Instructions to use magiccodingman/Seed-OSS-36B-Instruct-unsloth-MagicQuant-Hybrid-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use magiccodingman/Seed-OSS-36B-Instruct-unsloth-MagicQuant-Hybrid-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="magiccodingman/Seed-OSS-36B-Instruct-unsloth-MagicQuant-Hybrid-GGUF",
	filename="Seed-OSS-36B-Instruct-MXFP4_MOE.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use magiccodingman/Seed-OSS-36B-Instruct-unsloth-MagicQuant-Hybrid-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 magiccodingman/Seed-OSS-36B-Instruct-unsloth-MagicQuant-Hybrid-GGUF:MXFP4_MOE
# Run inference directly in the terminal:
llama cli -hf magiccodingman/Seed-OSS-36B-Instruct-unsloth-MagicQuant-Hybrid-GGUF:MXFP4_MOE

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf magiccodingman/Seed-OSS-36B-Instruct-unsloth-MagicQuant-Hybrid-GGUF:MXFP4_MOE
# Run inference directly in the terminal:
llama cli -hf magiccodingman/Seed-OSS-36B-Instruct-unsloth-MagicQuant-Hybrid-GGUF:MXFP4_MOE

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 magiccodingman/Seed-OSS-36B-Instruct-unsloth-MagicQuant-Hybrid-GGUF:MXFP4_MOE
# Run inference directly in the terminal:
./llama-cli -hf magiccodingman/Seed-OSS-36B-Instruct-unsloth-MagicQuant-Hybrid-GGUF:MXFP4_MOE

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 magiccodingman/Seed-OSS-36B-Instruct-unsloth-MagicQuant-Hybrid-GGUF:MXFP4_MOE
# Run inference directly in the terminal:
./build/bin/llama-cli -hf magiccodingman/Seed-OSS-36B-Instruct-unsloth-MagicQuant-Hybrid-GGUF:MXFP4_MOE

Use Docker

docker model run hf.co/magiccodingman/Seed-OSS-36B-Instruct-unsloth-MagicQuant-Hybrid-GGUF:MXFP4_MOE

LM Studio
Jan

vLLM

How to use magiccodingman/Seed-OSS-36B-Instruct-unsloth-MagicQuant-Hybrid-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "magiccodingman/Seed-OSS-36B-Instruct-unsloth-MagicQuant-Hybrid-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": "magiccodingman/Seed-OSS-36B-Instruct-unsloth-MagicQuant-Hybrid-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/magiccodingman/Seed-OSS-36B-Instruct-unsloth-MagicQuant-Hybrid-GGUF:MXFP4_MOE

Ollama
How to use magiccodingman/Seed-OSS-36B-Instruct-unsloth-MagicQuant-Hybrid-GGUF with Ollama:
```
ollama run hf.co/magiccodingman/Seed-OSS-36B-Instruct-unsloth-MagicQuant-Hybrid-GGUF:MXFP4_MOE
```

Unsloth Studio

How to use magiccodingman/Seed-OSS-36B-Instruct-unsloth-MagicQuant-Hybrid-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 magiccodingman/Seed-OSS-36B-Instruct-unsloth-MagicQuant-Hybrid-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 magiccodingman/Seed-OSS-36B-Instruct-unsloth-MagicQuant-Hybrid-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for magiccodingman/Seed-OSS-36B-Instruct-unsloth-MagicQuant-Hybrid-GGUF to start chatting

How to use magiccodingman/Seed-OSS-36B-Instruct-unsloth-MagicQuant-Hybrid-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf magiccodingman/Seed-OSS-36B-Instruct-unsloth-MagicQuant-Hybrid-GGUF:MXFP4_MOE

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": "magiccodingman/Seed-OSS-36B-Instruct-unsloth-MagicQuant-Hybrid-GGUF:MXFP4_MOE"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use magiccodingman/Seed-OSS-36B-Instruct-unsloth-MagicQuant-Hybrid-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf magiccodingman/Seed-OSS-36B-Instruct-unsloth-MagicQuant-Hybrid-GGUF:MXFP4_MOE

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 magiccodingman/Seed-OSS-36B-Instruct-unsloth-MagicQuant-Hybrid-GGUF:MXFP4_MOE

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use magiccodingman/Seed-OSS-36B-Instruct-unsloth-MagicQuant-Hybrid-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf magiccodingman/Seed-OSS-36B-Instruct-unsloth-MagicQuant-Hybrid-GGUF:MXFP4_MOE

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "magiccodingman/Seed-OSS-36B-Instruct-unsloth-MagicQuant-Hybrid-GGUF:MXFP4_MOE" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use magiccodingman/Seed-OSS-36B-Instruct-unsloth-MagicQuant-Hybrid-GGUF with Docker Model Runner:
```
docker model run hf.co/magiccodingman/Seed-OSS-36B-Instruct-unsloth-MagicQuant-Hybrid-GGUF:MXFP4_MOE
```

Lemonade

How to use magiccodingman/Seed-OSS-36B-Instruct-unsloth-MagicQuant-Hybrid-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull magiccodingman/Seed-OSS-36B-Instruct-unsloth-MagicQuant-Hybrid-GGUF:MXFP4_MOE

Run and chat with the model

lemonade run user.Seed-OSS-36B-Instruct-unsloth-MagicQuant-Hybrid-GGUF-MXFP4_MOE

List all available models

lemonade list

Any chance of safetensors format?

by apex-3d - opened Dec 19, 2025

Discussion

apex-3d

Dec 19, 2025

Thank you for giving us a MagicQuant of Seed!

Is there any chance we can get the weights in safetensors format for better compatibility with VLLM? I want to run this with tensor parallelism across two GPUs for better speed, but VLLM's TP doesn't support the seed_oss GGUF architecture. llama.cpp's --split-mode row helps a little bit, but it still leaves a lot of performance on the table.

magiccodingman

Owner Dec 20, 2025

So sadly, MagicQuant tactics and vLLM do not mix at all. And it pains me too! But this isn't a MagicQuant vs vLLM, but instead this is the nature of GGUF vs vLLM design reality.

vLLM is fundamentally built around uniform tensor layouts. So it can do true tensor parallelism. But MagicQuant via GGUF, deliberately uses hybrid per tensor quantization, which breaks those assumptions.

That said, if someone ever figures out how to bring hybrid tensor logic into vLLM native format, that would be an incredible day.

magiccodingman changed discussion status to closed Dec 20, 2025

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