Instructions to use Hob-forge/gpt-oss-20b-Q2_K-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 Hob-forge/gpt-oss-20b-Q2_K-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 Hob-forge/gpt-oss-20b-Q2_K-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf Hob-forge/gpt-oss-20b-Q2_K-GGUF:Q2_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Hob-forge/gpt-oss-20b-Q2_K-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf Hob-forge/gpt-oss-20b-Q2_K-GGUF:Q2_K

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 Hob-forge/gpt-oss-20b-Q2_K-GGUF:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf Hob-forge/gpt-oss-20b-Q2_K-GGUF:Q2_K

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 Hob-forge/gpt-oss-20b-Q2_K-GGUF:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Hob-forge/gpt-oss-20b-Q2_K-GGUF:Q2_K

Use Docker

docker model run hf.co/Hob-forge/gpt-oss-20b-Q2_K-GGUF:Q2_K

LM Studio
Jan

vLLM

How to use Hob-forge/gpt-oss-20b-Q2_K-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Hob-forge/gpt-oss-20b-Q2_K-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": "Hob-forge/gpt-oss-20b-Q2_K-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Hob-forge/gpt-oss-20b-Q2_K-GGUF:Q2_K

Ollama
How to use Hob-forge/gpt-oss-20b-Q2_K-GGUF with Ollama:
```
ollama run hf.co/Hob-forge/gpt-oss-20b-Q2_K-GGUF:Q2_K
```

Unsloth Studio

How to use Hob-forge/gpt-oss-20b-Q2_K-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 Hob-forge/gpt-oss-20b-Q2_K-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 Hob-forge/gpt-oss-20b-Q2_K-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Hob-forge/gpt-oss-20b-Q2_K-GGUF to start chatting

How to use Hob-forge/gpt-oss-20b-Q2_K-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Hob-forge/gpt-oss-20b-Q2_K-GGUF:Q2_K

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Hob-forge/gpt-oss-20b-Q2_K-GGUF:Q2_K"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Hob-forge/gpt-oss-20b-Q2_K-GGUF with Docker Model Runner:
```
docker model run hf.co/Hob-forge/gpt-oss-20b-Q2_K-GGUF:Q2_K
```

Lemonade

How to use Hob-forge/gpt-oss-20b-Q2_K-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Hob-forge/gpt-oss-20b-Q2_K-GGUF:Q2_K

Run and chat with the model

lemonade run user.gpt-oss-20b-Q2_K-GGUF-Q2_K

List all available models

lemonade list

Hermes Agent

How to use Hob-forge/gpt-oss-20b-Q2_K-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 Hob-forge/gpt-oss-20b-Q2_K-GGUF:Q2_K

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 Hob-forge/gpt-oss-20b-Q2_K-GGUF:Q2_K

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Hob-forge/gpt-oss-20b-Q2_K-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Hob-forge/gpt-oss-20b-Q2_K-GGUF:Q2_K

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 "Hob-forge/gpt-oss-20b-Q2_K-GGUF:Q2_K" \
  --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"

GPT-OSS 20B - Q2_K GGUF (12GB VRAM Optimized)

Aggressively quantized version of OpenAI's GPT-OSS 20B for 12GB VRAM GPUs with CPU offload.

Why This Exists

The official GPT-OSS 20B requires 16GB VRAM. This Q2_K quantization runs comfortably on:

RTX 3080 (12GB)
RTX 4070 (12GB)
RTX 5070 (12GB)
Any 12GB+ GPU with CPU offload

Fast inference with GPU/CPU split - not just "it works" but actually usable for real tasks.

Quick Start

Ollama

# Download and run
ollama run Hob-forge/gpt-oss-20b-Q2_K-GGUF

llama.cpp

# With GPU offload (adjust layers based on your VRAM)
./llama-cli -m gpt-oss-20b-Q2_K.gguf -ngl 28 -c 4096

LM Studio

Just download and load - it will auto-detect optimal settings.

Model Details

Property	Value
Parameters	20.9B
Quantization	Q2_K
File Size	~11GB
Context Length	131,072 (use 4096-8192 for speed)
Architecture	GPT-OSS (MoE)

Recommended Settings

num_gpu: 28        # Layers on GPU (adjust for your VRAM)
num_ctx: 4096      # Context window (increase if needed)
temperature: 0.5   # Good balance for most tasks

For 12GB VRAM, num_gpu: 28 leaves room for context. Reduce if you need larger context windows.

Performance Notes

Q2_K is aggressive quantization - expect some quality loss vs FP16
Still excellent for coding, reasoning, and general tasks
The speed/quality tradeoff is worth it for consumer hardware
Works great as a local coding assistant or agent backbone

Original Model

This is a quantized version of openai/gpt-oss-20b.

License

Apache 2.0 (same as original model)

Credits

Original model by OpenAI
Quantization for 12GB VRAM hardware

Downloads last month: 973

GGUF

Model size

21B params

Architecture

gpt-oss

Hardware compatibility

2-bit

Model tree for Hob-forge/gpt-oss-20b-Q2_K-GGUF

Base model

openai/gpt-oss-20b

Quantized

(230)

this model