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 Settings

llama.cpp

How to use morikomorizz/GRM-2.6-Plus-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 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 serve -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

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

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 serve -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 serve -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

Atomic Chat new
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-GGUF / README.md

morikomorizz

Update README.md

4ac4626 2 months ago

preview code

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	---
	license: apache-2.0
	base_model: OrionLLM/GRM-2.6-Plus
	tags:
	- gguf
	- conversational
	- reasoning
	- qwen3_5
	pipeline_tag: text-generation
	---

	# GRM-2.6-Plus (27B) - GGUF

	## Overview
	This repository contains the GGUF quantized files for [OrionLLM/GRM-2.6-Plus](https://huggingface.co/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](https://huggingface.co/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](https://github.com/ggml-org/llama.cpp) and popular graphical interfaces like LM Studio, Ollama.

	### Example using `llama.cpp` CLI:
	```bash
	./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