Instructions to use haiyewon/Decomposer-Qwen3-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use haiyewon/Decomposer-Qwen3-8B with Transformers:

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

pipe = pipeline("text-generation", model="haiyewon/Decomposer-Qwen3-8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("haiyewon/Decomposer-Qwen3-8B")
model = AutoModelForCausalLM.from_pretrained("haiyewon/Decomposer-Qwen3-8B", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use haiyewon/Decomposer-Qwen3-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "haiyewon/Decomposer-Qwen3-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "haiyewon/Decomposer-Qwen3-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/haiyewon/Decomposer-Qwen3-8B

SGLang

How to use haiyewon/Decomposer-Qwen3-8B 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 "haiyewon/Decomposer-Qwen3-8B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "haiyewon/Decomposer-Qwen3-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "haiyewon/Decomposer-Qwen3-8B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "haiyewon/Decomposer-Qwen3-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use haiyewon/Decomposer-Qwen3-8B with Docker Model Runner:
```
docker model run hf.co/haiyewon/Decomposer-Qwen3-8B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Decomposer

Decomposer decompiles symbolic music (MIDI) into executable Strudel programs. This model is the 8B model from the paper Decomposer: Learning to Decompile Symbolic Music to Programs, post-trained from Qwen3-8B.

📄 Paper: arXiv:2607.01849
🌐 Project page: yewon-kim.com/decomposer
🎹 Live demo: haiyewon/decomposer-demo
💻 Code: github.com/elianakim/Decomposer
🤗 Dataset: haiyewon/Strudel-Synth

Model Description

Base model: Qwen/Qwen3-8B
Model size: 8.2B
Type: Causal Language Model
Precision: bfloat16

Quick Start

Clone our GitHub code repo, run through the setup steps, and try:

git clone https://github.com/elianakim/Decomposer.git
cd Decomposer
uv sync

uv run generate_transformers.py \
    --model haiyewon/Decomposer-Qwen3-8B \
    --midi_path path/to/input.mid \
    --n_outputs 5

The repo and inference scripts provide a more complete usage guide.

Model Details

Input Format

MIDI is serialized as instrument-wise note events. Onsets are given in cycle coordinates instead of seconds:

[BPM=120 Meter=4]
[acoustic_grand_piano] C4@0.00 E4@0.25 G4@0.50 C5@1.00
[drum] bass_drum@0.00 closed_hi_hat@0.25 closed_hi_hat@0.50 snare@0.75

Header [BPM=… Meter=…] — tempo/meter; if absent, madmom is used to estimate them.
Events — one line per instrument, pitch@cycle_onset tokens; melodic pitches as note names (C4, F#3), drums as GM drum names (bass_drum, closed_hi_hat).

Training Data

Strudel-Synth is a synthetic corpus of 21,174 (MIDI, Strudel) pairs (20,152 train / 1,022 test) built by distilling Strudel programs from Claude-Opus-4.6 and rendering each to MIDI with the Strudel runtime. Its ~20K training split is divided into two disjoint ~10K halves, one per stage:

SFT uses the first half as paired (MIDI, Strudel) examples.
RL uses the second half (MIDI only), plus short-fragment (<30 s) LMD MIDI.

Inference Hyperparameters

Recommended settings matching the paper evaluation:

Setting	Value
`temperature`	1.0
`max_new_tokens`	4096
`enable_thinking`	`false`

Citation

If you find our model useful, please cite our research as

@article{kim2026decomposer,
  title   = {Decomposer: Learning to Decompile Symbolic Music to Programs},
  author  = {Kim, Yewon and Gandhi, Apurva and Chung, David and Neubig, Graham and Donahue, Chris},
  journal = {arXiv preprint arXiv:2607.01849},
  year    = {2026}
}