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

Libraries

How to use morikomorizz/GRM-2.6-Opus-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-Opus-GGUF",
	filename="GRM-2.6-Opus-MTP-IQ4_XS_V1.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

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

Use Docker

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

LM Studio
Jan

vLLM

How to use morikomorizz/GRM-2.6-Opus-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-Opus-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-Opus-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

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

Unsloth Studio new

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

Pi new

How to use morikomorizz/GRM-2.6-Opus-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-Opus-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-Opus-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use morikomorizz/GRM-2.6-Opus-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-Opus-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-Opus-GGUF:Q4_K_M

Run Hermes

hermes

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

Lemonade

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

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

GRM-2.6-Opus (27B) - GGUF

Overview

Original Model: OrionLLM/GRM-2.6-Opus
Architecture: Qwen3.6-27B
License: Apache 2.0
MTP Support: Yes

Version	Imatrix Source
v1	Unsloth
v2	mradermacher
v3	Self-computed

This repository contains the GGUF quantized files for OrionLLM/GRM-2.6-Opus.

GRM-2.6-Opus adopts structured Opus-style reasoning, outperforming the Plus version in coding, terminal agent workflows, complex problem-solving, and high-difficulty STEM evaluations. Compared to the Plus version, GRM-2.6-Opus offers:

Opus-style Reasoning: More structured, organized, and deliberate thinking.
Enhanced Performance: Significant improvements in coding, terminal agents, complex problem-solving, and advanced STEM tasks.

Key Capabilities

Opus-Style Structured Reasoning: GRM-2.6-Opus uses a more organized reasoning format, helping it produce clearer and more reliable solutions for complex tasks.
Improved Terminal Agent Ability: The model is better suited for terminal-based agents, tool-style workflows, debugging, code execution planning, and multi-step technical tasks.
Stronger Coding Performance: The merge improves code reasoning, implementation planning, and difficult programming task handling.
Enhanced General-Purpose Intelligence: GRM-2.6-Opus remains useful across research, STEM, chat, coding, local agents, and advanced problem-solving.
Improved Over GRM-2.6-Plus: The model builds on the original GRM-2.6-Plus and adds stronger structured reasoning behavior through the Opus-style distilled merge.

SVG TEST

Prompt: Create a complete SVG loading animation of a rose: with each rotation, the rose grows, blooms, and then closes

V3 (iq4_xs)	V2 (iq4_xs)	NomXL	NyamNyam

How to Use

These GGUF files are fully compatible with llama.cpp and popular graphical interfaces like LM Studio, Ollama.

Example using `llama.cpp` CLI:

./llama-cli -m GRM-2.6-Opus-Q4_K_M.gguf \
  -p "System: You are a helpful assistant.\nUser: Create a calculator in a single HTML file backwards.\nAssistant:" \
  -n 2048 -c 8192

Downloads last month: 9,587

GGUF

Model size

27B params

Architecture

qwen35

Hardware compatibility

3-bit

4-bit

6-bit

8-bit

View +3 variants

Model tree for morikomorizz/GRM-2.6-Opus-GGUF

Base model

OrionLLM/GRM-2.6-Opus

Quantized

(4)

this model

Collection including morikomorizz/GRM-2.6-Opus-GGUF

GRM-2.6

Collection

GRM-Repository • 3 items • Updated 11 days ago • 1