Instructions to use MuseMesh/mume-math-125m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MuseMesh/mume-math-125m with Transformers:

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

pipe = pipeline("text-generation", model="MuseMesh/mume-math-125m", trust_remote_code=True)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("MuseMesh/mume-math-125m", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use MuseMesh/mume-math-125m with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MuseMesh/mume-math-125m"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MuseMesh/mume-math-125m",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/MuseMesh/mume-math-125m

SGLang

How to use MuseMesh/mume-math-125m 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 "MuseMesh/mume-math-125m" \
    --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": "MuseMesh/mume-math-125m",
		"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 "MuseMesh/mume-math-125m" \
        --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": "MuseMesh/mume-math-125m",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use MuseMesh/mume-math-125m with Docker Model Runner:
```
docker model run hf.co/MuseMesh/mume-math-125m
```

mume-math-125m / eval /heldout_meta.json

ks2812

v0.1.0: MATH0 (owm_val_clean 1.06595, GSM8K 0.91%)

6307c7e verified 20 days ago

Raw History Blame Contribute Delete

610 Bytes

{
 "created": "2026-09-28 11:14",
 "sets": {
  "owm_val": {
   "records": 2704,
   "bytes_utf8": 20001168,
   "source": "open-web-math/open-web-math train-00113-of-00114",
   "note": "first 2704 documents in file order up to 20,000,000 bytes"
  },
  "gsm8k_test": {
   "records": 1319,
   "bytes_utf8": 652230,
   "source": "openai/gsm8k main test",
   "note": "question + blank line + answer; <<calculator>> annotations removed"
  },
  "math_test": {
   "records": 5000,
   "bytes_utf8": 3565468,
   "source": "EleutherAI/hendrycks_math test, 7 subjects",
   "note": "problem + blank line + solution"
  }
 }
}

	{
	"created": "2026-09-28 11:14",
	"sets": {
	"owm_val": {
	"records": 2704,
	"bytes_utf8": 20001168,
	"source": "open-web-math/open-web-math train-00113-of-00114",
	"note": "first 2704 documents in file order up to 20,000,000 bytes"
	},
	"gsm8k_test": {
	"records": 1319,
	"bytes_utf8": 652230,
	"source": "openai/gsm8k main test",
	"note": "question + blank line + answer; <<calculator>> annotations removed"
	},
	"math_test": {
	"records": 5000,
	"bytes_utf8": 3565468,
	"source": "EleutherAI/hendrycks_math test, 7 subjects",
	"note": "problem + blank line + solution"
	}
	}
	}