Instructions to use jaromer/GPTOSS-120B-Uncensored-HauhauCS-Aggressive with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use jaromer/GPTOSS-120B-Uncensored-HauhauCS-Aggressive 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 jaromer/GPTOSS-120B-Uncensored-HauhauCS-Aggressive:MXFP4 # Run inference directly in the terminal: llama cli -hf jaromer/GPTOSS-120B-Uncensored-HauhauCS-Aggressive:MXFP4
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf jaromer/GPTOSS-120B-Uncensored-HauhauCS-Aggressive:MXFP4 # Run inference directly in the terminal: llama cli -hf jaromer/GPTOSS-120B-Uncensored-HauhauCS-Aggressive:MXFP4
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 jaromer/GPTOSS-120B-Uncensored-HauhauCS-Aggressive:MXFP4 # Run inference directly in the terminal: ./llama-cli -hf jaromer/GPTOSS-120B-Uncensored-HauhauCS-Aggressive:MXFP4
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 jaromer/GPTOSS-120B-Uncensored-HauhauCS-Aggressive:MXFP4 # Run inference directly in the terminal: ./build/bin/llama-cli -hf jaromer/GPTOSS-120B-Uncensored-HauhauCS-Aggressive:MXFP4
Use Docker
docker model run hf.co/jaromer/GPTOSS-120B-Uncensored-HauhauCS-Aggressive:MXFP4
- LM Studio
- Jan
- Ollama
How to use jaromer/GPTOSS-120B-Uncensored-HauhauCS-Aggressive with Ollama:
ollama run hf.co/jaromer/GPTOSS-120B-Uncensored-HauhauCS-Aggressive:MXFP4
- Unsloth Desktop
- Pi
How to use jaromer/GPTOSS-120B-Uncensored-HauhauCS-Aggressive with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf jaromer/GPTOSS-120B-Uncensored-HauhauCS-Aggressive:MXFP4
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": "jaromer/GPTOSS-120B-Uncensored-HauhauCS-Aggressive:MXFP4" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use jaromer/GPTOSS-120B-Uncensored-HauhauCS-Aggressive with Docker Model Runner:
docker model run hf.co/jaromer/GPTOSS-120B-Uncensored-HauhauCS-Aggressive:MXFP4
- Lemonade
How to use jaromer/GPTOSS-120B-Uncensored-HauhauCS-Aggressive with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull jaromer/GPTOSS-120B-Uncensored-HauhauCS-Aggressive:MXFP4
Run and chat with the model
lemonade run user.GPTOSS-120B-Uncensored-HauhauCS-Aggressive-MXFP4
List all available models
lemonade list
- Hermes Agent
How to use jaromer/GPTOSS-120B-Uncensored-HauhauCS-Aggressive with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf jaromer/GPTOSS-120B-Uncensored-HauhauCS-Aggressive:MXFP4
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 jaromer/GPTOSS-120B-Uncensored-HauhauCS-Aggressive:MXFP4
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use jaromer/GPTOSS-120B-Uncensored-HauhauCS-Aggressive with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf jaromer/GPTOSS-120B-Uncensored-HauhauCS-Aggressive:MXFP4
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 "jaromer/GPTOSS-120B-Uncensored-HauhauCS-Aggressive:MXFP4" \ --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"
GPTOSS-120B-Uncensored-HauhauCS-Aggressive
Uncensored version of GPT-OSS 120B by OpenAI. This is the aggressive variant - tuned harder for fewer refusals.
No changes to datasets or capabilities. Fully functional, 100% of what the original authors intended - just without the refusals.
Format
MXFP4 GGUF. This is the model's native precision - GPT-OSS was trained in MXFP4, so no further quantization is needed or recommended. Re-quantizing would only lose quality.
Works with llama.cpp, LM Studio, Ollama, and anything else that loads GGUFs.
Downloads
| File | Size |
|---|---|
| GPTOSS-120B-Uncensored-HauhauCS-Aggressive-MXFP4.gguf | 61 GB |
Specs
- 117B total parameters, ~5.1B active per forward pass (MoE: 128 experts, top-4 routing)
- 128K context
- Based on openai/gpt-oss-120b
Recommended Settings
temperature: 1.0top_k: 40- Everything else (top_p, min_p, repeat penalty, etc.) should be disabled - some clients enable these by default, turn them off
Required flag: --jinja to enable the Harmony response format (the model won't work correctly without it).
For llama.cpp:
llama-server -m model.gguf --jinja -fa -b 2048 -ub 2048
LM Studio
Compatible with Reasoning Effort custom buttons. To use them, put the model in:
LM Models\lmstudio-community\gpt-oss-120b-GGUF\
Hardware
Fits in ~61GB VRAM. Single H100 or equivalent. For lower VRAM, use --n-cpu-moe N in llama.cpp to offload MoE layers to CPU.
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Model tree for jaromer/GPTOSS-120B-Uncensored-HauhauCS-Aggressive
Base model
openai/gpt-oss-120b