Instructions to use 1972521116s/mindbaton-brain-2b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use 1972521116s/mindbaton-brain-2b 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 1972521116s/mindbaton-brain-2b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf 1972521116s/mindbaton-brain-2b:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf 1972521116s/mindbaton-brain-2b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf 1972521116s/mindbaton-brain-2b: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 1972521116s/mindbaton-brain-2b:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf 1972521116s/mindbaton-brain-2b: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 1972521116s/mindbaton-brain-2b:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf 1972521116s/mindbaton-brain-2b:Q4_K_M

Use Docker

docker model run hf.co/1972521116s/mindbaton-brain-2b:Q4_K_M

LM Studio
Jan
Ollama
How to use 1972521116s/mindbaton-brain-2b with Ollama:
```
ollama run hf.co/1972521116s/mindbaton-brain-2b:Q4_K_M
```
Unsloth Desktop

How to use 1972521116s/mindbaton-brain-2b with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf 1972521116s/mindbaton-brain-2b: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": "1972521116s/mindbaton-brain-2b:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use 1972521116s/mindbaton-brain-2b with Docker Model Runner:
```
docker model run hf.co/1972521116s/mindbaton-brain-2b:Q4_K_M
```

Lemonade

How to use 1972521116s/mindbaton-brain-2b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull 1972521116s/mindbaton-brain-2b:Q4_K_M

Run and chat with the model

lemonade run user.mindbaton-brain-2b-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use 1972521116s/mindbaton-brain-2b with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf 1972521116s/mindbaton-brain-2b: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 1972521116s/mindbaton-brain-2b:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use 1972521116s/mindbaton-brain-2b with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf 1972521116s/mindbaton-brain-2b: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 "1972521116s/mindbaton-brain-2b: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"

Mindbaton brain 2B

Qwen/Qwen3.5-2B fine-tuned (LoRA) to read one chat message into the lasting facts it states about the writer or the people they name, as JSON. It runs in the background in Mindbaton through llama.cpp; Mindbaton's rules check every fact before it is saved. Trained only on made-up examples (Mindbaton's templates and chat messages written by gpt-oss-120b), never on anyone's messages.

Scores on Mindbaton's test sets (sentences it never saw): {"everyday phrasings": {"facts": "29/30", "no false facts": "18/18", "taken back": "5/5"}, "fresh set 2": {"facts": "25/30", "no false facts": "18/20", "taken back": "6/6"}, "fresh set 3": {"facts": "28/30", "no false facts": "15/15", "taken back": "6/6"}}

Downloads last month: -

GGUF

Model size

2B params

Architecture

qwen35

Hardware compatibility

4-bit

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Model tree for 1972521116s/mindbaton-brain-2b

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