Instructions to use Shinegupta/ShineMath with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Shinegupta/ShineMath with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("AI-MO/NuminaMath-7B-TIR")
model = PeftModel.from_pretrained(base_model, "Shinegupta/ShineMath")

Transformers

How to use Shinegupta/ShineMath with Transformers:

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

pipe = pipeline("text-generation", model="Shinegupta/ShineMath")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Shinegupta/ShineMath", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Shinegupta/ShineMath with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Shinegupta/ShineMath

SGLang

How to use Shinegupta/ShineMath 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 "Shinegupta/ShineMath" \
    --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": "Shinegupta/ShineMath",
		"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 "Shinegupta/ShineMath" \
        --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": "Shinegupta/ShineMath",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Shinegupta/ShineMath with Docker Model Runner:
```
docker model run hf.co/Shinegupta/ShineMath
```

Shinegupta commited on Feb 24

Commit

3b1cdbc

verified ·

1 Parent(s): fa440ed

Update README.md

Browse files

Files changed (1) hide show

README.md +4 -7

README.md CHANGED Viewed

@@ -84,12 +84,9 @@ See the LICENSE file or specify here (e.g., Apache-2.0 for open use).
 ### Citation
 If you use ShineMath in research or projects, please cite:
-@misc{shinegupta2026_shinemath,
-  author = {Shine Gupta},
-  title = {ShineMath: Mathematical Olympiad Language Model},
-  year = {2026},
-  publisher = {Hugging Face},
-  howpublished = {\url{https://huggingface.co/Shinegupta/ShineMath}}
-}
 For questions, collaborations, or issues — open a discussion on the model page! Happy math solving!

 ### Citation
 If you use ShineMath in research or projects, please cite:
+  author = Shine Gupta,
+  title = ShineMath: Mathematical Olympiad Language Model,
+  publisher = Hugging Face,
+  howpublished = https://huggingface.co/Shinegupta/ShineMath
 For questions, collaborations, or issues — open a discussion on the model page! Happy math solving!