OLMo-3-7B-RLZero-Math GGUF

GGUF & MLX quantizations of Allen Institute for AI's mathematical reasoning model, optimized for local inference with llama.cpp, Ollama, and Apple Silicon.

Highlights

Model Specifications

Available Versions

GGUF Quantizations

MLX (Apple Silicon)

4-bit quantized version in MLX-4bit/ folder - optimized for M1/M2/M3/M4 Macs.

Quick Start

Ollama (Easiest)

ollama run richardyoung/olmo-3-7b-rlzero-math

llama.cpp

# Download Q4_K_M (recommended)
wget https://huggingface.co/richardyoung/OLMo-3-7B-RLZero-Math-GGUF/resolve/main/Olmo-3-7B-RLZero-Math-Q4_K_M.gguf

# Run inference
./llama-cli -m Olmo-3-7B-RLZero-Math-Q4_K_M.gguf \
  -p "Solve step by step: What is 15% of 240?" \
  -n 512

MLX (Apple Silicon)

pip install mlx-lm

mlx_lm.generate \
  --model richardyoung/OLMo-3-7B-RLZero-Math-GGUF \
  --prompt "Solve: Find the derivative of x^3 + 2x" \
  --trust-remote-code

Python

from llama_cpp import Llama

llm = Llama(
    model_path="Olmo-3-7B-RLZero-Math-Q4_K_M.gguf",
    n_ctx=4096
)

output = llm(
    "Solve step by step: What is the sum of the first 10 prime numbers?",
    max_tokens=512
)
print(output["choices"][0]["text"])

System Requirements

Prompt Format

Solve the following math problem step by step:
{your problem here}

Example:

Solve the following math problem step by step:
A train travels 120 miles in 2 hours. If it continues at the same speed,
how long will it take to travel 300 miles?

Links

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Quantization by Richard Young | Original model by Allen Institute for AI