A 30B-parameter (Mixture-of-Experts) model specialized for **mathematical problem-solving and proof-writing in natural language**, packaged for Ollama with a 256K context window.

Nomos-1

A 30B-parameter (Mixture-of-Experts) model specialized for mathematical problem-solving and proof-writing in natural language, packaged for Ollama with a 256K context window.

🚀 Overview

nomos-1 is an Ollama distribution of Nomos-1, a model released by NousResearch that specializes the Qwen/Qwen3-30B-A3B-Thinking-2507 foundation model for deep mathematical reasoning, problem-solving, and proof-writing in natural language. According to the upstream model card, Nomos-1 was trained in collaboration with Hillclimb AI and is designed to be used alongside the Nomos Reasoning Harness, which was open-sourced concurrently.

This packaging exposes the model as ready-to-run GGUF quantizations on Ollama. The underlying architecture is Qwen3 MoE (qwen3moe) with 30.5B total parameters and an advertised 256K context window. It uses the ChatML prompt format (<|im_start|> / <|im_end|>).

Base lineage: Qwen/Qwen3-30B-A3B-Thinking-2507 (NousResearch/nomos-1)
Architecture: Qwen3 MoE (qwen3moe)
Total parameters: 30.5B
Context window: 256K (as advertised on the model’s Ollama page)
License: Apache 2.0 (per the upstream NousResearch model card)

Note: Per the upstream model card, it is recommended to run Nomos-1 without a system prompt.

📊 Benchmarks

The upstream model card reports the following result on Putnam 2025, measured when the model is wrapped in the Nomos Reasoning Harness:

Model	Putnam 2025 (with Nomos Reasoning Harness)
Nomos-1	87 / 120
Qwen3-30B-A3B-Thinking-2507 (base)	24 / 120

These figures are from the official NousResearch model card and reflect performance with the reasoning harness; results without the harness are not stated here.

🎯 Key Features

🧮 Math-specialized, tuned for problem-solving and rigorous proof-writing in natural language.
🧠 Reasoning (“Thinking”) base, built on Qwen3-30B-A3B-Thinking-2507, a chain-of-thought–oriented MoE model.
📏 256K context, long-context window suitable for lengthy problems, multi-step derivations, and large supporting material.
⚡ Efficient MoE, ~30.5B total parameters with sparse activation (A3B), giving strong reasoning at a fraction of the active compute of a dense 30B model.
🧩 Harness-ready, designed to pair with the open-source Nomos Reasoning Harness.

🏷️ Available Versions

Tag	Quantization	Size	Context
`nomos-1:Q4_K_M`	Q4_K_M	19 GB	256K
`nomos-1:iq4_xs`	IQ4_XS	17 GB	256K

Q4_K_M generally offers higher fidelity; iq4_xs is slightly smaller and lighter on memory. Sizes are as listed on the model’s Ollama page.

💻 Quick Start

# Pull and run (Q4_K_M)
ollama run richardyoung/nomos-1:Q4_K_M

# Or the smaller IQ4_XS build
ollama run richardyoung/nomos-1:iq4_xs

# Example: ask a math problem (no system prompt, per upstream guidance)
ollama run richardyoung/nomos-1:Q4_K_M "Prove that the sum of the first n odd positive integers equals n^2."

For best results, follow the upstream recommendation and run without a system prompt, and consider pairing the model with the Nomos Reasoning Harness for the strongest results.

🛠️ Use Cases

📐 Mathematical problem-solving (competition-style and general).
✍️ Writing and checking natural-language proofs.
🎓 Tutoring and step-by-step explanation of mathematical reasoning.
🔬 Research and experimentation with reasoning harnesses on long-context math tasks.

📋 System Requirements

These 4-bit builds are ~17–19 GB on disk. Practical guidance:

Setup	Notes
Recommended	A GPU with 24 GB+ VRAM (e.g. RTX ³⁰⁹⁰⁄₄₀₉₀) for comfortable inference at moderate context.
Minimum	~20 GB combined VRAM/RAM to load the weights; expect slower CPU/offloaded inference below that.
Long context	Using the full 256K window substantially increases memory use for the KV cache, additional headroom is required.

Actual requirements scale with the context length you use and your quantization choice.

🔧 Technical Details

Property	Value
Base model	`Qwen/Qwen3-30B-A3B-Thinking-2507`
Upstream release	`NousResearch/nomos-1`
Architecture	Qwen3 MoE (`qwen3moe`)
Total parameters	30.5B
Context window	256K (advertised)
Prompt format	ChatML (`<
Quantizations	Q4_K_M (19 GB), IQ4_XS (17 GB)
License	Apache 2.0 (per upstream card)
Specialization	Mathematical problem-solving & proof-writing

⚠️ Disclaimer

This is a community redistribution of NousResearch’s Nomos-1 in GGUF/Ollama form. It is provided “as is”, without warranty of any kind. Nomos-1 is specialized for mathematical reasoning; like all language models it can produce incorrect or incomplete reasoning, and its outputs, including proofs, should be independently verified before being relied upon. Use is subject to the upstream Apache 2.0 license and the licenses/terms of the underlying Qwen base model. The benchmark figures shown are reproduced from the upstream model card and were obtained with the Nomos Reasoning Harness; your results may differ.

🙏 Acknowledgments

NousResearch, for releasing Nomos-1.
Hillclimb AI, collaborator on training, per the upstream model card.
Qwen Team (Alibaba), for the Qwen3-30B-A3B-Thinking-2507 base model.
Ollama and llama.cpp, for the runtime and GGUF tooling that make local inference possible.

Built & maintained by Richard Young · DeepNeuro

A 30B-parameter (Mixture-of-Experts) model specialized for mathematical problem-solving and proof-writing in natural language, packaged for Ollama with a 256K context window.

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