Instructions to use Abiray/Artemis-31B-v1.2-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Abiray/Artemis-31B-v1.2-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 Abiray/Artemis-31B-v1.2-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf Abiray/Artemis-31B-v1.2-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 Abiray/Artemis-31B-v1.2-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf Abiray/Artemis-31B-v1.2-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 Abiray/Artemis-31B-v1.2-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Abiray/Artemis-31B-v1.2-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 Abiray/Artemis-31B-v1.2-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Abiray/Artemis-31B-v1.2-GGUF:Q4_K_M
Use Docker
docker model run hf.co/Abiray/Artemis-31B-v1.2-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use Abiray/Artemis-31B-v1.2-GGUF with Ollama:
ollama run hf.co/Abiray/Artemis-31B-v1.2-GGUF:Q4_K_M
- Unsloth Desktop
- Pi
How to use Abiray/Artemis-31B-v1.2-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Abiray/Artemis-31B-v1.2-GGUF:Q4_K_M
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": "Abiray/Artemis-31B-v1.2-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use Abiray/Artemis-31B-v1.2-GGUF with Docker Model Runner:
docker model run hf.co/Abiray/Artemis-31B-v1.2-GGUF:Q4_K_M
- Lemonade
How to use Abiray/Artemis-31B-v1.2-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Abiray/Artemis-31B-v1.2-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Artemis-31B-v1.2-GGUF-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use Abiray/Artemis-31B-v1.2-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 Abiray/Artemis-31B-v1.2-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 Abiray/Artemis-31B-v1.2-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use Abiray/Artemis-31B-v1.2-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Abiray/Artemis-31B-v1.2-GGUF:Q4_K_M
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 "Abiray/Artemis-31B-v1.2-GGUF:Q4_K_M" \ --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"
Artemis-31B-v1.2 GGUF
Static GGUF quantizations of TheDrummer/Artemis-31B-v1.2.
Artemis-31B-v1.2 is a fine-tuned, unaligned creative writing and roleplay model built on Google's Gemma 4 31B base architecture. It is specifically calibrated for literary depth, psychological nuance, long-horizon scene continuity, and multi-turn roleplay.
Available Files & VRAM Recommendations
| Filename | Size | Recommended VRAM | Description & Use Case |
|---|---|---|---|
Artemis-31B-v1.2-Q3_K_M.gguf |
15.3 GB | 18 – 20 GB | Low-memory footprint; runs comfortably on constrained setups. |
Artemis-31B-v1.2-Q4_K_S.gguf |
17.8 GB | 20 – 22 GB | Lean 4-bit quant; fits tighter single-GPU configurations. |
Artemis-31B-v1.2-Q4_K_M.gguf |
18.7 GB | 24 GB | Community Sweet Spot. Balances quality retention and context headroom. |
Artemis-31B-v1.2-Q5_K_M.gguf |
21.8 GB | 24 – 32 GB | Near-lossless precision; exceptional for descriptive prose and dialogue fidelity. |
Artemis-31B-v1.2-Q6_K.gguf |
25.2 GB | 32 – 40 GB | High-tier quant; indistinguishable from native 16-bit float. |
Artemis-31B-v1.2-Q8_0.gguf |
32.6 GB | 40 – 48 GB | Reference baseline quant with near-zero perplexity loss. |
Prompt Formats
Artemis-31B uses the Gemma 4 Chat Template and natively supports both Thinking (Reasoning Scratchpad) and Direct Storytelling operational modes.
1. Thinking Mode (Recommended for Complex Plots)
Trigger the reasoning planner by embedding <|think|> into the prompt. The model will plan character motivations, spatial distance, and scene tone in a scratchpad before generating the visible narrative.
<start_of_turn>user
<|think|>
Write a scene where two rival mercenaries negotiate a temporary truce in an abandoned chapel during a midnight thunderstorm.<end_of_turn>
<start_of_turn>model
<|channel>thought
The scene demands tension and heavy atmospheric detail. Both characters should remain guarded, tracking the other's weapon hand.
<|channel>call
Rain rattled against the leaded glass like loose teeth...
2. Standard Non-Thinking Mode
For traditional turn-by-turn dialogue without reasoning tokens:
<start_of_turn>user
{{user_prompt}}<end_of_turn>
<start_of_turn>model
{{model_response}}<end_of_turn>
Sampler Guidance (Anti-"Dash Spiral")
Because Artemis possesses a wide, expressive vocabulary distribution, standard greedy samplers can sometimes trigger repetitive loops or excessive em-dashes (—). The community consensus recommends pairing the model with Min-P:
Recommended Daily Driver (SillyTavern / KoboldCpp)
- Temperature:
0.95–1.05 - Min-P:
0.08 - Top-P: Disabled (
1.0) - Repetition Penalty:
1.06–1.08 - Repetition Penalty Range:
2048tokens - Presence / Frequency Penalty:
0.00
Quickstart Guide
Running via llama.cpp
# Serve as a local OpenAI-compatible API endpoint
llama-server \
-hf Abiray/Artemis-31B-v1.2-GGUF:Q4_K_M \
-c 8192 \
-ngl 99 \
--temp 0.95 \
--min-p 0.08 \
--repeat-penalty 1.06 \
--port 8080
Credits & Acknowledgments
- Original Model: TheDrummer for creating and fine-tuning Artemis-31B-v1.2.
- Base Architecture: Google DeepMind's Gemma 4 31B.
- Quantization Engine: llama.cpp by Georgi Gerganov and contributors.
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docker model run hf.co/Abiray/Artemis-31B-v1.2-GGUF: