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
- llama.cpp
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 Desktop
- Pi
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 @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": "niccolasmunoz/personnn-buddy-9b-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- 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
- OpenClaw
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"
Use Docker
docker model run hf.co/niccolasmunoz/personnn-buddy-9b-GGUF:Q4_K_MPersonnn 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.
๐ผ 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 โ all on your machine, no cloud account required. It installs and runs this model for you with its bundled llama.cpp runtime.
โ Download PersonnnOS (free) at personnn.com
PersonnnOS in action
Buddy runs inside PersonnnOS โ a native browser, workspace and agent on your machine.
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.
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Base model
ornith-ai/Ornith-1.0-9B



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?" } ] }'