Instructions to use tinyopsec/Skywork-OR1-Math-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 tinyopsec/Skywork-OR1-Math-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 tinyopsec/Skywork-OR1-Math-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf tinyopsec/Skywork-OR1-Math-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 tinyopsec/Skywork-OR1-Math-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf tinyopsec/Skywork-OR1-Math-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 tinyopsec/Skywork-OR1-Math-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf tinyopsec/Skywork-OR1-Math-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 tinyopsec/Skywork-OR1-Math-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf tinyopsec/Skywork-OR1-Math-7B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/tinyopsec/Skywork-OR1-Math-7B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use tinyopsec/Skywork-OR1-Math-7B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tinyopsec/Skywork-OR1-Math-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": "tinyopsec/Skywork-OR1-Math-7B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tinyopsec/Skywork-OR1-Math-7B-GGUF:Q4_K_M

Ollama
How to use tinyopsec/Skywork-OR1-Math-7B-GGUF with Ollama:
```
ollama run hf.co/tinyopsec/Skywork-OR1-Math-7B-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use tinyopsec/Skywork-OR1-Math-7B-GGUF with Docker Model Runner:
```
docker model run hf.co/tinyopsec/Skywork-OR1-Math-7B-GGUF:Q4_K_M
```

Lemonade

How to use tinyopsec/Skywork-OR1-Math-7B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull tinyopsec/Skywork-OR1-Math-7B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Skywork-OR1-Math-7B-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

Skywork-OR1-Math-7B - GGUF

GGUF quantizations of Skywork/Skywork-OR1-Math-7B.

Model Description

Skywork-OR1-Math-7B is a math-specialized reasoning model from the Skywork Open Reasoner 1 (OR1) series, trained using large-scale rule-based reinforcement learning. It is fine-tuned on top of DeepSeek-R1-Distill-Qwen-7B with a curated dataset of 110K verifiable math problems and 14K coding questions.

It achieves 69.8 on AIME24 and 52.3 on AIME25, outperforming all similarly sized models on mathematical reasoning tasks.

Benchmark Results

Model	AIME24 (Avg@32)	AIME25 (Avg@32)
DeepSeek-R1-Distill-Qwen-7B	55.5	39.2
Light-R1-7B-DS	59.1	44.3
Skywork-OR1-Math-7B	69.8	52.3

Available Quantizations

File	Bits	Size (approx)	Use Case
model_f16.gguf	16	~15.3 GB	Maximum quality, reference
model_q8_0.gguf	8	~8.1 GB	Best quality, if VRAM allows
model_q6_k.gguf	6	~6.3 GB	Very high quality
model_q5_k_m.gguf	5	~5.5 GB	Recommended balance
model_q5_k_s.gguf	5	~5.3 GB	High quality, slightly smaller
model_q4_k_m.gguf	4	~4.7 GB	Good quality, widely used
model_q4_k_s.gguf	4	~4.5 GB	Smaller, slight quality loss
model_q3_k_l.gguf	3	~4.0 GB	Low VRAM, moderate quality
model_q3_k_m.gguf	3	~3.7 GB	Low VRAM
model_q3_k_s.gguf	3	~3.5 GB	Minimal VRAM
model_q2_k.gguf	2	~2.9 GB	Lowest quality, smallest size

VRAM Requirements

Quantization	VRAM (approx)
F16	~16 GB
Q8_0	~9 GB
Q6_K	~7 GB
Q5_K_M	~6 GB
Q4_K_M	~5.5 GB
Q3_K_M	~4.5 GB
Q2_K	~3.5 GB

Usage

llama.cpp

./llama-cli -m model_q4_k_m.gguf -p "Solve: What is the sum of all prime numbers less than 20?" -n 512

llama-cpp-python

from llama_cpp import Llama

llm = Llama(model_path="model_q4_k_m.gguf", n_ctx=4096)
output = llm("Solve: What is the sum of all prime numbers less than 20?", max_tokens=512)
print(output["choices"][0]["text"])

LM Studio

Download any .gguf file from this repository and load it directly in LM Studio.

Ollama

ollama run hf.co/tinyopsec/Skywork-OR1-Math-7B-GGUF:Q4_K_M

Original Model

Original model: Skywork/Skywork-OR1-Math-7B
Base model: deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
Paper: Skywork Open Reasoner 1 Technical Report
License: Qwen License

Citation

@article{he2025skywork,
  title={Skywork Open Reasoner 1 Technical Report},
  author={He, Jujie and Liu, Jiacai and Liu, Chris Yuhao and Yan, Rui and Wang, Chaojie and Cheng, Peng and Zhang, Xiaoyu and Zhang, Fuxiang and Xu, Jiacheng and Shen, Wei and Li, Siyuan and Zeng, Liang and Wei, Tianwen and Cheng, Cheng and An, Bo and Liu, Yang and Zhou, Yahui},
  journal={arXiv preprint arXiv:2505.22312},
  year={2025}
}

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GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for tinyopsec/Skywork-OR1-Math-7B-GGUF

Base model

deepseek-ai/DeepSeek-R1-Distill-Qwen-7B

Finetuned

Skywork/Skywork-OR1-Math-7B

Quantized

(4)

this model

Paper for tinyopsec/Skywork-OR1-Math-7B-GGUF

Skywork Open Reasoner 1 Technical Report

Paper • 2505.22312 • Published May 28, 2025 • 56