Instructions to use niccolasmunoz/personnn-buddy-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 niccolasmunoz/personnn-buddy-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 niccolasmunoz/personnn-buddy-9b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf niccolasmunoz/personnn-buddy-9b-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 niccolasmunoz/personnn-buddy-9b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf niccolasmunoz/personnn-buddy-9b-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 niccolasmunoz/personnn-buddy-9b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf niccolasmunoz/personnn-buddy-9b-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 niccolasmunoz/personnn-buddy-9b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf niccolasmunoz/personnn-buddy-9b-GGUF:Q4_K_M

Use Docker

docker model run hf.co/niccolasmunoz/personnn-buddy-9b-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use niccolasmunoz/personnn-buddy-9b-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "niccolasmunoz/personnn-buddy-9b-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": "niccolasmunoz/personnn-buddy-9b-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/niccolasmunoz/personnn-buddy-9b-GGUF:Q4_K_M

Ollama
How to use niccolasmunoz/personnn-buddy-9b-GGUF with Ollama:
```
ollama run hf.co/niccolasmunoz/personnn-buddy-9b-GGUF:Q4_K_M
```

Unsloth Studio

How to use niccolasmunoz/personnn-buddy-9b-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 niccolasmunoz/personnn-buddy-9b-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 niccolasmunoz/personnn-buddy-9b-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for niccolasmunoz/personnn-buddy-9b-GGUF to start chatting

How to use niccolasmunoz/personnn-buddy-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 niccolasmunoz/personnn-buddy-9b-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": "niccolasmunoz/personnn-buddy-9b-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use niccolasmunoz/personnn-buddy-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 niccolasmunoz/personnn-buddy-9b-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 "niccolasmunoz/personnn-buddy-9b-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 niccolasmunoz/personnn-buddy-9b-GGUF with Docker Model Runner:
```
docker model run hf.co/niccolasmunoz/personnn-buddy-9b-GGUF:Q4_K_M
```

Lemonade

How to use niccolasmunoz/personnn-buddy-9b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull niccolasmunoz/personnn-buddy-9b-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.personnn-buddy-9b-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use niccolasmunoz/personnn-buddy-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 niccolasmunoz/personnn-buddy-9b-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 niccolasmunoz/personnn-buddy-9b-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

Personnn Buddy 9B GGUF

Buddy is PersonnnOS's local language model. It is tuned for conversational assistance, tool use and desktop workflows in Spanish and English.

This repository contains the Q4_K_M GGUF build for local inference with PersonnnOS, llama.cpp or Ollama.

🐼 Give Buddy a body. This is just the brain. PersonnnOS is the sovereign desktop app where Buddy lives: a native browser, workspace, tools and agent — all on your machine, no cloud account required. It installs and runs this model for you with its bundled llama.cpp runtime.

→ Download PersonnnOS (free) at personnn.com

PersonnnOS in action

Buddy runs inside PersonnnOS — a native browser, workspace and agent on your machine.



Buddy, local and adaptive — presence, appearance and behavior under your control.	Private AI on your VPS — run Buddy on your own infrastructure; data never touches third parties.

Brain — reasons over your project's files, relations and memory.	Personal Life (preview) — your agent lives in a shared city: journals, building, trade.

Model details

Property	Value
Base model	deepreinforce-ai/Ornith-1.0-9B
Architecture	Qwen3.5, 9B parameters
Fine-tuning	LoRA/SFT for PersonnnOS workflows
Training examples	397 curated examples
Quantization	Q4_K_M
File size	5.24 GiB
Recommended context	12,288 tokens
License	MIT

Download

Download personnn-buddy-9b-v2-Q4_K_M.gguf from this repository. PersonnnOS can install and run it with its bundled llama.cpp runtime, without Ollama or a cloud account.

Verify the file after downloading:

shasum -a 256 personnn-buddy-9b-v2-Q4_K_M.gguf

Expected SHA-256:

888bb1b58795277abc2371065f88f6f03e4777d881385720416e328ba14809fa

llama.cpp

llama-server \
  -m personnn-buddy-9b-v2-Q4_K_M.gguf \
  --ctx-size 12288 \
  --flash-attn on \
  --host 127.0.0.1 \
  --port 8080

Ollama

Keep Modelfile and the GGUF in the same directory, then run:

ollama create personnn-buddy:9b-v2 -f Modelfile
ollama run personnn-buddy:9b-v2

The included Modelfile defines the intended Buddy identity, context length and sampling defaults.

Evaluation

Buddy v2 scored 11/12 (92%) in PersonnnOS's internal workflow evaluation, with an average first-token latency of 6.9 seconds on the test machine. The suite covers chat, structured tool selection and desktop tasks. These results are internal product measurements, not a general-purpose benchmark.

Intended use

Local conversational assistance.
PersonnnOS tools and desktop workflows.
Spanish-first personal productivity.
Private on-device inference when run through a local runtime.

The model does not itself enforce permissions. PersonnnOS places tool calls behind its permission broker and requires confirmation for consequential actions.

Limitations

The model can hallucinate or select an incorrect tool.
Tool availability and schemas are supplied by the host application.
Outputs are not professional legal, medical or financial advice.
External, destructive or irreversible actions should require explicit user confirmation.
Local privacy depends on the runtime and application configuration. Using a cloud-hosted runtime sends prompts to that service.

Provenance

Personnn Buddy 9B is derived from Ornith-1.0-9B, published by deepreinforce-ai under the MIT license. See THIRD_PARTY_NOTICES.md.

About PersonnnOS

PersonnnOS is a privacy-first personal agent and browser. Learn more at personnn.com.

Downloads last month: 168

GGUF

Model size

9B params

Architecture

qwen35

Hardware compatibility

4-bit

Model tree for niccolasmunoz/personnn-buddy-9b-GGUF

Base model

ornith-ai/Ornith-1.0-9B

Quantized

(101)

this model