Overview

This music generation model provides a lightweight, MIT-licensed solution for developers needing programmatic audio synthesis. Unlike closed-source APIs, its permissive licensing makes it ideal for integrating directly into gaming engines, app backgrounds, or generative art pipelines without restrictive royalty concerns. The model focuses on converting structured inputs into audio waveforms, allowing devs to automate soundtrack creation or build interactive soundscapes. While it may not match the fidelity of massive proprietary models, its efficiency and open nature make it a strong candidate for edge deployment or rapid prototyping where low latency and cost-effectiveness are prioritized over studio-grade production.

Highlights

Permissive MIT license for commercial application
Optimized for programmatic audio synthesis
Ideal for game development and app backgrounds
Low-latency integration for real-time projects
Flexible deployment across diverse hardware environments

Usage

Install

# Install Hugging Face transformers
pip install transformers torch

SDK Usage

# Load model with transformers
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("metoonhathung/music-generation")
tokenizer = AutoTokenizer.from_pretrained("metoonhathung/music-generation")

Hugging Face Download

We recommend downloading the model via the Hugging Face CLI or Hub SDK.

Guidance：Before downloading, install huggingface_hub with:

Guidance

pip install -U huggingface_hub

CLI Download

Download the full repository

huggingface-cli download metoonhathung/music-generation

Download a single file to a local folder (e.g. config.json into ./dir)

huggingface-cli download metoonhathung/music-generation config.json --local-dir ./dir

See the official docs for more CLI options

SDK Download

# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('metoonhathung/music-generation')

Git Download

Make sure git-lfs is installed first

Git Download

git lfs install
git clone https://huggingface.co/metoonhathung/music-generation

To skip LFS large-file downloads, use:

Skip LFS

GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/metoonhathung/music-generation

Model files are hosted on the Hugging Face Hub — download directly via HF CLI / SDK / Git, not through this site.

PyTorch / Transformers Usage

Install Transformers

pip install -U transformers torch

Load the model and run inference

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('metoonhathung/music-generation')
tokenizer = AutoTokenizer.from_pretrained('metoonhathung/music-generation')

Full Documentation

来源: HuggingFace

---
library_name: transformers
license: mit
base_model: gpt2
tags:

generated_from_trainer

model-index:

name: music-generation

results: []
---

music-generation

This model is a fine-tuned version of gpt2 on an unknown dataset.
It achieves the following results on the evaluation set:

Loss: 0.5312

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: 0.0001

train_batch_size: 16

eval_batch_size: 16

seed: 42

gradient_accumulation_steps: 16

total_train_batch_size: 256

optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments

lr_scheduler_type: cosine

lr_scheduler_warmup_steps: 100

num_epochs: 20

mixed_precision_training: Native AMP

Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-------:|:----:|:---------------:|
| 3.7238 | 0.9217 | 100 | 2.8460 |
| 2.4643 | 1.8387 | 200 | 1.8829 |
| 1.8339 | 2.7558 | 300 | 1.4234 |
| 1.5013 | 3.6728 | 400 | 1.2203 |
| 1.3125 | 4.5899 | 500 | 1.0966 |
| 1.1899 | 5.5069 | 600 | 1.0028 |
| 1.0982 | 6.4240 | 700 | 0.9353 |
| 1.0302 | 7.3410 | 800 | 0.8779 |
| 0.9766 | 8.2581 | 900 | 0.8276 |
| 0.9243 | 9.1751 | 1000 | 0.7757 |
| 0.8825 | 10.0922 | 1100 | 0.7345 |
| 0.845 | 11.0092 | 1200 | 0.7000 |
| 0.8083 | 11.9309 | 1300 | 0.6624 |
| 0.7784 | 12.8479 | 1400 | 0.6328 |
| 0.7502 | 13.7650 | 1500 | 0.6052 |
| 0.7281 | 14.6820 | 1600 | 0.5816 |
| 0.7072 | 15.5991 | 1700 | 0.5622 |
| 0.6903 | 16.5161 | 1800 | 0.5486 |
| 0.6796 | 17.4332 | 1900 | 0.5386 |
| 0.6705 | 18.3502 | 2000 | 0.5335 |
| 0.6646 | 19.2673 | 2100 | 0.5312 |

Framework versions

Transformers 4.55.0

Pytorch 2.6.0+cu124

Datasets 4.0.0

Tokenizers 0.21.4