Instructions to use llm-jp/optimal-sparsity-math-d1024-E32-k16-3.5B-A1.9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use llm-jp/optimal-sparsity-math-d1024-E32-k16-3.5B-A1.9B with Transformers:

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

pipe = pipeline("text-generation", model="llm-jp/optimal-sparsity-math-d1024-E32-k16-3.5B-A1.9B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("llm-jp/optimal-sparsity-math-d1024-E32-k16-3.5B-A1.9B")
model = AutoModelForCausalLM.from_pretrained("llm-jp/optimal-sparsity-math-d1024-E32-k16-3.5B-A1.9B", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use llm-jp/optimal-sparsity-math-d1024-E32-k16-3.5B-A1.9B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "llm-jp/optimal-sparsity-math-d1024-E32-k16-3.5B-A1.9B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "llm-jp/optimal-sparsity-math-d1024-E32-k16-3.5B-A1.9B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/llm-jp/optimal-sparsity-math-d1024-E32-k16-3.5B-A1.9B

SGLang

How to use llm-jp/optimal-sparsity-math-d1024-E32-k16-3.5B-A1.9B 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 "llm-jp/optimal-sparsity-math-d1024-E32-k16-3.5B-A1.9B" \
    --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": "llm-jp/optimal-sparsity-math-d1024-E32-k16-3.5B-A1.9B",
		"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 "llm-jp/optimal-sparsity-math-d1024-E32-k16-3.5B-A1.9B" \
        --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": "llm-jp/optimal-sparsity-math-d1024-E32-k16-3.5B-A1.9B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use llm-jp/optimal-sparsity-math-d1024-E32-k16-3.5B-A1.9B with Docker Model Runner:
```
docker model run hf.co/llm-jp/optimal-sparsity-math-d1024-E32-k16-3.5B-A1.9B
```

optimal-sparsity-math-d1024-E32-k16-3.5B-A1.9B / README.md

Taishi-N324

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metadata

pipeline_tag: text-generation
library_name: transformers
license: apache-2.0
tags:
  - mixtral
  - moe
  - reasoning

Optimal Sparsity of Mixture-of-Experts Language Models for Reasoning Tasks

This repository contains model checkpoints from the paper Optimal Sparsity of Mixture-of-Experts Language Models for Reasoning Tasks.

For more details, including code and evaluation procedures, please refer to the official GitHub repository: https://github.com/rioyokotalab/optimal-sparsity

How to cite

If you find our work helpful, please feel free to cite the paper.

@inproceedings{
    nakamura2026optimal,
    title={Optimal Sparsity of Mixture-of-Experts Language Models for Reasoning Tasks},
    author={Taishi Nakamura and Satoki Ishikawa and Masaki Kawamura and Takumi Okamoto and Daisuke Nohara and Jun Suzuki and Rio Yokota},
    booktitle={The Fourteenth International Conference on Learning Representations},
    year={2026},
    url={https://openreview.net/forum?id=XFw2EPRUUR}
}