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 / tokenizer_config.json

ks2812

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

6307c7e verified 26 days ago

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424 Bytes

{
  "tokenizer_class": "PreTrainedTokenizerFast",
  "auto_map": {
    "AutoTokenizer": [
      "tokenization_mume.MumeTokenizer",
      null
    ]
  },
  "unk_token": "<unk>",
  "bos_token": "<s>",
  "eos_token": "</s>",
  "pad_token": "</s>",
  "add_bos_token": false,
  "add_eos_token": false,
  "split_special_tokens": true,
  "model_max_length": 1024,
  "padding_side": "left",
  "clean_up_tokenization_spaces": false
}

	{
	"tokenizer_class": "PreTrainedTokenizerFast",
	"auto_map": {
	"AutoTokenizer": [
	"tokenization_mume.MumeTokenizer",
	null
	]
	},
	"unk_token": "<unk>",
	"bos_token": "<s>",
	"eos_token": "</s>",
	"pad_token": "</s>",
	"add_bos_token": false,
	"add_eos_token": false,
	"split_special_tokens": true,
	"model_max_length": 1024,
	"padding_side": "left",
	"clean_up_tokenization_spaces": false
	}