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

Use Docker

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

LM Studio
Jan

vLLM

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

Install from pip and serve model

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

Use Docker

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

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

Lemonade

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

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

Atomic Chat

Skywork-OR1-7B GGUF

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

Description

Skywork-OR1-7B (Open Reasoner 1) is a general-purpose math and code reasoning model trained with large-scale rule-based reinforcement learning using a customized GRPO algorithm. It is based on DeepSeek-R1-Distill-Qwen-7B and trained on 110K math problems and 14K coding questions with model-aware difficulty estimation, offline/online filtering, rejection sampling, multi-stage training pipeline, and adaptive entropy control.

Quantization Files

File	Bits	Size	Use Case
model_f16.gguf	16	~15.2 GB	Maximum quality, reference
model_q8_0.gguf	8	~8.5 GB	Best quality, near-lossless
model_q6_k.gguf	6	~6.6 GB	High quality
model_q5_k_m.gguf	5	~5.7 GB	Balanced quality
model_q5_k_s.gguf	5	~5.5 GB	Balanced quality, smaller
model_q4_k_m.gguf	4	~4.9 GB	Good quality, recommended
model_q4_k_s.gguf	4	~4.7 GB	Good quality, smaller
model_q3_k_l.gguf	3	~4.0 GB	Low quality, small
model_q3_k_m.gguf	3	~3.7 GB	Low quality, smaller
model_q3_k_s.gguf	3	~3.5 GB	Very low quality
model_q2_k.gguf	2	~3.0 GB	Lowest quality, minimum size

VRAM Requirements

Quantization	VRAM
F16	~16 GB
Q8_0	~9 GB
Q6_K	~7 GB
Q5_K_M	~6 GB
Q4_K_M	~5 GB
Q3_K_M	~4 GB
Q2_K	~3.5 GB

Usage

llama.cpp

./llama-cli -m model_q4_k_m.gguf -p "<your prompt>" -n 512

llama-cpp-python

from llama_cpp import Llama

llm = Llama(model_path="model_q4_k_m.gguf", n_ctx=32768)
output = llm("<your prompt>", max_tokens=512)
print(output["choices"][0]["text"])

LM Studio

Load any .gguf file directly in LM Studio via Load Model.

Ollama

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

Original Model

Skywork/Skywork-OR1-7B
Training data: Skywork/Skywork-OR1-RL-Data
License: Apache 2.0

Downloads last month: 1,271

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-OR1-7B-GGUF

Base model

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

Finetuned

Skywork/Skywork-OR1-7B

Quantized

(6)

this model