Instructions to use FreedomAISVR/gpt-oss-20B-MXFP4-MOE-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FreedomAISVR/gpt-oss-20B-MXFP4-MOE-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="FreedomAISVR/gpt-oss-20B-MXFP4-MOE-GGUF",
	filename="gpt-oss-20B-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 FreedomAISVR/gpt-oss-20B-MXFP4-MOE-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf FreedomAISVR/gpt-oss-20B-MXFP4-MOE-GGUF:MXFP4_MOE
# Run inference directly in the terminal:
llama-cli -hf FreedomAISVR/gpt-oss-20B-MXFP4-MOE-GGUF:MXFP4_MOE

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf FreedomAISVR/gpt-oss-20B-MXFP4-MOE-GGUF:MXFP4_MOE
# Run inference directly in the terminal:
llama-cli -hf FreedomAISVR/gpt-oss-20B-MXFP4-MOE-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 FreedomAISVR/gpt-oss-20B-MXFP4-MOE-GGUF:MXFP4_MOE
# Run inference directly in the terminal:
./llama-cli -hf FreedomAISVR/gpt-oss-20B-MXFP4-MOE-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 FreedomAISVR/gpt-oss-20B-MXFP4-MOE-GGUF:MXFP4_MOE
# Run inference directly in the terminal:
./build/bin/llama-cli -hf FreedomAISVR/gpt-oss-20B-MXFP4-MOE-GGUF:MXFP4_MOE

Use Docker

docker model run hf.co/FreedomAISVR/gpt-oss-20B-MXFP4-MOE-GGUF:MXFP4_MOE

LM Studio
Jan

vLLM

How to use FreedomAISVR/gpt-oss-20B-MXFP4-MOE-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FreedomAISVR/gpt-oss-20B-MXFP4-MOE-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": "FreedomAISVR/gpt-oss-20B-MXFP4-MOE-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/FreedomAISVR/gpt-oss-20B-MXFP4-MOE-GGUF:MXFP4_MOE

Ollama
How to use FreedomAISVR/gpt-oss-20B-MXFP4-MOE-GGUF with Ollama:
```
ollama run hf.co/FreedomAISVR/gpt-oss-20B-MXFP4-MOE-GGUF:MXFP4_MOE
```

Unsloth Studio

How to use FreedomAISVR/gpt-oss-20B-MXFP4-MOE-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 FreedomAISVR/gpt-oss-20B-MXFP4-MOE-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 FreedomAISVR/gpt-oss-20B-MXFP4-MOE-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open /spaces/unsloth/studio in your browser
# Search for FreedomAISVR/gpt-oss-20B-MXFP4-MOE-GGUF to start chatting

How to use FreedomAISVR/gpt-oss-20B-MXFP4-MOE-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf FreedomAISVR/gpt-oss-20B-MXFP4-MOE-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": "FreedomAISVR/gpt-oss-20B-MXFP4-MOE-GGUF:MXFP4_MOE"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use FreedomAISVR/gpt-oss-20B-MXFP4-MOE-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 FreedomAISVR/gpt-oss-20B-MXFP4-MOE-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 FreedomAISVR/gpt-oss-20B-MXFP4-MOE-GGUF:MXFP4_MOE

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use FreedomAISVR/gpt-oss-20B-MXFP4-MOE-GGUF with Docker Model Runner:
```
docker model run hf.co/FreedomAISVR/gpt-oss-20B-MXFP4-MOE-GGUF:MXFP4_MOE
```

Lemonade

How to use FreedomAISVR/gpt-oss-20B-MXFP4-MOE-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull FreedomAISVR/gpt-oss-20B-MXFP4-MOE-GGUF:MXFP4_MOE

Run and chat with the model

lemonade run user.gpt-oss-20B-MXFP4-MOE-GGUF-MXFP4_MOE

List all available models

lemonade list

gpt-oss-20B-MXFP4-MOE-GGUF

MXFP4_MOE GGUF quantization of openai/gpt-oss-20b.

About MXFP4_MOE

MXFP4_MOE is the MoE variant of MXFP4 (OCP Microscaling FP4 E2M1 format). It applies a mixed-precision quantization:

Tensor Group	Quantization	Bits/Param
Expert FFN weights	MXFP4 (block FP4)	~4.25
Attention weights	Q8_0	8
Router/norms/biases	F32	32

Total: ~4.63 bits/param — the file is 11.28 GiB for 20.91B total parameters.

MXFP4 runs on any GPU or CPU (unlike NVFP4 which requires Blackwell).

Files

Filename	Type	Size	Description
`gpt-oss-20B-MXFP4_MOE.gguf`	MXFP4_MOE	11.28 GiB	Main model weights

Usage

llama.cpp CLI

llama-cli -hf FreedomAISVR/gpt-oss-20B-MXFP4-MOE-GGUF -cnv -p "You are a helpful assistant"

llama-server

llama-server -hf FreedomAISVR/gpt-oss-20B-MXFP4-MOE-GGUF --ctx-size 0 --jinja

llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
    repo_id="FreedomAISVR/gpt-oss-20B-MXFP4-MOE-GGUF",
    filename="gpt-oss-20B-MXFP4_MOE.gguf",
)

Quantization Pipeline

# 1. Convert HF model to intermediate GGUF
python convert_hf_to_gguf.py ./models/gpt-oss-20b/ --outfile gpt-oss-20b-f16.gguf --outtype f16

# 2. Quantize to MXFP4_MOE (requires --allow-requantize due to converter auto-type assignment)
llama-quantize --allow-requantize gpt-oss-20b-f16.gguf gpt-oss-20b-mxfp4_moe.gguf MXFP4_MOE

Hardware

GPU	VRAM	Notes
NVIDIA RTX 5060 Ti	16 GB	Quantization performed on this GPU

License

Apache-2.0 (same as openai/gpt-oss-20b)

Downloads last month: 945

GGUF

Model size

21B params

Architecture

gpt-oss

Hardware compatibility

4-bit

Model tree for FreedomAISVR/gpt-oss-20B-MXFP4-MOE-GGUF

Base model

openai/gpt-oss-20b

Quantized

(203)

this model