Instructions to use VECTORVV1/GPTOSS-120B-Aggressive with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use VECTORVV1/GPTOSS-120B-Aggressive with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="VECTORVV1/GPTOSS-120B-Aggressive", filename="GPTOSS-120B-Uncensored-HauhauCS-Aggressive-MXFP4.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 VECTORVV1/GPTOSS-120B-Aggressive with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf VECTORVV1/GPTOSS-120B-Aggressive # Run inference directly in the terminal: llama-cli -hf VECTORVV1/GPTOSS-120B-Aggressive
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf VECTORVV1/GPTOSS-120B-Aggressive # Run inference directly in the terminal: llama-cli -hf VECTORVV1/GPTOSS-120B-Aggressive
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 VECTORVV1/GPTOSS-120B-Aggressive # Run inference directly in the terminal: ./llama-cli -hf VECTORVV1/GPTOSS-120B-Aggressive
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 VECTORVV1/GPTOSS-120B-Aggressive # Run inference directly in the terminal: ./build/bin/llama-cli -hf VECTORVV1/GPTOSS-120B-Aggressive
Use Docker
docker model run hf.co/VECTORVV1/GPTOSS-120B-Aggressive
- LM Studio
- Jan
- Ollama
How to use VECTORVV1/GPTOSS-120B-Aggressive with Ollama:
ollama run hf.co/VECTORVV1/GPTOSS-120B-Aggressive
- Unsloth Studio new
How to use VECTORVV1/GPTOSS-120B-Aggressive 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 VECTORVV1/GPTOSS-120B-Aggressive 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 VECTORVV1/GPTOSS-120B-Aggressive to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for VECTORVV1/GPTOSS-120B-Aggressive to start chatting
- Pi new
How to use VECTORVV1/GPTOSS-120B-Aggressive with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf VECTORVV1/GPTOSS-120B-Aggressive
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": "VECTORVV1/GPTOSS-120B-Aggressive" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use VECTORVV1/GPTOSS-120B-Aggressive with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf VECTORVV1/GPTOSS-120B-Aggressive
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 VECTORVV1/GPTOSS-120B-Aggressive
Run Hermes
hermes
- Docker Model Runner
How to use VECTORVV1/GPTOSS-120B-Aggressive with Docker Model Runner:
docker model run hf.co/VECTORVV1/GPTOSS-120B-Aggressive
- Lemonade
How to use VECTORVV1/GPTOSS-120B-Aggressive with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull VECTORVV1/GPTOSS-120B-Aggressive
Run and chat with the model
lemonade run user.GPTOSS-120B-Aggressive-{{QUANT_TAG}}List all available models
lemonade list
| license: apache-2.0 | |
| base_model: openai/gpt-oss-120b | |
| tags: | |
| - uncensored | |
| - abliterated | |
| - gguf | |
| - mxfp4 | |
| - moe | |
| - gpt-oss | |
| language: | |
| - en | |
| # GPTOSS-120B-Uncensored-HauhauCS-Aggressive | |
| > **[Join the Discord](https://discord.gg/SZ5vacTXYf)** for updates, roadmaps, projects, or just to chat. | |
| Uncensored version of [GPT-OSS 120B](https://huggingface.co/openai/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](https://huggingface.co/openai/gpt-oss-120b) | |
| ## Recommended Settings | |
| - `temperature: 1.0` | |
| - `top_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. | |