Optimal Sparsity Code
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Optimal Sparsity of Mixture-of-Experts Language Models for Reasoning Tasks • 65 items • Updated • 1
How to use llm-jp/optimal-sparsity-code-d2048-E128-k8-52.2B-A3.9B with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="llm-jp/optimal-sparsity-code-d2048-E128-k8-52.2B-A3.9B") # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("llm-jp/optimal-sparsity-code-d2048-E128-k8-52.2B-A3.9B")
model = AutoModelForCausalLM.from_pretrained("llm-jp/optimal-sparsity-code-d2048-E128-k8-52.2B-A3.9B")How to use llm-jp/optimal-sparsity-code-d2048-E128-k8-52.2B-A3.9B with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "llm-jp/optimal-sparsity-code-d2048-E128-k8-52.2B-A3.9B"
# 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-code-d2048-E128-k8-52.2B-A3.9B",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/llm-jp/optimal-sparsity-code-d2048-E128-k8-52.2B-A3.9B
How to use llm-jp/optimal-sparsity-code-d2048-E128-k8-52.2B-A3.9B with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "llm-jp/optimal-sparsity-code-d2048-E128-k8-52.2B-A3.9B" \
--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-code-d2048-E128-k8-52.2B-A3.9B",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'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-code-d2048-E128-k8-52.2B-A3.9B" \
--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-code-d2048-E128-k8-52.2B-A3.9B",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'How to use llm-jp/optimal-sparsity-code-d2048-E128-k8-52.2B-A3.9B with Docker Model Runner:
docker model run hf.co/llm-jp/optimal-sparsity-code-d2048-E128-k8-52.2B-A3.9B
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
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}
}