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 9B-parameter vision-language model developed with sponsorship from Galactic Mandate Linux.

Model files and capabilities

Full-precision release: BF16 Safetensors in Gmlinux9b.safetensors.
Quantized release: Q8 GGUF in GMlinuxQ8.gguf.
Architecture: Qwen3.5 conditional-generation vision-language model. The BF16 repository includes processor and chat-template files.
Context: the model configuration advertises up to 262,144 positions. Practical context length depends on inference runtime and available memory; local use has been exercised at 68,096 tokens. Longer contexts have not been validated here.

For image input, use the Transformers processor and model files. The Q8 GGUF may require a matching multimodal projector (mmproj) in runtimes that use a separate projector file; check the runtime's vision setup before expecting image support from that quantized 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 the intended tasks before relying on it.

Transformers usage

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, use the GMlinuxQ8.gguf file. Set context length according to available memory and runtime support.

Base models and licensing

This release is based on Qwen3.5-9B, listed as Apache-2.0, and Ornith-1.5-9B, listed as MIT. The repository's existing Hub metadata remains Apache-2.0. Review both upstream model repositories and their license notices when using or redistributing this 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

(525)

this model