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

Libraries

How to use morikomorizz/GRM-2.6-Plus-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-Plus-GGUF",
	filename="GRM-2.6-Plus-Q3_K_M.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-Plus-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-Plus-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf morikomorizz/GRM-2.6-Plus-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-Plus-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf morikomorizz/GRM-2.6-Plus-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-Plus-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf morikomorizz/GRM-2.6-Plus-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-Plus-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf morikomorizz/GRM-2.6-Plus-GGUF:Q4_K_M

Use Docker

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

LM Studio
Jan

vLLM

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

Use Docker

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

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

Unsloth Studio new

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

Pi new

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

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

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

Run Hermes

hermes

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

Lemonade

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

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

GRM-2.6-Plus (27B) - GGUF

Overview

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

GRM-2.6-Plus is a highly capable 27B-parameter reasoning model built on the Qwen3.6 architecture. It is specifically engineered for general-purpose AI and optimized for difficult, high-complexity tasks.

Original Model: OrionLLM/GRM-2.6-Plus
Architecture: Qwen3.6-27B
License: Apache 2.0

Key Capabilities

Elite-Level Reasoning for Hard Tasks: GRM-2.6-Plus is optimized to handle difficult reasoning workloads with clarity, consistency, and strong step-by-step problem-solving ability.
High Performance for Its Size: With 27B parameters, the model is designed to deliver excellent capability relative to its scale, balancing strong intelligence with practical deployment.
Advanced Coding and Agentic Use: GRM-2.6-Plus is well suited for code generation, structured problem-solving, tool-style workflows, and local agentic applications.
Optimized for Practical Deployment: The model aims to remain efficient and usable across capable consumer and workstation hardware while offering strong performance for advanced tasks.

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-Plus-Q8_0.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: 3,916

GGUF

Model size

27B params

Architecture

qwen35

Hardware compatibility

3-bit

4-bit

8-bit

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

Base model

Qwen/Qwen3.6-27B

Finetuned

OrionLLM/GRM-2.6-Plus

Quantized

(9)

this model

Collection including morikomorizz/GRM-2.6-Plus-GGUF

GRM-2.6

Collection

GRM-Repository • 3 items • Updated 10 days ago • 1