaptation) fine-tune for the [OliBomby/Mapperatorinator-v30](https://huggingface.co/OliBomby/Mapperatorinator-v30) model. It has been trained to generate beatmaps with aim control style.

<details>
<summary>AB Test Note (Click to expand)</summary>

If you came from [mjoink/Mapperatorinator-v30-LoRA-AB-test](https://huggingface.co/mjoink/Mapperatorinator-v30-LoRA-AB-test), this is the model I consider "better". You can verify by checking the hash:

**SHA256 of `adapter_model.safetensors`:**
```
7c3f73af005dd2b20eb491bd227f4933550f2b7602b5d4e66f4601ee377f9c45
```

</details>

## Model Details
- **Base Model:** `OliBomby/Mapperatorinator-v30`
- **Model Type:** LoRA

## How to Use

You can load the LoRA directly from this repository without manually downloading the files. The script will handle it automatically.

1.  **Run Inference:** Use the `lora_path` argument and set it to the name of this repository.
    ```bash
    python inference.py \
      audio_path='/path/to/your/audio.mp3' \
      output_path='/path/to/your/output_folder' \
      lora_path='mjoink/Mapperatorinator-v30-LoRA-aim-control' \
      ... # other arguments
    ```



## Training Parameters

This LoRA was trained using the following parameters. Testing has shown that these settings successfully achieve above 99 on all `test/*_acc` metrics, which is why they are documented here. The configuration inheritance chain is: `default.yaml` -> `v30.yaml` -> `lora.yaml` -> `this.yaml`.

```yaml
defaults:
  - lora
  - _self_

compile: false

data:
  dataset_type: "mmrs"
  train_dataset_path: ""
  test_dataset_path: ""
  train_dataset_start: 0
  train_dataset_end: 40
  test_dataset_start: 40
  test_dataset_end: 45

optim:
  name: muon
  base_lr: 0.003
  base_lr_2: 0.00075
  batch_size: 8
  grad_acc: 1
  total_steps: 7000
  warmup_steps: 100

eval:
  every_steps: 1000
  steps: 500
```

Based on this configuration, you can extrapolate the training parameters needed to achieve the 99 accuracy metric for a dataset of around 200 beatmaps.

Please note that only 40 beatmaps were used for training in this run, rather than 45. When dealing with such an extremely small dataset, it is generally recommended to use all available data for training instead of splitting it.