Instructions to use DancingIguana/music-generation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DancingIguana/music-generation with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="DancingIguana/music-generation")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DancingIguana/music-generation")
model = AutoModelForCausalLM.from_pretrained("DancingIguana/music-generation")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use DancingIguana/music-generation with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DancingIguana/music-generation"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DancingIguana/music-generation",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/DancingIguana/music-generation

SGLang

How to use DancingIguana/music-generation with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "DancingIguana/music-generation" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DancingIguana/music-generation",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "DancingIguana/music-generation" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DancingIguana/music-generation",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use DancingIguana/music-generation with Docker Model Runner:
```
docker model run hf.co/DancingIguana/music-generation
```

music-generation

This model a trained from scratch version of distilgpt2 on a dataset where the text represents musical notes. The dataset consists of one stream of notes from MIDI files (the stream with most notes), where all of the melodies were transposed either to C major or A minor. Also, the BPM of the song is ignored, the duration of each note is based on its quarter length.

Each element in the melody is represented by a series of letters and numbers with the following structure.

For a note: ns[pitch of the note as a string]s[duration]
- Examples: nsC4s0p25, nsF7s1p0,
For a rest: rs[duration]:
- Examples: rs0p5, rs1q6
For a chord: cs[number of notes in chord]s[pitches of chords separated by "s"]s[duration]
- Examples: cs2sE7sF7s1q3, cs2sG3sGw3s0p25

The following special symbols are replaced in the strings by the following:

. = p
/ = q
=
- = t

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: 0.0005
train_batch_size: 32
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 256
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 1000
num_epochs: 100
mixed_precision_training: Native AMP

DancingIguana
/

music-generation

music-generation

=

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Spaces using DancingIguana/music-generation 2