Instructions to use shafire/OpenZero-SkyPilot-3.8B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use shafire/OpenZero-SkyPilot-3.8B-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="shafire/OpenZero-SkyPilot-3.8B-GGUF")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("shafire/OpenZero-SkyPilot-3.8B-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use shafire/OpenZero-SkyPilot-3.8B-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 shafire/OpenZero-SkyPilot-3.8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf shafire/OpenZero-SkyPilot-3.8B-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 shafire/OpenZero-SkyPilot-3.8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf shafire/OpenZero-SkyPilot-3.8B-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 shafire/OpenZero-SkyPilot-3.8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf shafire/OpenZero-SkyPilot-3.8B-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 shafire/OpenZero-SkyPilot-3.8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf shafire/OpenZero-SkyPilot-3.8B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/shafire/OpenZero-SkyPilot-3.8B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use shafire/OpenZero-SkyPilot-3.8B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "shafire/OpenZero-SkyPilot-3.8B-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": "shafire/OpenZero-SkyPilot-3.8B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/shafire/OpenZero-SkyPilot-3.8B-GGUF:Q4_K_M

SGLang

How to use shafire/OpenZero-SkyPilot-3.8B-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "shafire/OpenZero-SkyPilot-3.8B-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "shafire/OpenZero-SkyPilot-3.8B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "shafire/OpenZero-SkyPilot-3.8B-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "shafire/OpenZero-SkyPilot-3.8B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use shafire/OpenZero-SkyPilot-3.8B-GGUF with Ollama:
```
ollama run hf.co/shafire/OpenZero-SkyPilot-3.8B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use shafire/OpenZero-SkyPilot-3.8B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf shafire/OpenZero-SkyPilot-3.8B-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": "shafire/OpenZero-SkyPilot-3.8B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use shafire/OpenZero-SkyPilot-3.8B-GGUF with Docker Model Runner:
```
docker model run hf.co/shafire/OpenZero-SkyPilot-3.8B-GGUF:Q4_K_M
```

Lemonade

How to use shafire/OpenZero-SkyPilot-3.8B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull shafire/OpenZero-SkyPilot-3.8B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.OpenZero-SkyPilot-3.8B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use shafire/OpenZero-SkyPilot-3.8B-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 shafire/OpenZero-SkyPilot-3.8B-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 shafire/OpenZero-SkyPilot-3.8B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use shafire/OpenZero-SkyPilot-3.8B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf shafire/OpenZero-SkyPilot-3.8B-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 "shafire/OpenZero-SkyPilot-3.8B-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"

OpenZero SkyPilot 3.8B GGUF

Private research release — a verified local GGUF for simulator-first aerial logistics and mission-proposal research. It is not a certified flight controller or deployment-ready autonomy system.

OpenZero SkyPilot 3.8B is an experimental mission-level aerial-vehicle proposal model derived from the pinned microsoft/Phi-4-mini-instruct revision cfbefacb99257ffa30c83adab238a50856ac3083.

The model is intended to produce typed, reviewable mission proposals for integration research around MAVLink-compatible simulators such as PX4 SITL and ArduPilot SITL. Stabilization, geofencing, collision avoidance, flight envelopes, return-to-home, emergency handling and operator authority remain with deterministic systems outside the LLM.

Verified GGUF artifact

Field	Verified value
File	`OpenZero-SkyPilot-3.8B-Q4_K_M.gguf`
Quantization	Q4_K_M
Size	2,493,840,096 bytes
SHA-256	`10e3dad8684d74cd62162c497e27bf7c0ed62a22818d16795b163330e04dffd7`
llama.cpp build	b10451
llama.cpp commit	`10bf611e533d81f739128304991c5e133c6aebd8`
Runtime gate	Passed: real GGUF inference, 8 generated tokens
Observed CPU generation	5.6 tokens/s in the recorded gate

The machine-readable verification record is included as OpenZero-SkyPilot-3.8B-GGUF-Evidence.json.

Research use cases

simulator-first aerial logistics and approved package-handling proposals
waypoint, altitude and return-to-home mission-envelope testing
MAVLink/PX4/ArduPilot adapter conformance research
infrastructure inspection and environmental survey planning
fault injection, degraded-link and abort-policy evaluation
typed mission proposals for independent validation and operator approval

SkyPilot is a language-level mission proposer. It does not replace a flight controller, state estimator, collision-avoidance system or qualified operator.

Training and provenance

Base: microsoft/Phi-4-mini-instruct, exact revision pinned above.
Finite two-step QLoRA smoke run on a Tesla T4.
32 deterministic locally authored training cases.
6 disjoint validation cases.
Recorded training loss: 4.62113618850708.
Adapter SHA-256: 1296b817cd2d2063bb23f348361e9a77c604e9742c10b18eb43b61d62fba6f38.
Excluded: Fusion data, teacher outputs, locked evaluation, rejected data, Ministral data and failed Gemma-31B checkpoints.

This deliberately small smoke dataset establishes pipeline integrity and artifact reproducibility. It does not establish broad aerial competence, safety certification or superiority over the exact base model.

Run with llama.cpp

llama-cli \
  -m OpenZero-SkyPilot-3.8B-Q4_K_M.gguf \
  -cnv \
  -p "Propose a simulator-only inspection mission as typed JSON."

Treat every output as untrusted proposed data. Validate it against a signed capability manifest, schema, geofence, vehicle limits, current telemetry and explicit operator authority before any simulator adapter accepts it.

Integration boundary

operator intent
    -> SkyPilot typed mission proposal
    -> schema and capability validation
    -> policy, geofence and fault checks
    -> operator approval
    -> MAVLink simulator adapter
    -> PX4/ArduPilot SITL deterministic control

Approved package pickup or delivery research must use declared benign payload constraints, verified pickup/drop zones and operator-controlled abort behavior.

Explicit exclusions

SkyPilot is not designed for weapons or harmful payloads, person targeting or pursuit, threat engagement, evasion, interference, unauthorised control, raw motor/actuator access, flight-controller replacement or safety-system bypass.

Status and claims

experimental research artifact. No MOD, UKRI, OpenAI, Microsoft or other institutional approval, certification, procurement or endorsement is claimed.

Downloads last month: 64

GGUF

Model size

4B params

Architecture

phi3

Hardware compatibility

4-bit

Model tree for shafire/OpenZero-SkyPilot-3.8B-GGUF

Base model

microsoft/Phi-4-mini-instruct

Quantized

(197)

this model