Instructions to use Skywork/Skywork-R1V3-38B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use Skywork/Skywork-R1V3-38B-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Skywork/Skywork-R1V3-38B-GGUF", filename="Skywork-R1V3-38B-Q4_K_M.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use Skywork/Skywork-R1V3-38B-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Skywork/Skywork-R1V3-38B-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf Skywork/Skywork-R1V3-38B-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 Skywork/Skywork-R1V3-38B-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf Skywork/Skywork-R1V3-38B-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 Skywork/Skywork-R1V3-38B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Skywork/Skywork-R1V3-38B-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 Skywork/Skywork-R1V3-38B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Skywork/Skywork-R1V3-38B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/Skywork/Skywork-R1V3-38B-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use Skywork/Skywork-R1V3-38B-GGUF with Ollama:
ollama run hf.co/Skywork/Skywork-R1V3-38B-GGUF:Q4_K_M
- Unsloth Studio new
How to use Skywork/Skywork-R1V3-38B-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 Skywork/Skywork-R1V3-38B-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 Skywork/Skywork-R1V3-38B-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Skywork/Skywork-R1V3-38B-GGUF to start chatting
- Pi new
How to use Skywork/Skywork-R1V3-38B-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf Skywork/Skywork-R1V3-38B-GGUF:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "Skywork/Skywork-R1V3-38B-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use Skywork/Skywork-R1V3-38B-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf Skywork/Skywork-R1V3-38B-GGUF:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default Skywork/Skywork-R1V3-38B-GGUF:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use Skywork/Skywork-R1V3-38B-GGUF with Docker Model Runner:
docker model run hf.co/Skywork/Skywork-R1V3-38B-GGUF:Q4_K_M
- Lemonade
How to use Skywork/Skywork-R1V3-38B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Skywork/Skywork-R1V3-38B-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Skywork-R1V3-38B-GGUF-Q4_K_M
List all available models
lemonade list
llm.create_chat_completion(
messages = "No input example has been defined for this model task."
)π§ Skywork-R1V3-38B - GGUF Quantized
This repository provides a GGUF quantized version of the Skywork-R1V3-38B model, converted using the latest master branch of llama.cpp. This version is optimized for fast and memory-efficient local inference on CPU or GPU.
π» How to Use
You can run this model with llama.cpp:
./llama-server -m /path/to/Skywork-R1V3-38B-Q8_0.gguf --mmproj /path/to/mmproj-Skywork-R1V3-38B-f16.gguf --port 8080
You can now use OpenAI-compatible tools (like curl) to query the model:
BASE64_IMAGE=$(base64 -w 0 /path/to/image)
curl -X POST http://localhost:8080/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "Skywork-R1V3",
"messages": [
{
"role": "user",
"content": [
{"type": "text", "text": "Please describe this image."},
{"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,'"${BASE64_IMAGE}"'" }}
]
}
],
"temperature": 0.7,
"max_tokens": 512
}'
Citation
If you use this model in your research, please cite:
@misc{shen2025skyworkr1v3technicalreport,
title={Skywork-R1V3 Technical Report},
author={Wei Shen and Jiangbo Pei and Yi Peng and Xuchen Song and Yang Liu and Jian Peng and Haofeng Sun and Yunzhuo Hao and Peiyu Wang and Jianhao Zhang and Yahui Zhou},
year={2025},
eprint={2507.06167},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2507.06167},
}
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# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Skywork/Skywork-R1V3-38B-GGUF", filename="", )