Instructions to use AesSedai/Motif-3-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 AesSedai/Motif-3-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 AesSedai/Motif-3-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf AesSedai/Motif-3-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 AesSedai/Motif-3-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf AesSedai/Motif-3-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 AesSedai/Motif-3-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf AesSedai/Motif-3-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 AesSedai/Motif-3-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf AesSedai/Motif-3-GGUF:Q4_K_M
Use Docker
docker model run hf.co/AesSedai/Motif-3-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use AesSedai/Motif-3-GGUF with Ollama:
ollama run hf.co/AesSedai/Motif-3-GGUF:Q4_K_M
- Unsloth Studio
How to use AesSedai/Motif-3-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 AesSedai/Motif-3-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 AesSedai/Motif-3-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for AesSedai/Motif-3-GGUF to start chatting
- Pi
How to use AesSedai/Motif-3-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf AesSedai/Motif-3-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": "AesSedai/Motif-3-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- OpenClaw new
How to use AesSedai/Motif-3-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf AesSedai/Motif-3-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 "AesSedai/Motif-3-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"
- Docker Model Runner
How to use AesSedai/Motif-3-GGUF with Docker Model Runner:
docker model run hf.co/AesSedai/Motif-3-GGUF:Q4_K_M
- Lemonade
How to use AesSedai/Motif-3-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull AesSedai/Motif-3-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Motif-3-GGUF-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use AesSedai/Motif-3-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 AesSedai/Motif-3-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 AesSedai/Motif-3-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat
Requires this PR to run.
This repo contains specialized MoE-quants for Motif-Technologies/Motif-3. The idea being that given the huge size of the FFN tensors compared to the rest of the tensors in the model, it should be possible to achieve a better quality while keeping the overall size of the entire model smaller compared to a similar naive quantization. To that end, the quantization type default is kept in high quality and the FFN UP + FFN GATE tensors are quanted down along with the FFN DOWN tensors.
| Quant | Size | Mixture | PPL | 1-(Mean PPL(Q)/PPL(base)) | KLD |
|---|---|---|---|---|---|
| Q8_0 | 314.95 GiB (8.60 BPW) | Q8_0 | 35.367303 ยฑ 0.451587 | +2.8870% | 0.010134 ยฑ 0.000061 |
| Q5_K_M | 220.07 GiB (6.01 BPW) | Q8_0 / Q5_K / Q5_K / Q6_K | 35.336996 ยฑ 0.450859 | +2.7989% | 0.015087 ยฑ 0.000088 |
| Q4_K_M | 183.47 GiB (5.01 BPW) | Q8_0 / Q4_K / Q4_K / Q5_K | 35.635889 ยฑ 0.455428 | +3.6684% | 0.026133 ยฑ 0.000135 |
| IQ4_XS | 143.12 GiB (3.91 BPW) | Q8_0 / IQ3_S / IQ3_S / IQ4_XS | 36.272353 ยฑ 0.463193 | +5.5199% | 0.058303 ยฑ 0.000287 |
| IQ3_M | 123.79 GiB (3.38 BPW) | Q6_K / IQ3_XXS / IQ3_XXS / IQ3_S | 38.364498 ยฑ 0.492873 | +11.6062% | 0.103530 ยฑ 0.000507 |
| IQ3_S | 111.84 GiB (3.05 BPW) | Q6_K / IQ2_S / IQ2_S / IQ3_S | 39.318385 ยฑ 0.502525 | +14.3811% | 0.157063 ยฑ 0.000752 |
| IQ2_S | 101.38 GiB (2.77 BPW) | Q6_K / IQ2_XS / IQ2_XS / IQ3_XXS | 41.701314 ยฑ 0.536307 | +21.3133% | 0.222056 ยฑ 0.001044 |
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Model tree for AesSedai/Motif-3-GGUF
Base model
Motif-Technologies/Motif-3-Base
