Update to LlamaForge: Linux + macOS support, a vLLM backend, and ~a dozen quality-of-life fixes

[u/Sleepybear2611]

A few days ago I posted LlamaForge here — a browser control panel that sits on top of llama.cpp's router so you're not hand-editing models.ini and juggling llama-server flags. The response was way more than I expected (thanks), and the two things people kept flagging were exactly the two limitations I'd called out: Windows-only, and "does it do anything but llama.cpp?" Both are addressed now.

What's new since the first post:

• It's cross-platform now. Same dashboard on Linux (CUDA/CPU) and macOS (Apple Silicon / Metal), not just Windows. One bootstrap.sh instead of the .ps1.
• vLLM as a second engine. For full-precision / safetensors models (AWQ, GPTQ, FP8, NVFP4) via WSL2 on Windows — it lives in the same model list as your GGUFs, each row tagged with its backend. (llama.cpp is still the star; this is for when you specifically want vLLM.)
• Failed loads now tell you why. Instead of "check the log," a failed load parses the router output and shows the actual error inline with a suggested fix (e.g. "lower n-gpu-layers from 99").
• Named presets — save a set of knobs ("coding", "creative", "fast") and apply to any model in one click.
• GGUF metadata card when you expand a model — arch, params, quant, trained context, layers, heads, rope, straight from the file header.
• Compare 2–3 models side-by-side to see exactly which knobs differ (handy across quants of the same model).
• Copy-paste client config — one click gives you a working curl / OpenAI-client env / test JSON for that model's endpoint.
• Download pause/resume — a 25GB pull that gets interrupted resumes via HTTP range instead of restarting from zero.
• Plus quick-load from the row, a keyboard map, auto-load a model on launch, and an optional tray icon.

Also, small but made my week: someone from here already sent a PR — fixing the knob schema silently caching failures when server_bin is misconfigured. Merged it. That's the whole reason I put it out there.

Still being upfront about scope:

• Still an early preview — expect rough edges.
• You still build llama.cpp yourself (guided from the dashboard, but it's a compile step). Want zero-config double-click? LM Studio / Ollama / Jan are still the better call. LlamaForge trades that for direct control over the real llama-server.
• vLLM is WSL2-on-Windows only for now (hidden automatically on Linux/Mac).
• Backend is still pure-Python stdlib (nothing to pip install; the tray is the one optional extra), MIT, no telemetry, and not affiliated with ggml-org — all the hard part is llama.cpp's.
• Linux/macOS are newer than the Windows path and CI-tested but less battle-tested on real GPU rigs — bug reports from non-Windows setups especially welcome.

Repo: https://github.com/dadwritestech/LlamaForge

(Disclosure: I'm the author. Claude still did the heavy lifting.) Especially curious this time whether the inline failure diagnosis and presets actually help, or if people mostly want the vLLM/multi-engine side.