Instructions to use NousResearch/nomos-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NousResearch/nomos-1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="NousResearch/nomos-1") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("NousResearch/nomos-1") model = AutoModelForCausalLM.from_pretrained("NousResearch/nomos-1") 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
- vLLM
How to use NousResearch/nomos-1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "NousResearch/nomos-1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "NousResearch/nomos-1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/NousResearch/nomos-1
- SGLang
How to use NousResearch/nomos-1 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 "NousResearch/nomos-1" \ --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": "NousResearch/nomos-1", "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 "NousResearch/nomos-1" \ --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": "NousResearch/nomos-1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use NousResearch/nomos-1 with Docker Model Runner:
docker model run hf.co/NousResearch/nomos-1
Nomos 1
We release Nomos 1, a specialization of Qwen/Qwen3-30B-A3B-Thinking-2507 for mathematical problem-solving and proof-writing in natural language. Nomos-1 was trained in collaboration with Hillclimb AI.
Nomos 1 is designed to be used with the Nomos Reasoning Harness, which we open-source concurrently.
On Putnam 2025, Nomos 1 scores 87/120 when wrapped in the Nomos reasoning harness. Under the same conditions, Qwen/Qwen3-30B-A3B-Thinking-2507 scores 24/120.
Usage
We recommend using Nomos-1 without a system prompt.
Hugging Face
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "NousResearch/nomos-1"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.float16,
device_map="auto",
trust_remote_code=True,
)
messages = [
{
"role": "user",
"content": (
"Solve the following problem and show your reasoning:\n\n"
"Let a, b, c be positive real numbers such that abc = 1. "
"Prove that\n"
"\\[\n"
" \\frac{1}{1+a} + \\frac{1}{1+b} + \\frac{1}{1+c} \\ge 1.\n"
"\\]"
),
},
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt",
).to(model.device)
with torch.no_grad():
outputs = model.generate(
**inputs,
max_new_tokens=400,
temperature=0.6,
top_p=0.95,
top_k=20,
do_sample=True,
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
SGLang
python -m sglang.launch_server \
--model-path NousResearch/nomos-1 \
--tp-size 8
vLLM
vllm serve \
--model NousResearch/nomos-1 \
--tensor-parallel-size 8
Acknowledgements
We would like to thank the following contributors:
Nous Research
Roger Jin, Jeffrey Quesnelle, Dakota Mahan, Chen Guang, Teknium
Hillclimb AI
jpark, Ibrakhim Ustelbay, Pruthvi Rajaghatta
Math experts
Samuel Kim, Miron Yurkevich, Adilet Zauytkhan, Rinat Amankos, Alexander Andreyev, Damir Nurlanov, Abuzer Abuov
Other
massiveaxe
Citation
@misc{nomos_model2025,
title = {Nomos Model},
author = {Jin, Roger and Quesnelle, Jeffrey and Mahan, Dakota and Guang, Chen and Teknium, Ryan and Park, Jun and Ustelbay, Ibrakhim and Kim, Samuel and Yurkevich, Miron and Zauytkhan, Adilet and Amankos, Rinat and Andreyev, Alex and Nurlanov, Damir and Abuov, Abuzer and massiveaxe, Askar},
year = {2025},
howpublished = {\url{https://huggingface.co/NousResearch/nomos-1}},
note = {Model release},
}
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