Instructions to use 1l2x-r/Buddy-Brain-Assets with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use 1l2x-r/Buddy-Brain-Assets 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 1l2x-r/Buddy-Brain-Assets
# Run inference directly in the terminal:
llama cli -hf 1l2x-r/Buddy-Brain-Assets

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf 1l2x-r/Buddy-Brain-Assets
# Run inference directly in the terminal:
llama cli -hf 1l2x-r/Buddy-Brain-Assets

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 1l2x-r/Buddy-Brain-Assets
# Run inference directly in the terminal:
./llama-cli -hf 1l2x-r/Buddy-Brain-Assets

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 1l2x-r/Buddy-Brain-Assets
# Run inference directly in the terminal:
./build/bin/llama-cli -hf 1l2x-r/Buddy-Brain-Assets

Use Docker

docker model run hf.co/1l2x-r/Buddy-Brain-Assets

LM Studio
Jan
Ollama
How to use 1l2x-r/Buddy-Brain-Assets with Ollama:
```
ollama run hf.co/1l2x-r/Buddy-Brain-Assets
```
Unsloth Desktop

How to use 1l2x-r/Buddy-Brain-Assets with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf 1l2x-r/Buddy-Brain-Assets

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": "1l2x-r/Buddy-Brain-Assets"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use 1l2x-r/Buddy-Brain-Assets with Docker Model Runner:
```
docker model run hf.co/1l2x-r/Buddy-Brain-Assets
```

Lemonade

How to use 1l2x-r/Buddy-Brain-Assets with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull 1l2x-r/Buddy-Brain-Assets

Run and chat with the model

lemonade run user.Buddy-Brain-Assets-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use 1l2x-r/Buddy-Brain-Assets with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf 1l2x-r/Buddy-Brain-Assets

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 1l2x-r/Buddy-Brain-Assets

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use 1l2x-r/Buddy-Brain-Assets with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf 1l2x-r/Buddy-Brain-Assets

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 "1l2x-r/Buddy-Brain-Assets" \
  --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"

🦙 Llama-3.2-1B-Instruct (Q4_K_M, GGUF)

This repository hosts the quantized GGUF version of Llama‑3.2‑1B‑Instruct (llama-3.2-1b-instruct-q4_k_m.gguf).
It is optimized for lightweight inference and suitable for mobile and desktop devices.

🚀 Quick Start

Run with llama.cpp

Download the model and run it using llama.cpp:

wget https://huggingface.co/1l2x-r/llama-3.2-1b-instruct-q4_k_m.gguf/resolve/main/llama-3.2-1b-instruct-q4_k_m.gguf

./main -m llama-3.2-1b-instruct-q4_k_m.gguf -p "Hello, world!"

Downloads last month: 225

GGUF

Model size

2B params

Architecture

qwen2

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

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