🧠 Marziel AI — v0.5.9

Private Local Intelligence — runs entirely on your hardware.

Marziel AI is a locally-powered AI assistant with real-time maritime intelligence, web search, browser automation, GitHub analysis, URL reading, code generation, and autonomous agent capabilities — all without sending a single byte to the cloud.

Quick Install

pip install marziel
marziel serve

That's it. The right engine is auto-installed for your hardware. The first run downloads the model from Hugging Face. Everything is automatic.

Changelog

v0.5.9

Semantic routing — intent detection via vector similarity (~5ms), replaces brittle keyword matching
8 intent routes — chat, web_search, agent, browser, maritime, code, github, url
fastembed BGE-small — local embeddings, no API calls, 95% routing accuracy
Graceful degradation — falls back to keywords if semantic-router not installed
Install: pip install marziel[router] for semantic routing

v0.5.8

3-tier inference — Apple Silicon → MLX, NVIDIA GPU → vLLM, CPU → llama.cpp
llama.cpp for CPU — uses same 4.9GB GGUF file as GPU, no more vLLM CPU format issues
Auto-install engines — mlx-lm, vllm, or llama-cpp-python[server] detected + installed automatically
Chat-first UI — opens directly to chat interface (no landing page)
Python 3.14 support — tested on 3.10–3.14
Works on any Linux — Arch, Fedora, RHEL, Ubuntu, Debian + macOS + WSL

v0.5.7

CPU uses safetensors — vLLM CPU loads model.safetensors from HF (GGUF not supported on CPU)
GPU uses GGUF — faster local loading with quantized model
Fixed tokenizer — PreTrainedTokenizerFast in HF tokenizer_config.json

v0.5.6

Clean install logic — only installs vLLM if missing; detects GPU → CUDA, no GPU → CPU wheel
No forced uninstalls — respects existing user environment

v0.5.5

Tokenizer fix — --trust-remote-code for custom tokenizer loading
Clean CPU install — auto-uninstalls CUDA vllm/torch before installing CPU wheel

v0.5.4

vLLM CPU pre-built wheel — auto-installs vllm+cpu from GitHub releases on CPU-only machines
GPU auto-detects — installs standard CUDA vLLM when NVIDIA GPU present
CPU optimized — bfloat16 dtype, VLLM_CPU_KVCACHE_SPACE, TCMalloc, Intel OpenMP
3-minute CPU timeout — 180s for CPU model loading

v0.5.3

Fixed CPU device detection — set VLLM_TARGET_DEVICE=cpu for machines without GPU
vLLM debug logging — errors now saved to ~/.marziel/vllm.log

v0.5.2

Prefix caching — system prompt KV cache reused across requests (faster responses)
120s CPU timeout — longer startup window for CPU-only machines loading 4.6GB model
All NVIDIA GPUs supported (GTX, RTX, Tesla, A100) — auto-detected via nvidia-smi

v0.5.1

vLLM auto-installs — no more manual pip install vllm, it's a declared dependency
8GB RAM optimized — reduced context to 2048 tokens, eager execution, swap-space fallback
Engine routing — Apple Silicon → MLX, everything else → vLLM (CPU & GPU)

v0.5.0

Unified architecture: MLX (Apple Silicon) + vLLM (all other platforms)
GGUF Q4_K_M model for vLLM (~4.6 GB)
Auto-download model from Hugging Face
Zero-config: pip install marziel && marziel serve

What It Does

Feature	Description
🧠 AI Chat	Conversational AI with personality, code generation, data analysis
📡 Maritime Intelligence	Real-time GDACS, GDELT, NASA EONET, AIS, market data, bunker prices
💰 Market Data	Live Brent crude, BDI, EU Carbon, freight rates, forex, 12-port bunker prices
🚢 Vessel Integration	AIS tracking, fleet management, voyage data, maintenance records
🔌 Third-Party Connectors	Plug in MarineTraffic, VesselFinder, Spire, Datalastic, or any REST API
📊 Live Reports	Generate comprehensive maritime intelligence reports on demand
🔍 Web Search	Live internet results via DuckDuckGo — always up-to-date answers
🌐 Browser Control	Headless Chromium automation — navigate, click, type, screenshot
🔗 URL Analysis	Share any link — reads, extracts, and analyzes content
🐙 GitHub Analysis	Paste any repo URL — get architecture, languages, README analysis
⚡ Autonomous Agent	Multi-step reasoning with bash, file, web, browser, and maritime tools
🔒 100% Private	Everything runs locally. Zero cloud dependency

Maritime Intelligence (Enterprise)

Marziel integrates real-time maritime data from multiple global sources:

┌───────────────────────────────────────────────────────────┐
│                   MARZIEL MARITIME INTEL                   │
├───────────────────────────────────────────────────────────┤
│  OSINT (Free APIs)         │  Platform Integration        │
│  ─────────────────         │  ────────────────────        │
│  📡 GDACS — Disasters      │  🚢 Fleet Vessels            │
│  📰 GDELT — Geopolitics    │  🗺️  Active Voyages          │
│  🌍 NASA EONET — Climate   │  ⚠️  Risk Zones              │
│  💰 Yahoo Finance — Market │  🏗️  Maintenance             │
│  💱 ExchangeRate — Forex   │  📋 Incidents                │
│  🚢 AISStream — Tracking   │  ⛽ Fuel Records             │
│                            │  🏭 Port Directory           │
├───────────────────────────────────────────────────────────┤
│  Third-Party Connectors (Enterprise)                      │
│  ─────────────────────────────────                        │
│  MarineTraffic · VesselFinder · Spire Maritime            │
│  Datalastic · OpenWeather · Custom REST API               │
└───────────────────────────────────────────────────────────┘

Market Data Sources

Data	Source	Update
Brent Crude Oil	Yahoo Finance (BZ=F)	Real-time
Bunker VLSFO/MGO/HSFO	Brent-derived (12 ports)	Real-time
Baltic Dry Index	BDRY ETF × 200	Real-time
EU Carbon (EUA)	KRBN ETF × 2.1	Real-time
Freight Rates	DSX/GNK shipping stocks	Real-time
Exchange Rates	ExchangeRate API (9 currencies)	Real-time

Third-Party Integration

Connect Marziel to any maritime service via ~/.marziel/connectors.json:

{
  "connectors": [
    {"type": "marinetraffic", "api_key": "YOUR_KEY"},
    {"type": "openweather", "api_key": "YOUR_KEY"},
    {
      "type": "custom",
      "name": "my_fleet",
      "base_url": "https://api.myfleet.com/v1",
      "api_key": "secret",
      "endpoints": {
        "vessels": "/ships",
        "voyages": "/trips",
        "weather": "/weather?lat={lat}&lon={lon}"
      }
    }
  ]
}

Maritime API Endpoints

GET  /api/maritime/report           — Full intelligence report
GET  /api/maritime/data/market      — Live market data
GET  /api/maritime/data/bunker      — 12-port bunker prices
GET  /api/maritime/data/fx          — Exchange rates
GET  /api/maritime/data/gdacs       — Disaster events
GET  /api/maritime/data/gdelt       — Geopolitical news
GET  /api/maritime/data/nasa        — Natural events
GET  /api/maritime/data/ais         — Vessel tracking
GET  /api/maritime/data/vessels     — Fleet data
GET  /api/maritime/data/voyages     — Voyage data
GET  /api/maritime/data/risk-zones  — Active risk zones
GET  /api/maritime/data/ports       — Port directory
GET  /api/maritime/connectors       — List connectors
POST /api/maritime/connectors/query — Query third-party services

Agent Tools

The LLM has access to 20+ tools:

Tool	Purpose
`bash`	Shell command execution
`file_read` / `file_write`	File I/O
`web_search`	DuckDuckGo search
`browser`	Headless Chromium automation
`maritime_report`	Full intelligence report
`market_data`	Brent, BDI, Carbon, bunker, freight
`gdacs`	Active disasters & cyclones
`gdelt`	Geopolitical conflict news
`nasa`	NASA EONET natural events
`ais`	Live AIS vessel tracking
`fleet`	Fleet vessel data
`voyages`	Active voyage data
`risk_zones`	Active risk zones
`ports`	Global port directory
`exchange_rates`	Live forex rates
`maintenance`	Vessel maintenance records
`incidents`	Maritime incident reports
`fuel`	Fuel consumption data
`port_calls`	Port call history

Requirements

RAM: 8GB minimum (16GB recommended)
Disk: ~5GB for the model
Python: 3.10–3.14
Supported platforms:

Platform	Engine	Model	Auto-installed
Apple Silicon (M1–M4)	MLX	Fused float16	`mlx-lm`
NVIDIA GPU (any)	vLLM	GGUF Q4_K_M	`vllm`
CPU (any x86/ARM)	llama.cpp	GGUF Q4_K_M	`llama-cpp-python[server]`

Linux: Ubuntu, Debian, Arch, Fedora, RHEL, Alpine — any distro with glibc
macOS: Intel (llama.cpp) or Apple Silicon (MLX)
Windows: WSL2 recommended

Usage

# Install and start (basic)
pip install marziel
marziel serve

# With browser automation
pip install "marziel[browser]"
python -m playwright install chromium
marziel serve

# Check status
marziel status

# Show version
marziel version

Then open http://localhost:8001 — your private AI dashboard.

Architecture

┌──────────────────────────────────────────────┐
│  Browser (localhost:8001)                     │
│  ┌────────────────────────────────────────┐   │
│  │  Marziel Dashboard (React)            │   │
│  │  Dashboard · Chat · Settings          │   │
│  └──────────────┬─────────────────────────┘   │
│                 │ API calls                    │
│  ┌──────────────▼─────────────────────────┐   │
│  │  Marziel Backend (Flask+Gunicorn)     │   │
│  │  Agent · Search · Browser · Maritime  │   │
│  │  Billing · Connectors · Reports       │   │
│  └──────┬───────┬───────┬─────────────────┘   │
│         │       │       │                      │
│  ┌──────▼──┐ ┌──▼────┐ ┌▼──────────────────┐  │
│  │ LLM     │ │ OSINT │ │ Third-Party APIs   │  │
│  │ MLX     │ │ GDACS │ │ MarineTraffic      │  │
│  │ vLLM    │ │ GDELT │ │ VesselFinder       │  │
│  │ llama   │ │ NASA  │ │ Spire · Custom     │  │
│  │ .cpp    │ │ Yahoo │ │                    │  │
│  └─────────┘ └───────┘ └────────────────────┘  │
│                YOUR MACHINE                     │
└──────────────────────────────────────────────┘

Pricing

Plan	Price	Features
Free	€0/mo	AI Chat, Web Search, URL Analysis, GitHub Analysis
Pro	€20/mo	+ Agent Mode, Browser Control, Priority Inference
Enterprise	€199/mo	+ Maritime Intelligence, Connectors, Reports, Custom Fine-tuning

Contact

📧 info@efkan-isazade.com

License

MIT License

Built with ❤️ by Efe (Efkan Isazade)

Downloads last month: 744

Safetensors

Model size

2B params

Tensor type

BF16

I64

I32

MLX

Hardware compatibility

4-bit

Model tree for efops/marziel-8b-custom

Base model

meta-llama/Llama-3.1-8B

Finetuned

meta-llama/Llama-3.1-8B-Instruct

Quantized

(604)

this model

efops
/

marziel-8b-custom