What's New

This page summarizes the most recent additions to LlamaForge. Each entry links to the full reference for that capability. For the longer-term direction, see the project's ROADMAP.md.

Focused scanning and a lighter Models tab

Setup can now persist one or more model folders instead of walking every drive, while an explicit blank list still selects the platform defaults. A llama.cpp model can be Unregistered from models.ini without deleting its GGUF file. The Models view also avoids polling collapsed logs, shares GPU telemetry through a short cache instead of spawning nvidia-smi per browser poll, and keeps the CRT scanline look without the expensive full-viewport blend mode.

Fresh bootstrap runs now accept an existing llama.cpp checkout and derive its build/server paths, removing the hand-edit step reported by WSL2 users.

See Setup, Models & Tuning, and Install.

Fail-closed network and explicit credentials

Network Access now distinguishes the loopback-only dashboard from the router it controls. LlamaForge accepts only local (127.0.0.1) or LAN (0.0.0.0) router scope, requires a usable key for every newly configured LAN router, and refuses LlamaForge-owned starts/restarts when that policy is unsafe. Existing manual or legacy settings are shown for repair rather than silently rewritten. Routine dashboard state is redacted; Client Config, Agent Config, and generated keys are deliberate no-store actions. This is tested product behavior in an early preview, not a formal security standard or audit certification.

See Security, Setup, HTTP API, and Connect an Agent.

ik_llama as a second llama-family engine

The Build tab now builds and drives ik_llama.cpp alongside stock llama.cpp, switched with one control (POST /api/engine/switch). The switch is gated on a capability probe: a binary whose llama-server lacks router mode (--models-preset) is refused with an explanation rather than taking the router down, and ik_llama keeps its own models.ini registry (a -ikllama sibling of the main one). Per-model tuning comes along automatically because the knob schema is parsed from whichever binary is active.

See Build & Update and models.ini Format.

Bind a preset as a model's default

Named presets can now be bound to a model, not just applied once. Binding writes the preset's knobs into the model's section and — the point of it — editing a bound preset re-syncs every model using it. A ◉/○ dot on each preset chip toggles the binding; the bound chip is highlighted. Hand edits still win, and unbinding leaves the knobs in place.

See Models & Tuning.

Auto-wired MTP draft models

A scan now attaches an mtp-* speculative draft sidecar to its parent model the way mmproj already is, filling spec-draft-model. It enables spec-type=draft-mtp only when the sidecar actually declares NextN layers — the signal llama.cpp itself gates on — so a model that can't use MTP is never broken. The wiring is additive: it never overwrites a spec-type you set by hand (e.g. an ngram mode).

See models.ini Format.

Offload-aware VRAM-fit rating

The FITS / TIGHT / CPU OFFLOAD badge in Discover now defers to the same physics estimate as the Will-it-run panel instead of a raw size-vs-VRAM guess. A large MoE that runs fast with its experts on CPU reads as TIGHT rather than being mislabeled CPU OFFLOAD just for being bigger than VRAM. The size heuristic remains the fallback when no prediction is available.

See Discover.

A more forgiving first run

Several fresh-install rough edges are gone. A missing config.json is seeded from config.example.json instead of crashing; models.ini is created if absent; a relative models_ini resolves against the repo root so the router never comes up with zero models; a busy router port is reported by name instead of failing silently; a freshly installed tool is detected without a restart; and a build whose llama-server succeeded but whose UI-asset step failed reports "built, with warnings" instead of BUILD FAILED.

See First Run, Setup, and Troubleshooting.

Lite and Advanced modes, with a guided first run

A first-run wizard now walks a new install through engine detection, hardware review, model selection, and a recommended tune — then applies it and loads the model. The dashboard runs in one of two modes: Lite presents a reduced, task-oriented set of controls; Advanced exposes every server flag. A hardware auto-tune proposes per-model settings (GPU-layer offload, KV-cache type, context ceiling, and intent presets for balanced / speed / context / coding) sized to the detected VRAM.

See First Run and Models & Tuning.

Anthropic-compatible endpoint

The panel exposes an Anthropic-compatible POST /v1/messages endpoint that translates to the local OpenAI-style router, with full streaming and tool-use support. Combined with the existing OpenAI-compatible surface, LlamaForge can serve clients written for either API against your local models.

See HTTP API.

One-click agent setup

A Connect an Agent panel generates — and optionally writes — the configuration for Claude Code, Codex, and pi.dev, pointing each at your local endpoint. Claude Code is scoped to 127.0.0.1; generated files are backed up before any change is written.

See Connect an Agent.

Context Wiki

A working directory of Markdown context documents, composed into named profiles and selected per model. An active profile is delivered two ways: injected into requests through the Anthropic shim and the OpenAI proxy, or exported into an agent's native context file (CLAUDE.md / AGENTS.md) inside a managed marker region. Because the injected prefix is stable, the router's prompt-cache reuses it across requests.

See Context Wiki.

Light and dark themes, plus a colorblind-safe mode

The dashboard now offers a Light theme alongside the original dark one, and an independent Colorblind-safe mode that applies a universal Okabe–Ito status palette and adds non-color cues (glyphs and labels) so status never depends on hue alone. The two axes are orthogonal — all four combinations are valid — and each choice persists per device.

See Theming & Accessibility.

In-app documentation

The documentation you are reading is available inside the dashboard under the Help view and is also published as a static site. Both surfaces render from a single Markdown source, so they never drift.

Navigation moved from a top tab bar to a left sidebar that collapses between a compact icon rail and a labeled, expanded state. Settings are pinned at the bottom; the layout adapts to narrow windows with an overlay drawer. All existing navigation, theming, and keyboard shortcuts are unchanged.

See Keyboard Shortcuts.

Refine benchmark in the Models panel

A Refine button now sits inline in each model's editor (beside the Presets bar). Pick an intent (balanced / speed / context / coding), click Run, and it auto-generates knob recommendations, benchmarks candidates with real completion requests (~200 tokens each), and applies the fastest config. A results table shows tok/s per candidate and which was chosen. The same autotune engine used in the first-run wizard is now available anytime from the main Models tab.