GPTOSS-120B-Aggressive

Instructions to use VECTORVV1/GPTOSS-120B-Aggressive with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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

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

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

VECTORVV1

HauhauCS commited on Apr 14

Commit

723cd42

0 Parent(s):

Duplicate from HauhauCS/GPTOSS-120B-Uncensored-HauhauCS-Aggressive

Browse files

Co-authored-by: HauHau <HauhauCS@users.noreply.huggingface.co>

Files changed (3) hide show

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+---
+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.