TheOrc v1.12.0
TheOrc v1.12.0
100% local AI coding assistant — no cloud, no subscriptions, no data leaving your machine.
What's new in v1.12.0
TheOrc Foundry ships its first specialist model. theorc-toolcaller — Qwen2.5-1.5B fine-tuned to propose the correct tool call (or correctly refuse) from a worker's role, its available tools, and a natural-language request — went from spec to a promoted, benchmarked, deployed model in one release cycle. The full pipeline landed in the Training Pit: a synthetic dataset generator with decision-type balance guarantees, a gated LoRA training runner (config-driven, immutable run manifests, GPU exclusivity enforced against every other Foundry consumer), and a new Stage 4 ARENA panel that benchmarks decision accuracy, tool precision, and per-class F1 live against a sealed 260-example held-out set.
A statistically honest refusal benchmark, not a vibe check. Alongside the sealed Arena set, a new Refusal Gauntlet generates thousands of deterministic adversarial cases across six failure families — foreign tools, out-of-role requests, near-miss tools, prompt injection, missing arguments, and ordinary no-tool conversation — and scores them with exact Clopper-Pearson confidence bounds and paraphrase-consistency checks, so the reported number is the defensible lower bound, not an optimistic point estimate. The gap it found got closed: retraining from r2 to r3 raised sealed-eval decision accuracy 97.3% → 98.5%, and gauntlet safety (never fabricating a tool call) 90.3% → 98.3% on held-out phrasings the model never trained on.
The trained specialist is live, opt-in, and learning from real use. theorc-toolcaller:qwen25-1.5b is deployed via Ollama and wired into the Swarm worker loop as an opt-in repair lane — when a worker's response contains no parseable tool call, the specialist gets one shot at proposing one before the turn falls through to today's behavior, still gated by the same deterministic tool-policy engine as every other call. Real usage now feeds the next training round from two organic sources, both off by default under one settings toggle: Swarm tool-call decisions, and — new this release — OrcChat single-agent chat decisions, captured under a wider "v1" tool inventory that's a deliberate sibling to Swarm's frozen six-tool set, not an edit to it.
🪟 Windows — one-click install (recommended)
- Download OrchestratorSetup.exe below
- Run it — the wizard detects your GPU, downloads the right runtime and model, configures everything
- Launch from the Desktop shortcut or Start Menu
Portable: download TheOrc-1.12.0-win-x64-portable.zip instead — contains both OrchestratorIDE.exe and OrchestratorSetup.exe, extract both to the same folder, run OrchestratorIDE.exe.
🍎 macOS (Apple Silicon)
- Download OrchestratorSetup below (no extension)
- In Terminal:
chmod +x OrchestratorSetup && ./OrchestratorSetup - First launch will show a Gatekeeper warning ("cannot be opened because the developer cannot be verified") — this build isn't code-signed yet. Right-click the file → Open, or run
xattr -d com.apple.quarantine OrchestratorSetupfirst. - The wizard detects your hardware, downloads the right runtime and model, configures everything
Portable: download TheOrc-1.12.0-osx-arm64-portable.tar.gz instead — tar -xzf it, both binaries are already executable.
Intel Macs (osx-x64) aren't built yet — only Apple Silicon.
🐝 Warband (headless HIVE node, no GUI)
Deploy theorc-warband on any machine you want to join your HIVE without opening the
full app — a cloud box, a home-lab server, a Raspberry Pi. Download the binary for your
OS below, chmod +x theorc-warband, then either run it directly or set it up as a
systemd service (Linux) / launchd agent (macOS) so it survives reboots. See
--show-identity and --pair --target <host> --expect-fingerprint "<phrase>" for
joining an existing HIVE headlessly.
🖥️ System requirements
| OS | Windows 10/11 (x64) · macOS (Apple Silicon) |
| .NET | Not required — fully self-contained |
| GPU | NVIDIA RTX (CUDA 12) · AMD (Vulkan) · Apple Silicon (Metal) · CPU-only (AVX2 / baseline) |
| RAM | 8 GB minimum · 16 GB recommended |
Full changelog: v1.11.3...v1.12.0
Built from v1.12.0 · .NET 10 · Source · Support the Orc ☕