Instructions to use DeusImperator/Artemis-31B-v1.2-GGUF-long-ctx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use DeusImperator/Artemis-31B-v1.2-GGUF-long-ctx 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 DeusImperator/Artemis-31B-v1.2-GGUF-long-ctx:Q5_K_M_HB
# Run inference directly in the terminal:
llama cli -hf DeusImperator/Artemis-31B-v1.2-GGUF-long-ctx:Q5_K_M_HB

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf DeusImperator/Artemis-31B-v1.2-GGUF-long-ctx:Q5_K_M_HB
# Run inference directly in the terminal:
llama cli -hf DeusImperator/Artemis-31B-v1.2-GGUF-long-ctx:Q5_K_M_HB

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 DeusImperator/Artemis-31B-v1.2-GGUF-long-ctx:Q5_K_M_HB
# Run inference directly in the terminal:
./llama-cli -hf DeusImperator/Artemis-31B-v1.2-GGUF-long-ctx:Q5_K_M_HB

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 DeusImperator/Artemis-31B-v1.2-GGUF-long-ctx:Q5_K_M_HB
# Run inference directly in the terminal:
./build/bin/llama-cli -hf DeusImperator/Artemis-31B-v1.2-GGUF-long-ctx:Q5_K_M_HB

Use Docker

docker model run hf.co/DeusImperator/Artemis-31B-v1.2-GGUF-long-ctx:Q5_K_M_HB

LM Studio
Jan

vLLM

How to use DeusImperator/Artemis-31B-v1.2-GGUF-long-ctx with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DeusImperator/Artemis-31B-v1.2-GGUF-long-ctx"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DeusImperator/Artemis-31B-v1.2-GGUF-long-ctx",
		"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/DeusImperator/Artemis-31B-v1.2-GGUF-long-ctx:Q5_K_M_HB

Ollama
How to use DeusImperator/Artemis-31B-v1.2-GGUF-long-ctx with Ollama:
```
ollama run hf.co/DeusImperator/Artemis-31B-v1.2-GGUF-long-ctx:Q5_K_M_HB
```
Unsloth Desktop

How to use DeusImperator/Artemis-31B-v1.2-GGUF-long-ctx with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf DeusImperator/Artemis-31B-v1.2-GGUF-long-ctx:Q5_K_M_HB

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": "DeusImperator/Artemis-31B-v1.2-GGUF-long-ctx:Q5_K_M_HB"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use DeusImperator/Artemis-31B-v1.2-GGUF-long-ctx with Docker Model Runner:
```
docker model run hf.co/DeusImperator/Artemis-31B-v1.2-GGUF-long-ctx:Q5_K_M_HB
```

Lemonade

How to use DeusImperator/Artemis-31B-v1.2-GGUF-long-ctx with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull DeusImperator/Artemis-31B-v1.2-GGUF-long-ctx:Q5_K_M_HB

Run and chat with the model

lemonade run user.Artemis-31B-v1.2-GGUF-long-ctx-Q5_K_M_HB

List all available models

lemonade list

Hermes Agent

How to use DeusImperator/Artemis-31B-v1.2-GGUF-long-ctx with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf DeusImperator/Artemis-31B-v1.2-GGUF-long-ctx:Q5_K_M_HB

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 DeusImperator/Artemis-31B-v1.2-GGUF-long-ctx:Q5_K_M_HB

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use DeusImperator/Artemis-31B-v1.2-GGUF-long-ctx with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf DeusImperator/Artemis-31B-v1.2-GGUF-long-ctx:Q5_K_M_HB

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 "DeusImperator/Artemis-31B-v1.2-GGUF-long-ctx:Q5_K_M_HB" \
  --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"

Llamacpp imatrix special quantizations of Artemis-31B-v1.2

This repo contains special long-context quants of TheDrummer/Artemis-31B-v1.2 - see original model page for details on how to use this model.

Made with llama.cpp version b11211.

Quantization details

I used bartowski's imatrix file for this model.

The quants here are specialized for long context. The following layers are preserved with overridden, higher quantization level:

input/output layer (token_embed - only one layer here since gemma 4 uses tied embeddings)
attention layers - gemma 4 uses sliding-window and global attention in ratio 5:1, so global attention is the primary focus - global attention layers are always in bf16 and other attention layers have lower quantization level

Quants are focusing on 32GB VRAM setup, so the base quantization level is Q5_K_M + overrides.

Available quants:

Artemis-31B-v1.2-Q5_K_M_hb8-ga8-a6-fl.gguf - base level Q5_K_M, input/output Q8_0, global attention Q8_0, other attention Q6_K, ffn_down Q6_K, layers 0, 1 and 59 minimum at Q6_K - 6.31 BPW

Downloads last month: 189

GGUF

Model size

31B params

Architecture

gemma4

Hardware compatibility

5-bit

Model tree for DeusImperator/Artemis-31B-v1.2-GGUF-long-ctx

Base model

google/gemma-4-31B

Finetuned

google/gemma-4-31B-it

Finetuned

TheDrummer/Artemis-31B-v1.2

Quantized

(20)

this model