Instructions to use llm-jp/optimal-sparsity-math-d1024-E32-k8-3.5B-A1.1B 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-k8-3.5B-A1.1B 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-k8-3.5B-A1.1B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("llm-jp/optimal-sparsity-math-d1024-E32-k8-3.5B-A1.1B")
model = AutoModelForCausalLM.from_pretrained("llm-jp/optimal-sparsity-math-d1024-E32-k8-3.5B-A1.1B", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use llm-jp/optimal-sparsity-math-d1024-E32-k8-3.5B-A1.1B 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-k8-3.5B-A1.1B"
# 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-k8-3.5B-A1.1B",
		"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-k8-3.5B-A1.1B

SGLang

How to use llm-jp/optimal-sparsity-math-d1024-E32-k8-3.5B-A1.1B 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-k8-3.5B-A1.1B" \
    --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-k8-3.5B-A1.1B",
		"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-k8-3.5B-A1.1B" \
        --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-k8-3.5B-A1.1B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use llm-jp/optimal-sparsity-math-d1024-E32-k8-3.5B-A1.1B with Docker Model Runner:
```
docker model run hf.co/llm-jp/optimal-sparsity-math-d1024-E32-k8-3.5B-A1.1B
```

Improve model card: Add metadata, paper details, and links

by nielsr HF Staff - opened Aug 31, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+21

-1

nielsr

Aug 31, 2025

This PR significantly enhances the model card by adding essential metadata and detailed information.

Key improvements include:

Setting library_name: transformers to ensure proper integration and enable the "how to use" widget.
Adding pipeline_tag: text-generation for better discoverability on the Hugging Face Hub.
Specifying license: apache-2.0 for clarity regarding usage rights.
Including relevant tags such as mixtral, moe, reasoning, and llm to improve searchability.
Providing a direct link to the paper on Hugging Face Papers: Optimal Sparsity of Mixture-of-Experts Language Models for Reasoning Tasks.
Adding a link to the associated GitHub repository for the code: https://github.com/rioyokotalab/optimal-sparsity.
Incorporating the paper's abstract to provide immediate context about the model and research findings.

Please review and merge if these improvements align with the repository's goals.

Improve model card: Add metadata, paper details, and links34ef0939

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