metal: advance the LFRU recency clock on the GPU-prerouted decode path (#417)

On Metal, when routing is precomputed on the GPU (g_pre_idx), the moe fast
path bumps eusage/ehit/eheat for the selected experts but skips the one thing
the full CPU router does at the equivalent site: elast[layer][e] =
++eaccess_clock. So the session-local recency clock advances during prefill
(full router) but freezes the moment GPU-prerouted decode starts, and REPIN's
tier_pick_lfru() tie-breaker then runs on stale recency for the rest of the
run. Mirror the exact update the non-Metal path already does. Inside
#ifdef COLI_METAL, so CPU/CUDA are untouched; elast only feeds the LFRU
eviction heuristic, so this cannot affect output, only which experts REPIN
keeps warm.

Found and reported by @monotophic with a source-level trace repro
(ELAST_TRACE). Fix is inspection-verified against line ~3055; needs a
Metal build to exercise end-to-end.

Closes #417

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

README.md

@@ -151,6 +151,10 @@ scale-granularity/rotation ablations live in
 
 ## Get started
 
+> **New here?** The [Quick Start guide](docs/quickstart.md) walks through
+> install → build → model → first chat step by step for Linux, Windows, and
+> macOS, with copy-paste commands and no assumed background.
+
 ### 1. Get the model
 
 A pre-converted **GLM-5.2 int4** container is on Hugging Face — **use the

c/glm.c

@@ -2931,6 +2931,11 @@ static void moe(Model *m, Layer *l, int layer, float *x, int S, float *out, int
                 m->eusage[layer][idxs[(int64_t)s*K+kk]]++;
                 ehit_mark(m,layer,idxs[(int64_t)s*K+kk]);
                 if(m->eheat[layer][idxs[(int64_t)s*K+kk]]<UINT32_MAX) m->eheat[layer][idxs[(int64_t)s*K+kk]]++;
+                /* #417: la scorciatoia GPU-prerouted deve far avanzare l'orologio di recency
+                 * come il percorso router completo (riga ~3055), altrimenti elast/eaccess_clock
+                 * si congelano a fine prefill e il tie-breaker LFRU di REPIN gira su punteggi
+                 * stantii durante il decode su Metal. */
+                m->elast[layer][idxs[(int64_t)s*K+kk]]=++m->eaccess_clock;
             }
             for(int d=0;d<D;d++) out[(int64_t)s*D+d]=0;
         }

docs/quickstart.md

@@ -0,0 +1,164 @@
+# Quick Start — from zero to a running model
+
+A step-by-step guide for first-time users on **Linux**, **Windows**, and **macOS**.
+No prior experience with C, CUDA, or model conversion is assumed. If you get
+stuck, `./coli doctor` (below) tells you exactly what's missing.
+
+> **What you're setting up:** colibrì runs a very large Mixture-of-Experts model
+> (e.g. GLM-5.2, 744B parameters) on a normal machine by streaming the model's
+> experts from disk instead of needing them all in RAM. The engine is a single
+> C program; Python is only used once, to prepare the model files.
+
+---
+
+## 0. What you need first (prerequisites)
+
+| | Minimum | Recommended |
+|---|---|---|
+| **RAM** | ~16 GB | 24 GB+ |
+| **Free disk** | ~380 GB for the int4 model | a fast NVMe SSD (streaming speed = your token speed) |
+| **OS** | Linux, Windows 10/11, or macOS | any |
+| **Tools** | a C compiler + `make` + `git` + `python3` | — |
+
+You do **not** need a GPU. A GPU only helps if you have one; the engine runs
+CPU-only by default.
+
+---
+
+## 1. Install the build tools
+
+### Linux (Ubuntu / Debian)
+
+```bash
+sudo apt update
+sudo apt install -y build-essential git python3
+```
+
+`build-essential` gives you `gcc`, `make`, and OpenMP (libgomp) — everything the
+engine needs.
+
+### Windows
+
+You have two options.
+
+**Option A — download a prebuilt binary (no compiler needed).**
+Grab the latest `colibri-<version>-windows-x86_64.zip` from the
+[Releases page](https://github.com/JustVugg/colibri/releases), unzip it, and
+skip to [step 3](#3-get-the-model). Python 3 (from
+[python.org](https://www.python.org/downloads/)) is still needed if you want to
+convert a model yourself.
+
+**Option B — build from source with MSYS2.**
+Install [MSYS2](https://www.msys2.org/), open the **UCRT64** shell, and run:
+
+```bash
+pacman -S --needed mingw-w64-ucrt-x86_64-gcc make git python
+```
+
+### macOS
+
+```bash
+xcode-select --install          # C compiler (clang)
+brew install libomp git python  # OpenMP for multithreading
+```
+
+---
+
+## 2. Get the code and build the engine
+
+```bash
+git clone https://github.com/JustVugg/colibri.git
+cd colibri/c
+./setup.sh
+```
+
+`setup.sh` checks your compiler and OpenMP, builds the engine, and runs a tiny
+self-test. When it prints:
+
+```
+engine self-test: 32/32  (expected 32/32)
+```
+
+the engine is working correctly. (On Windows Option A you already have the
+binary — you can skip this step.)
+
+---
+
+## 3. Get the model
+
+You have two paths.
+
+### Easiest — download a ready-made int4 container
+
+A pre-converted **GLM-5.2 int4** model is on Hugging Face. **Use the version
+with the int8 MTP heads** (the plain int4 heads disable speculative decoding —
+see [#8](https://github.com/JustVugg/colibri/issues/8)):
+
+**https://huggingface.co/mateogrgic/GLM-5.2-colibri-int4-with-int8-mtp**
+
+Download it into a folder on a fast disk, e.g. `/nvme/glm52_i4` (Linux/macOS) or
+`D:\glm52_i4` (Windows). It is about **372 GB**, so make sure you have the space.
+
+### Or convert it yourself from the FP8 source
+
+One resumable command downloads and converts the model shard by shard, so it
+never needs the full ~756 GB on disk at once:
+
+```bash
+./coli convert --model /nvme/glm52_i4
+```
+
+This step uses Python and runs only once. Safe to interrupt and re-run — it
+resumes where it left off.
+
+---
+
+## 4. Run it
+
+Point `COLI_MODEL` at the folder from step 3 and start chatting:
+
+```bash
+# Linux / macOS
+COLI_MODEL=/nvme/glm52_i4 ./coli chat
+
+# Windows (UCRT64 shell)
+COLI_MODEL=/d/glm52_i4 ./coli chat
+```
+
+Useful first commands:
+
+```bash
+COLI_MODEL=/nvme/glm52_i4 ./coli doctor   # read-only check: is everything ready?
+COLI_MODEL=/nvme/glm52_i4 ./coli plan     # shows where the model will live (RAM/disk/GPU)
+COLI_MODEL=/nvme/glm52_i4 ./coli chat --topp 0.85   # faster: reads less from disk, same quality
+```
+
+> **Tip:** `--topp 0.85` is worth adding on a disk-bound machine — it reads
+> fewer expert bytes per token with no quality loss, which directly means more
+> tokens per second.
+
+---
+
+## 5. What to expect
+
+- **First launch loads the resident weights** (~10 GB) — this takes a moment.
+- **Speed depends on your disk.** The experts stream from storage, so a fast
+  NVMe SSD is the single biggest factor in tokens/second. On a slow or shared
+  disk, generation can be well under 1 token/second — that's expected, and it's
+  the honest cost of running a 744B model on a small machine.
+- **It's still the full model.** Placement only changes speed, never the model's
+  answers or precision.
+
+If something doesn't work, run `./coli doctor` — it reports exactly what's
+missing (compiler, model files, permissions) and how to fix it.
+
+---
+
+## Where to go next
+
+| Topic | Doc |
+|---|---|
+| Windows native build (and CUDA DLL) | [docs/windows.md](windows.md) |
+| Tuning: cache, prefetch, speculation | [docs/tuning.md](tuning.md) |
+| OpenAI-compatible API + web dashboard | [docs/api.md](api.md) |
+| Every environment variable | [docs/ENVIRONMENT.md](ENVIRONMENT.md) |