Instructions to use Baekpica/Motif-3-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Baekpica/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 Baekpica/Motif-3-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf Baekpica/Motif-3-GGUF:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Baekpica/Motif-3-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf Baekpica/Motif-3-GGUF:Q8_0

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 Baekpica/Motif-3-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf Baekpica/Motif-3-GGUF:Q8_0

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 Baekpica/Motif-3-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Baekpica/Motif-3-GGUF:Q8_0

Use Docker

docker model run hf.co/Baekpica/Motif-3-GGUF:Q8_0

LM Studio
Jan

vLLM

How to use Baekpica/Motif-3-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Baekpica/Motif-3-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Baekpica/Motif-3-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Baekpica/Motif-3-GGUF:Q8_0

Ollama
How to use Baekpica/Motif-3-GGUF with Ollama:
```
ollama run hf.co/Baekpica/Motif-3-GGUF:Q8_0
```

Unsloth Studio

How to use Baekpica/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 Baekpica/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 Baekpica/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 Baekpica/Motif-3-GGUF to start chatting

How to use Baekpica/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 Baekpica/Motif-3-GGUF:Q8_0

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": "Baekpica/Motif-3-GGUF:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use Baekpica/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 Baekpica/Motif-3-GGUF:Q8_0

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 "Baekpica/Motif-3-GGUF:Q8_0" \
  --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 Baekpica/Motif-3-GGUF with Docker Model Runner:
```
docker model run hf.co/Baekpica/Motif-3-GGUF:Q8_0
```

Lemonade

How to use Baekpica/Motif-3-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Baekpica/Motif-3-GGUF:Q8_0

Run and chat with the model

lemonade run user.Motif-3-GGUF-Q8_0

List all available models

lemonade list

Hermes Agent

How to use Baekpica/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 Baekpica/Motif-3-GGUF:Q8_0

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 Baekpica/Motif-3-GGUF:Q8_0

Run Hermes

hermes

Atomic Chat

Baekpica commited on 2 days ago

Commit

5c266c9

verified ·

1 Parent(s): 590e3f6

Clarify runtime and reproduction handoff status

Browse files

Files changed (1) hide show

README.md +7 -3

README.md CHANGED Viewed

@@ -63,7 +63,8 @@ Q8_0; decision-sensitive controls remain at their locked higher precision.
 | Native context metadata | 262,144 tokens |
 | Official implementation oracle | `MotifTechnologies/vllm@4cd9eb4129883565e69d508038d783d59ee01867` |
 | Conversion base | `ggml-org/llama.cpp@1d2869c6e54d5003f3927a79efbca0fefa034a6d` |
-| Runtime/reproduction | [`Baekpica/ds4`](https://github.com/Baekpica/ds4), [`Baekpica/motif-3-mixed-ds4`](https://github.com/Baekpica/motif-3-mixed-ds4) |
 Only the official final Motif-3 checkpoint above was used. Motif-3-Beta was
 not used as a source, calibration input, implementation oracle, or fallback.
@@ -105,8 +106,11 @@ GDLA/differential heads and output gate, modified mHC, interleaved SWA/full
 attention with YaRN, latent KV semantics, and MTP. Do not assume that a stock
 GGUF runtime recognizes `motif3` merely because it can parse the container.
-The target runtime is
-[`Baekpica/ds4:feature/motif-3-model-loader`](https://github.com/Baekpica/ds4/tree/feature/motif-3-model-loader).
 ## Intended use in the mixed-quant pipeline

 | Native context metadata | 262,144 tokens |
 | Official implementation oracle | `MotifTechnologies/vllm@4cd9eb4129883565e69d508038d783d59ee01867` |
 | Conversion base | `ggml-org/llama.cpp@1d2869c6e54d5003f3927a79efbca0fefa034a6d` |
+| Runtime base | [`Baekpica/ds4`](https://github.com/Baekpica/ds4) |
+| H200 reproduction state | Preserved in the private `Baekpica/motif-3-spark-handoff` bucket |
 Only the official final Motif-3 checkpoint above was used. Motif-3-Beta was
 not used as a source, calibration input, implementation oracle, or fallback.
 attention with YaRN, latent KV semantics, and MTP. Do not assume that a stock
 GGUF runtime recognizes `motif3` merely because it can parse the container.
+The H200 development branch is named `feature/motif-3-model-loader` on the
+[`Baekpica/ds4`](https://github.com/Baekpica/ds4) base. Its exact current
+uncommitted state is preserved as a patch plus complete touched sources in the
+private Spark handoff; this Q8 artifact does not claim that a public remote
+feature branch has been released.
 ## Intended use in the mixed-quant pipeline