---

license: apache-2.0

base_model: allenai/OLMo-3-7B-RLZero-Math

language:

• en

library_name: transformers

tags:

• gguf
• mlx
• ollama
• math
• reasoning
• olmo
• quantized

pipeline_tag: text-generation

---

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

| | |

|---|---|

| Math Specialist | Fine-tuned with RL-Zero for step-by-step mathematical reasoning |

| 65K Context | 65,536 token context window with YaRN scaling |

| Apple Silicon Ready | MLX-optimized 4-bit quantization included |

| Runs Anywhere | From 4GB RAM to full precision |

Model Specifications

| Property | Value |

|----------|-------|

| Parameters | 7 billion |

| Architecture | OLMo2 |

| Context Length | 65,536 tokens |

| Training | RL-Zero mathematical reasoning |

| License | Apache 2.0 |

Available Versions

GGUF Quantizations

| Quantization | Size | Quality | Use Case |

|--------------|------|---------|----------|

| F16 | 14 GB | Near-perfect | Maximum quality, research |

| Q8_0 | 7.2 GB | Excellent | Near-lossless, high-end hardware |

| Q5_K_M | 4.9 GB | Very Good | Excellent quality/size balance |

| Q4_K_M | 4.2 GB | Good | Recommended for most users |

| IQ4_XS | 3.8 GB | Good | Compact 4-bit |

| IQ3_M | 3.2 GB | Acceptable | Ultra-compact, constrained devices |

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

| Quantization | Min RAM | Recommended | Apple Silicon |

|--------------|---------|-------------|---------------|

| IQ3_M | 4 GB | 8 GB | M1 8GB |

| IQ4_XS / Q4_K_M | 6 GB | 12 GB | M1 8GB |

| Q5_K_M / Q8_0 | 8 GB | 16 GB | M1 16GB |

| F16 | 16 GB | 24 GB | M2 Pro+ |

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

| Resource | Link |

|----------|------|

| Original Model | allenai/OLMo-3-7B-RLZero-Math |

| Ollama | richardyoung/olmo-3-7b-rlzero-math |

| Allen AI | allenai.org |

---

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