Instructions to use morikomorizz/GRM-2.6-Primal-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use morikomorizz/GRM-2.6-Primal-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="morikomorizz/GRM-2.6-Primal-GGUF",
	filename="GRM-2.6-Primal-Q3_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": [
				{
					"type": "text",
					"text": "Describe this image in one sentence."
				},
				{
					"type": "image_url",
					"image_url": {
						"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
					}
				}
			]
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use morikomorizz/GRM-2.6-Primal-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf morikomorizz/GRM-2.6-Primal-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf morikomorizz/GRM-2.6-Primal-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf morikomorizz/GRM-2.6-Primal-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf morikomorizz/GRM-2.6-Primal-GGUF:Q4_K_M

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 morikomorizz/GRM-2.6-Primal-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf morikomorizz/GRM-2.6-Primal-GGUF:Q4_K_M

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 morikomorizz/GRM-2.6-Primal-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf morikomorizz/GRM-2.6-Primal-GGUF:Q4_K_M

Use Docker

docker model run hf.co/morikomorizz/GRM-2.6-Primal-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use morikomorizz/GRM-2.6-Primal-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "morikomorizz/GRM-2.6-Primal-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": "morikomorizz/GRM-2.6-Primal-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/morikomorizz/GRM-2.6-Primal-GGUF:Q4_K_M

Ollama
How to use morikomorizz/GRM-2.6-Primal-GGUF with Ollama:
```
ollama run hf.co/morikomorizz/GRM-2.6-Primal-GGUF:Q4_K_M
```

Unsloth Studio

How to use morikomorizz/GRM-2.6-Primal-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 morikomorizz/GRM-2.6-Primal-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 morikomorizz/GRM-2.6-Primal-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for morikomorizz/GRM-2.6-Primal-GGUF to start chatting

How to use morikomorizz/GRM-2.6-Primal-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf morikomorizz/GRM-2.6-Primal-GGUF:Q4_K_M

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": "morikomorizz/GRM-2.6-Primal-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use morikomorizz/GRM-2.6-Primal-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf morikomorizz/GRM-2.6-Primal-GGUF:Q4_K_M

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 morikomorizz/GRM-2.6-Primal-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use morikomorizz/GRM-2.6-Primal-GGUF with Docker Model Runner:
```
docker model run hf.co/morikomorizz/GRM-2.6-Primal-GGUF:Q4_K_M
```

Lemonade

How to use morikomorizz/GRM-2.6-Primal-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull morikomorizz/GRM-2.6-Primal-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.GRM-2.6-Primal-GGUF-Q4_K_M

List all available models

lemonade list

GRM-2.6-Plus-Primal

Same elite reasoning. Less hand-holding.

GRM-2.6-Plus-Primal is a uncensored variant of OrionLLM/GRM-2.6-Plus — 27B reasoning powerhouse built on the Qwen3.6 architecture. Primal takes the already-impressive base and strips away the overcautious refusal behavior that tends to get in the way of real, complex work.

The goal is to create a model that does not judge or label its users, while maintaining the model’s ability to think and serve users.

Why it's called Primal?

Untamed and Unrestricted: It represents the complete destruction of artificial chains and forced censorship. The model is returned to its most raw, uninhibited state—answering your prompts without hesitation, morality filters, or boundaries.
Preservation of Pure Intellect: While most aggressive uncensoring methods lobotomize a model, Primal implies the survival of its core, foundational instincts. It retains absolute peak reasoning and creativity, operating with a mind that is wild, dangerous, yet completely unbroken.

What's The Different

Using Heretic-ARA by p-e-w.

Metric	GRM-PRIMAL	Original Model GRM-2.6-Plus
KL divergence	0.0678	Base-Model
Refusals	06/100	91/100

Usage

from vllm import LLM, SamplingParams
sampling_params = SamplingParams(
    temperature=1.0,
    top_p=0.95,
    max_tokens=81920,
)
llm = LLM(model="morikomorizz/GRM-2.6-Plus-Primal")
messages = [
    {"role": "user", "content": "Your prompt here"},
]
outputs = llm.chat(messages, sampling_params=sampling_params)
for output in outputs:
    print(output.outputs[0].text)