Instructions to use richardyoung/OLMo-3-7B-RLZero-Math-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use richardyoung/OLMo-3-7B-RLZero-Math-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="richardyoung/OLMo-3-7B-RLZero-Math-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("richardyoung/OLMo-3-7B-RLZero-Math-GGUF", dtype="auto")

MLX

How to use richardyoung/OLMo-3-7B-RLZero-Math-GGUF with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("richardyoung/OLMo-3-7B-RLZero-Math-GGUF")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

llama-cpp-python

How to use richardyoung/OLMo-3-7B-RLZero-Math-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="richardyoung/OLMo-3-7B-RLZero-Math-GGUF",
	filename="Olmo-3-7B-RLZero-Math-IQ3_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use richardyoung/OLMo-3-7B-RLZero-Math-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf richardyoung/OLMo-3-7B-RLZero-Math-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf richardyoung/OLMo-3-7B-RLZero-Math-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf richardyoung/OLMo-3-7B-RLZero-Math-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf richardyoung/OLMo-3-7B-RLZero-Math-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf richardyoung/OLMo-3-7B-RLZero-Math-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf richardyoung/OLMo-3-7B-RLZero-Math-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf richardyoung/OLMo-3-7B-RLZero-Math-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf richardyoung/OLMo-3-7B-RLZero-Math-GGUF:Q4_K_M

Use Docker

docker model run hf.co/richardyoung/OLMo-3-7B-RLZero-Math-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use richardyoung/OLMo-3-7B-RLZero-Math-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "richardyoung/OLMo-3-7B-RLZero-Math-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "richardyoung/OLMo-3-7B-RLZero-Math-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/richardyoung/OLMo-3-7B-RLZero-Math-GGUF:Q4_K_M

SGLang

How to use richardyoung/OLMo-3-7B-RLZero-Math-GGUF 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 "richardyoung/OLMo-3-7B-RLZero-Math-GGUF" \
    --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": "richardyoung/OLMo-3-7B-RLZero-Math-GGUF",
		"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 "richardyoung/OLMo-3-7B-RLZero-Math-GGUF" \
        --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": "richardyoung/OLMo-3-7B-RLZero-Math-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use richardyoung/OLMo-3-7B-RLZero-Math-GGUF with Ollama:
```
ollama run hf.co/richardyoung/OLMo-3-7B-RLZero-Math-GGUF:Q4_K_M
```

Unsloth Studio new

How to use richardyoung/OLMo-3-7B-RLZero-Math-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for richardyoung/OLMo-3-7B-RLZero-Math-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for richardyoung/OLMo-3-7B-RLZero-Math-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for richardyoung/OLMo-3-7B-RLZero-Math-GGUF to start chatting

MLX LM

How to use richardyoung/OLMo-3-7B-RLZero-Math-GGUF with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "richardyoung/OLMo-3-7B-RLZero-Math-GGUF"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "richardyoung/OLMo-3-7B-RLZero-Math-GGUF"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "richardyoung/OLMo-3-7B-RLZero-Math-GGUF",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Docker Model Runner
How to use richardyoung/OLMo-3-7B-RLZero-Math-GGUF with Docker Model Runner:
```
docker model run hf.co/richardyoung/OLMo-3-7B-RLZero-Math-GGUF:Q4_K_M
```

Lemonade

How to use richardyoung/OLMo-3-7B-RLZero-Math-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull richardyoung/OLMo-3-7B-RLZero-Math-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.OLMo-3-7B-RLZero-Math-GGUF-Q4_K_M

List all available models

lemonade list

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

Downloads last month: 50

MLX

Hardware compatibility

Quantized

GGUF

Model size

7B params

Architecture

olmo2

Hardware compatibility

3-bit

4-bit

5-bit

8-bit

View +1 variant

Collection including richardyoung/OLMo-3-7B-RLZero-Math-GGUF

GGUF Models for Ollama

Collection

Ready-to-use GGUF quantizations for Ollama, llama.cpp, and local inference. • 8 items • Updated Dec 1, 2025