Instructions to use togethercomputer/M1-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use togethercomputer/M1-3B with Transformers:

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

pipe = pipeline("text-generation", model="togethercomputer/M1-3B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("togethercomputer/M1-3B")
model = AutoModelForCausalLM.from_pretrained("togethercomputer/M1-3B", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use togethercomputer/M1-3B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "togethercomputer/M1-3B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "togethercomputer/M1-3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/togethercomputer/M1-3B

SGLang

How to use togethercomputer/M1-3B 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 "togethercomputer/M1-3B" \
    --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": "togethercomputer/M1-3B",
		"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 "togethercomputer/M1-3B" \
        --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": "togethercomputer/M1-3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use togethercomputer/M1-3B with Docker Model Runner:
```
docker model run hf.co/togethercomputer/M1-3B
```

M1-3B / README.md

JunxiongWang

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metadata

license: mit
library_name: transformers
pipeline_tag: text-generation

This is the model is trained using paper, M1: Towards Scalable Test-Time Compute with Mamba Reasoning Models.

Model	AIME 2025	AIME 2024	MATH 500	AMC 2023	OlympiadBench
Qwen2.5-Math-7B-Instruct (Transformer)	–	13.3	79.8	50.6	40.7
rStar-Math-7B (Transformer)	–	26.7	78.4	47.5	47.1
Eurus-2-7B-PRIME (Transformer)	–	26.7	79.2	57.8	42.1
Qwen2.5-7B-SimpleRL (Transformer)	–	26.7	82.4	62.5	43.3
DeepSeek-R1-Distill-Qwen-1.5B (Transformer)	23.0	28.8	82.8	62.9	43.3
M1-3B (Mamba Hybrid Models)	23.5	28.5	84.0	62.8	47.3

Code: https://github.com/jxiw/M1

@article{wang2025m1scalabletesttimecompute,
  title={M1: Towards Scalable Test-Time Compute with Mamba Reasoning Models}, 
  author={Junxiong Wang and Wen-Ding Li and Daniele Paliotta and Daniel Ritter and Alexander M. Rush and Tri Dao},
  journal={arXiv preprint arXiv:2504.10449},
  year={2025},
  url={https://arxiv.org/abs/2504.10449}, 
}