Instructions to use schneewolflabs/B1-9B-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 schneewolflabs/B1-9B-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 schneewolflabs/B1-9B-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf schneewolflabs/B1-9B-GGUF:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf schneewolflabs/B1-9B-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf schneewolflabs/B1-9B-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 schneewolflabs/B1-9B-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf schneewolflabs/B1-9B-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 schneewolflabs/B1-9B-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf schneewolflabs/B1-9B-GGUF:Q8_0

Use Docker

docker model run hf.co/schneewolflabs/B1-9B-GGUF:Q8_0

LM Studio
Jan
Ollama
How to use schneewolflabs/B1-9B-GGUF with Ollama:
```
ollama run hf.co/schneewolflabs/B1-9B-GGUF:Q8_0
```
Unsloth Desktop

How to use schneewolflabs/B1-9B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf schneewolflabs/B1-9B-GGUF:Q8_0

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": "schneewolflabs/B1-9B-GGUF:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use schneewolflabs/B1-9B-GGUF with Docker Model Runner:
```
docker model run hf.co/schneewolflabs/B1-9B-GGUF:Q8_0
```

Lemonade

How to use schneewolflabs/B1-9B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull schneewolflabs/B1-9B-GGUF:Q8_0

Run and chat with the model

lemonade run user.B1-9B-GGUF-Q8_0

List all available models

lemonade list

Hermes Agent

How to use schneewolflabs/B1-9B-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 schneewolflabs/B1-9B-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 schneewolflabs/B1-9B-GGUF:Q8_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use schneewolflabs/B1-9B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf schneewolflabs/B1-9B-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 "schneewolflabs/B1-9B-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"

B1-9B — GGUF

Q8_0 (the quant every card number was measured on) and vision mmproj for schneewolflabs/B1-9B — the B0-9B iteration that answers after it thinks.

llama-server -m B1-9B-Q8_0.gguf -ngl 99 -c 8192 --jinja -fa on -np 1 \
    --spec-type draft-mtp --spec-draft-n-max 4 \
    --mmproj B1-9B-mmproj-f16.gguf

Same architecture and layer count as B0-9B, so the B0-9B-GGUF deployment guide — offload rules, f16 KV cache, 6GB recipes — applies unchanged. With thinking on, pass tool definitions through the native tools field and do not prefix /think; that request shape is where the answer-after-thinking gain is largest.