Instructions to use ryota-komatsu/SylReg-LM-7B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ryota-komatsu/SylReg-LM-7B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="ryota-komatsu/SylReg-LM-7B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ryota-komatsu/SylReg-LM-7B-Instruct")
model = AutoModelForCausalLM.from_pretrained("ryota-komatsu/SylReg-LM-7B-Instruct")
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 ryota-komatsu/SylReg-LM-7B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ryota-komatsu/SylReg-LM-7B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ryota-komatsu/SylReg-LM-7B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ryota-komatsu/SylReg-LM-7B-Instruct

SGLang

How to use ryota-komatsu/SylReg-LM-7B-Instruct 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 "ryota-komatsu/SylReg-LM-7B-Instruct" \
    --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": "ryota-komatsu/SylReg-LM-7B-Instruct",
		"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 "ryota-komatsu/SylReg-LM-7B-Instruct" \
        --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": "ryota-komatsu/SylReg-LM-7B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ryota-komatsu/SylReg-LM-7B-Instruct with Docker Model Runner:
```
docker model run hf.co/ryota-komatsu/SylReg-LM-7B-Instruct
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

SylReg-LM 7B Instruct

Model Details

Model Description

Model type: Qwen2ForCausalLM
Language(s) (NLP): English
License: CC BY-NC-SA 4.0
Finetuned from model: ryota-komatsu/SylReg-LM-7B

Model Sources

Repository: Code
Paper: arXiv:2607.04064
Demo: Project page

How to Get Started with the Model

Use the code below to get started with the model.

git clone https://github.com/ryota-komatsu/speaker_disentangled_hubert.git
cd speaker_disentangled_hubert

sudo apt install git-lfs  # for UTMOS

conda create -y -n py310 -c pytorch -c nvidia -c conda-forge python=3.10 pip=24.0 setuptools=81.0.0 faiss-gpu=1.13.2
conda activate py310
pip install -r requirements/requirements.txt

sh scripts/setup.sh

import re

import torch
import torchaudio
from transformers import AutoModelForCausalLM, AutoTokenizer

from src.flow_matching import FlowMatchingWithBigVGan
from src.s5hubert import SylRegForSyllableDiscovery

wav_path = "/path/to/wav"

# download pretrained models from hugging face hub
encoder = SylRegForSyllableDiscovery.from_pretrained("ryota-komatsu/SylReg-Distill", device_map="cuda", dtype="auto")
decoder = FlowMatchingWithBigVGan.from_pretrained("ryota-komatsu/SylReg-Decoder", device_map="cuda", dtype="auto")
speechlm = AutoModelForCausalLM.from_pretrained("ryota-komatsu/SylReg-LM-7B-Instruct", device_map="cuda", dtype="auto")
tokenizer = AutoTokenizer.from_pretrained("ryota-komatsu/SylReg-LM-7B-Instruct")

# load a waveform
waveform, sr = torchaudio.load(wav_path)
waveform = torchaudio.functional.resample(waveform, sr, 16000)

# encode a waveform into syllabic units
outputs = encoder(waveform.to(encoder.device))
units = outputs[0]["units"]  # [3950, 67, ..., 503]

# speech language modeling
messages = [
    {"role": "user", "content": "".join(f"<{unit}>" for unit in units)},
]

input_ids = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt",
).input_ids.to(speechlm.device)

generated_ids = speechlm.generate(input_ids=input_ids, do_sample=True, temperature=0.8)[0]

units = tokenizer.decode(generated_ids)
units = torch.tensor([int(unit) for unit in re.findall(r"<(\d+)>", units)], device=decoder.device)

# unit-to-speech synthesis
generated_speech = decoder(units.unsqueeze(0)).waveform.cpu()

Training Details

Training Data

	Hours	License	Provider
LibriSpeech	960	CC BY 4.0	V. Panayotov et al.
Libriheavy	50,978	public domain	W. Kang et al.
Emilia-Large	4,447	CC BY 4.0, CC BY-NC 4.0	H. He et al.
People's Speech (clean, clean_sa)	5,640	CC-BY, CC-BY-SA	D. Galvez et al.
VoxPopuli	543	CC0-1.0	C. Wang et al.
TinyStories	27,810	cdla-sharing-1.0	R. Eldan et al.
Cosmopedia-v2	38,986	odc-by	L. B. Allal et al.
Total	129,364

Training Hyperparameters

Training regime: bf16 mixed precision
Training steps: 15k
Batch size: 2,097,152 (=2²¹) tokens
Optimizer: AdamW(lr=0.0003, betas=(0.9, 0.95), weight_decay=0.01)
Scheduler: warmup_stable_decay(num_warmup_steps=100, num_decay_steps=5000, min_lr_ratio=0.1)

Hardware

32 NVIDIA H100 GPUs

Citation

BibTeX:

@article{Komatsu_SylReg_2026,
  author    = {Komatsu, Ryota and Kawakita, Kota and Okamoto, Takuma and Shinozaki, Takahiro},
  title     = {Speaker-Disentangled Chunk-Wise Regression for Syllabic Tokenization},
  year      = {2026},
  volume    = {7},
  journal   = {IEEE Open Journal of Signal Processing},
  pages     = {},
}