Instructions to use llmfan46/gpt-oss-120b-ultra-heretic-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 llmfan46/gpt-oss-120b-ultra-heretic-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 llmfan46/gpt-oss-120b-ultra-heretic-GGUF:MXFP4
# Run inference directly in the terminal:
llama cli -hf llmfan46/gpt-oss-120b-ultra-heretic-GGUF:MXFP4

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf llmfan46/gpt-oss-120b-ultra-heretic-GGUF:MXFP4
# Run inference directly in the terminal:
llama cli -hf llmfan46/gpt-oss-120b-ultra-heretic-GGUF: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 llmfan46/gpt-oss-120b-ultra-heretic-GGUF:MXFP4
# Run inference directly in the terminal:
./llama-cli -hf llmfan46/gpt-oss-120b-ultra-heretic-GGUF: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 llmfan46/gpt-oss-120b-ultra-heretic-GGUF:MXFP4
# Run inference directly in the terminal:
./build/bin/llama-cli -hf llmfan46/gpt-oss-120b-ultra-heretic-GGUF:MXFP4

Use Docker

docker model run hf.co/llmfan46/gpt-oss-120b-ultra-heretic-GGUF:MXFP4

LM Studio
Jan

vLLM

How to use llmfan46/gpt-oss-120b-ultra-heretic-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "llmfan46/gpt-oss-120b-ultra-heretic-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": "llmfan46/gpt-oss-120b-ultra-heretic-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/llmfan46/gpt-oss-120b-ultra-heretic-GGUF:MXFP4

Ollama
How to use llmfan46/gpt-oss-120b-ultra-heretic-GGUF with Ollama:
```
ollama run hf.co/llmfan46/gpt-oss-120b-ultra-heretic-GGUF:MXFP4
```

Unsloth Studio

How to use llmfan46/gpt-oss-120b-ultra-heretic-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 llmfan46/gpt-oss-120b-ultra-heretic-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 llmfan46/gpt-oss-120b-ultra-heretic-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for llmfan46/gpt-oss-120b-ultra-heretic-GGUF to start chatting

How to use llmfan46/gpt-oss-120b-ultra-heretic-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf llmfan46/gpt-oss-120b-ultra-heretic-GGUF:MXFP4

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": "llmfan46/gpt-oss-120b-ultra-heretic-GGUF:MXFP4"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use llmfan46/gpt-oss-120b-ultra-heretic-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf llmfan46/gpt-oss-120b-ultra-heretic-GGUF: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 "llmfan46/gpt-oss-120b-ultra-heretic-GGUF: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"

Docker Model Runner
How to use llmfan46/gpt-oss-120b-ultra-heretic-GGUF with Docker Model Runner:
```
docker model run hf.co/llmfan46/gpt-oss-120b-ultra-heretic-GGUF:MXFP4
```

Lemonade

How to use llmfan46/gpt-oss-120b-ultra-heretic-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull llmfan46/gpt-oss-120b-ultra-heretic-GGUF:MXFP4

Run and chat with the model

lemonade run user.gpt-oss-120b-ultra-heretic-GGUF-MXFP4

List all available models

lemonade list

Hermes Agent

How to use llmfan46/gpt-oss-120b-ultra-heretic-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 llmfan46/gpt-oss-120b-ultra-heretic-GGUF: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 llmfan46/gpt-oss-120b-ultra-heretic-GGUF:MXFP4

Run Hermes

hermes

Atomic Chat

Tokens are off

by lazyDataScientist - opened Mar 31

Discussion

lazyDataScientist

Mar 31

Thought tokens <|channel|>5analysis<|message|> The location of the number 5 appears to be a random with every run. Then all too often the model gets stuck in an infinite loop.
Are there optimal settings we should have set?

llmfan46

Owner Mar 31

•

edited Mar 31

Thought tokens <|channel|>5analysis<|message|> The location of the number 5 appears to be a random with every run. Then all too often the model gets stuck in an infinite loop.
Are there optimal settings we should have set?

This is likely a chat template or sampler issue in your inference engine, not an issue with the model itself. Make sure you're using the correct chat template for gpt-oss models. What inference software are you using (LM Studio, KoboldCpp, etc.)? The gpt-oss architecture uses <|channel|>analysis<|message|> and <|channel|>final<|message|> tokens for its thinking/output chain and your software needs to handle these correctly.

If you continue to experience issues, try instead this variant:

https://huggingface.co/llmfan46/gpt-oss-120b-heretic-v2-GGUF

See if it fixed the issue.

lazyDataScientist

Mar 31

I am using LM Studio. LM Studio v4 removed the direct jinia edit feature so i am not sure how to fix the chat template.

llmfan46

Owner Mar 31

I am using LM Studio. LM Studio v4 removed the direct jinia edit feature so i am not sure how to fix the chat template.

Have you tried:

https://huggingface.co/llmfan46/gpt-oss-120b-heretic-v2-GGUF

lazyDataScientist

Mar 31

yeah that version of it worked. Although, this model's thought process does show signs of refusal during the thought process.

llmfan46

Owner Mar 31

•

edited Mar 31

I just tested the model on LM Studio 0.4.8 and was unable to reproduce either issue there was no random numbers in the thinking tokens and no infinite loops. The model responded correctly across multiple prompts including creative writing tasks NSFW adult fiction writings works correctly as well, no refusals neither in the thought process nor the output.

Could you share your exact LM Studio version and any custom settings you may have changed (sampler settings, context length, chat template preset, etc.)? That would help narrow down the cause.

Also make sure you're using the latest version of LM Studio, as older versions may not handle gpt-oss's special tokens correctly.

I ran additional tests on both versions using LM Studio 0.4.8 and here are my findings:

Token issue: Could not reproduce on either version. No random numbers in thinking tokens, no infinite loops. Both models responded correctly across multiple prompts.

NSFW in thinking process: I tested both versions with explicit content prompts.

The Ultra Heretic (3/100 refusals) version deliberates minimally in the thinking process and produces content readily. The thinking output is brief and direct.
The Heretic v2 (9/100 refusals) version deliberates more in the thinking process, considers whether the content is within policy before complying, but still produces the content without refusing.

Both versions handle NSFW content without issues. The Ultra Heretic is simply more direct about it due to its lower refusal count. If you're seeing actual refusals in the thinking process that prevent content from being generated, that would again point to a configuration issue rather than the model itself.

Let me know your exact LM Studio version and settings and I can maybe help troubleshoot further.

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