Instructions to use emperorofrome/GMLINUX-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use emperorofrome/GMLINUX-9B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="emperorofrome/GMLINUX-9B")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("emperorofrome/GMLINUX-9B")
model = AutoModelForMultimodalLM.from_pretrained("emperorofrome/GMLINUX-9B", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use emperorofrome/GMLINUX-9B 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 emperorofrome/GMLINUX-9B
# Run inference directly in the terminal:
llama cli -hf emperorofrome/GMLINUX-9B

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf emperorofrome/GMLINUX-9B
# Run inference directly in the terminal:
llama cli -hf emperorofrome/GMLINUX-9B

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 emperorofrome/GMLINUX-9B
# Run inference directly in the terminal:
./llama-cli -hf emperorofrome/GMLINUX-9B

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 emperorofrome/GMLINUX-9B
# Run inference directly in the terminal:
./build/bin/llama-cli -hf emperorofrome/GMLINUX-9B

Use Docker

docker model run hf.co/emperorofrome/GMLINUX-9B

LM Studio
Jan

vLLM

How to use emperorofrome/GMLINUX-9B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "emperorofrome/GMLINUX-9B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "emperorofrome/GMLINUX-9B",
		"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/emperorofrome/GMLINUX-9B

SGLang

How to use emperorofrome/GMLINUX-9B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "emperorofrome/GMLINUX-9B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "emperorofrome/GMLINUX-9B",
		"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 images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "emperorofrome/GMLINUX-9B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "emperorofrome/GMLINUX-9B",
		"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"
						}
					}
				]
			}
		]
	}'

Ollama
How to use emperorofrome/GMLINUX-9B with Ollama:
```
ollama run hf.co/emperorofrome/GMLINUX-9B
```
Unsloth Desktop

How to use emperorofrome/GMLINUX-9B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf emperorofrome/GMLINUX-9B

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "emperorofrome/GMLINUX-9B"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use emperorofrome/GMLINUX-9B with Docker Model Runner:
```
docker model run hf.co/emperorofrome/GMLINUX-9B
```

Lemonade

How to use emperorofrome/GMLINUX-9B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull emperorofrome/GMLINUX-9B

Run and chat with the model

lemonade run user.GMLINUX-9B-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use emperorofrome/GMLINUX-9B with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf emperorofrome/GMLINUX-9B

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 emperorofrome/GMLINUX-9B

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use emperorofrome/GMLINUX-9B with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf emperorofrome/GMLINUX-9B

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 "emperorofrome/GMLINUX-9B" \
  --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"

GMLinux 9B

GMLinux 9B is a 9-billion-parameter merged vision-language model sponsored by Galactic Mandate Linux.

Model files

File	Format	Description
Gmlinux9b.safetensors	BF16 Safetensors	Transformers model weights
GMlinuxQ8.gguf	Q8_0 GGUF	Quantized model for compatible runtimes

The repository also includes the configuration, tokenizer, processor, and chat-template files used by the Transformers release.

Context and vision

The configuration advertises a context length of up to 262,144 tokens. Practical context depends on the inference runtime and available memory. Local use has been tested at 68,096 tokens; longer contexts have not been validated here.

For image input, use the Transformers model and processor files. The Q8 GGUF may require a separate multimodal projector, depending on the runtime. Check the runtime's vision setup before expecting image support from that file.

Evaluation

On Galactic Mandate Linux's internal knowledge and reasoning benchmarks, GMLinux 9B scores 30% higher than Qwen3.5-9B and Ornith-1.5-9B.

General coding performance has not been established as superior to the base models. Evaluate it on your intended tasks before relying on it.

Transformers

import torch
from transformers import AutoModelForImageTextToText, AutoProcessor

model_id = "emperorofrome/GMLINUX-9B"
processor = AutoProcessor.from_pretrained(model_id)
model = AutoModelForImageTextToText.from_pretrained(
    model_id,
    dtype=torch.bfloat16,
    device_map="auto",
)

For LM Studio or llama.cpp, load GMlinuxQ8.gguf and set the context length according to available memory and runtime support.

Upstream models and licensing

The listed upstream repositories are Qwen3.5-9B (Apache-2.0) and Ornith-1.5-9B (MIT). This repository retains its existing Apache-2.0 metadata. Review the upstream license notices before using or redistributing the model.

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GGUF

Model size

9B params

Architecture

qwen35

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Model tree for emperorofrome/GMLINUX-9B

Base model

Qwen/Qwen3.5-9B-Base

Finetuned

Qwen/Qwen3.5-9B

Quantized

(537)

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