Instructions to use brittlewis12/Open-Reasoner-Zero-7B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use brittlewis12/Open-Reasoner-Zero-7B-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf brittlewis12/Open-Reasoner-Zero-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf brittlewis12/Open-Reasoner-Zero-7B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf brittlewis12/Open-Reasoner-Zero-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf brittlewis12/Open-Reasoner-Zero-7B-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 brittlewis12/Open-Reasoner-Zero-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf brittlewis12/Open-Reasoner-Zero-7B-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 brittlewis12/Open-Reasoner-Zero-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf brittlewis12/Open-Reasoner-Zero-7B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/brittlewis12/Open-Reasoner-Zero-7B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use brittlewis12/Open-Reasoner-Zero-7B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "brittlewis12/Open-Reasoner-Zero-7B-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": "brittlewis12/Open-Reasoner-Zero-7B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/brittlewis12/Open-Reasoner-Zero-7B-GGUF:Q4_K_M

Ollama
How to use brittlewis12/Open-Reasoner-Zero-7B-GGUF with Ollama:
```
ollama run hf.co/brittlewis12/Open-Reasoner-Zero-7B-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use brittlewis12/Open-Reasoner-Zero-7B-GGUF with Docker Model Runner:
```
docker model run hf.co/brittlewis12/Open-Reasoner-Zero-7B-GGUF:Q4_K_M
```

Lemonade

How to use brittlewis12/Open-Reasoner-Zero-7B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull brittlewis12/Open-Reasoner-Zero-7B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Open-Reasoner-Zero-7B-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

Open Reasoner Zero 7B GGUF

Original model: Open Reasoner Zero 7B

Model creator: Open Reasoner Zero

An Open Source Approach to Scaling Up Reinforcement Learning on the Base Model

🌊 We introduce Open-Reasoner-Zero, the first open source implementation of large-scale reasoning-oriented RL training focusing on scalability, simplicity and accessibility.

To enable broader participation in this pivotal moment we witnessed and accelerate research towards artificial general intelligence (AGI), we release our source code, parameter settings, training data, and model weights. Please refer to our paper for more insights.

This repo contains GGUF format model files for Open Reasoner Zero’s Open Reasoner Zero 7B.

What is GGUF?

GGUF is a file format for representing AI models. It is the third version of the format, introduced by the llama.cpp team on August 21st 2023.

Converted with llama.cpp build 4764 (revision 7ad0779), using autogguf-rs.

Prompt template: Open Reasoner Zero

A conversation between User and Assistant. The User asks a question, and the Assistant solves it. The Assistant first thinks about the reasoning process in the mind and then provides the User with the answer. The reasoning process is enclosed within <think> </think> and answer is enclosed within <answer> </answer> tags, respectively, i.e., <think> reasoning process here </think> <answer> answer here </answer>.
User: You must put your answer inside <answer> </answer> tags, i.e., <answer> answer here </answer>. And your final answer will be extracted automatically by the \\boxed{} tag.\nThis is the problem:
{{prompt}}
Assistant: <think>

Download & run with cnvrs on iPhone, iPad, and Mac!

cnvrs is the best app for private, local AI on your device:

create & save Characters with custom system prompts & temperature settings
download and experiment with any GGUF model you can find on HuggingFace!
- or, use an API key with the chat completions-compatible model provider of your choice -- ChatGPT, Claude, Gemini, DeepSeek, & more!
make it your own with custom Theme colors
powered by Metal ⚡️ & Llama.cpp, with haptics during response streaming!
try it out yourself today, on Testflight!
- if you already have the app, download Open Reasoner Zero 7B now!
- cnvrsai:///models/search/hf?id=brittlewis12/Open-Reasoner-Zero-7B-GGUF
follow cnvrs on twitter to stay up to date

Original Model Evaluation

Figure 1 | Evaluation performance of Open-Reasoner-Zero-{7B, 32B}. We report the average accuracy on the benchmark dataset for each question with 16 responses. Notably, Open-Reasoner-Zero-32B outperforms DeepSeek-R1-Zero-Qwen-32B on the GPQA Diamond benchmark while only requiring 1/30 of the training steps. We are continuing to scale up these RL settings until this preprint is released, as there is no sign of saturation.

Downloads last month: 940

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

1-bit

2-bit

3-bit

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5-bit

6-bit

8-bit

16-bit

Model tree for brittlewis12/Open-Reasoner-Zero-7B-GGUF

Base model

Open-Reasoner-Zero/Open-Reasoner-Zero-7B

Quantized

(5)

this model