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docs: the engine is colibri(.exe) everywhere — kill the obsolete rename step

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The README and quickstart still told Windows users to rename the downloaded
engine to glm.exe: that was the pre-#508 archive layout. Since #508 the
archive ships the engine plainly named colibri.exe and the launcher finds it
with zero setup — the rename step now just confuses people (and the file it
names no longer exists).

Same sweep for the build docs: 'make glm.exe' is not a target anymore
(windows.md recommended it), direct-run examples said ./glm(.exe), metal.md
built 'make glm METAL=1'. All now use the real names; troubleshooting rows
keep a '(glm.exe pre-rename)' note where old trees are the subject.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
This commit is contained in:
JustVugg
2026-07-22 08:33:28 +02:00
co-authored by Claude Fable 5
parent 3cd675a850
commit 10d9b580fa
6 changed files with 33 additions and 39 deletions
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README.md
+8 -8
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@@ -206,10 +206,10 @@ scale-granularity/rotation ablations live in
> through install → build → model → first chat step by step for Linux, Windows,
> and macOS. On **Windows** you don't even need to build: download the
> `colibri-<version>-windows-x86_64.zip` from
> [Releases](https://github.com/JustVugg/colibri/releases), unzip it, rename
> `colibri-*-windows-x86_64.exe` → `glm.exe`, install
> [Python 3](https://www.python.org/downloads/), and run `coli chat` — full
> details in the [Windows section](docs/quickstart.md#windows).
> [Releases](https://github.com/JustVugg/colibri/releases), unzip it, install
> [Python 3](https://www.python.org/downloads/), and run `coli chat` — the
> engine ships ready to run as `colibri.exe`, the launcher finds it by itself.
> Full details in the [Windows section](docs/quickstart.md#windows).
### 1. Get the model
@@ -245,10 +245,10 @@ and the optional API gateway.
**On Windows?** You don't need to build. Download the
`colibri-<version>-windows-x86_64.zip` from
[Releases](https://github.com/JustVugg/colibri/releases), unzip it, rename
`colibri-*-windows-x86_64.exe` → `glm.exe` (so the `coli` launcher finds the
engine), install [Python 3](https://www.python.org/downloads/), then run
`coli chat`. Full walkthrough in the [Quick Start guide](docs/quickstart.md#windows).
[Releases](https://github.com/JustVugg/colibri/releases), unzip it, install
[Python 3](https://www.python.org/downloads/), then run `coli chat` — the
engine is already named `colibri.exe` and the launcher finds it next to
itself. Full walkthrough in the [Quick Start guide](docs/quickstart.md#windows).
Prefer a `coli` command on your PATH? From a checkout, `pip install -e .`
registers it (the engine itself still lives in `c/` — this is an editable
docs/cuda.md
+5 -5
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@@ -10,7 +10,7 @@ cd c
make cuda-test CUDA=1 # q8/q4/q2/f32 kernel correctness
make CUDA=1
# optional dense-path experiment (hot experts are configured below)
COLI_CUDA=1 COLI_GPU=0 CUDA_DENSE=1 SNAP=/nvme/glm52_i4 ./glm 64 4 4
COLI_CUDA=1 COLI_GPU=0 CUDA_DENSE=1 SNAP=/nvme/glm52_i4 ./colibri 64 4 4
```
Requirements: Linux, an NVIDIA driver, and a CUDA Toolkit under
@@ -26,19 +26,19 @@ A measured `PIN` profile promotes its hottest experts into a persistent VRAM
tier while keeping the rest in RAM:
```bash
STATS=stats.txt SNAP=/nvme/glm52_i4 ./glm 64 4 4 # collect routing frequencies first
STATS=stats.txt SNAP=/nvme/glm52_i4 ./colibri 64 4 4 # collect routing frequencies first
COLI_CUDA=1 COLI_GPU=0 CUDA_EXPERT_GB=16 \
PIN=stats.txt PIN_GB=160 SNAP=/nvme/glm52_i4 ./glm 64 4 4
PIN=stats.txt PIN_GB=160 SNAP=/nvme/glm52_i4 ./colibri 64 4 4
# multi-GPU expert tier, 150 GB total budget across six 32 GB devices
COLI_CUDA=1 COLI_GPUS=0,1,2,3,4,5 CUDA_EXPERT_GB=150 \
CUDA_DENSE=1 PIN=stats.txt PIN_GB=300 RAM_GB=226 \
SNAP=/nvme/glm52_i4 ./glm 64 4 4
SNAP=/nvme/glm52_i4 ./colibri 64 4 4
# large-RAM host: fill safe VRAM, then keep every remaining expert in RAM
COLI_CUDA=1 COLI_GPUS=0,1,2,3,4,5 CUDA_EXPERT_GB=auto \
CUDA_DENSE=1 COLI_CUDA_ATTN=1 PIN=stats.txt PIN_GB=all RAM_GB=auto \
SNAP=/nvme/glm52_i4 ./glm 64 4 4
SNAP=/nvme/glm52_i4 ./colibri 64 4 4
```
Selected experts are uploaded during startup, so capacity failures occur before
docs/grammar-draft.md
+1 -1
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@@ -31,7 +31,7 @@ Fewer forwards also means less LRU churn, which compounds with cache hit rate.
## Usage
```bash
GRAMMAR=fit.gbnf COLI_TEMP=0 PROMPT="..." ./glm 64 4 8
GRAMMAR=fit.gbnf COLI_TEMP=0 PROMPT="..." ./colibri 64 4 8
```
- `GRAMMAR=<file.gbnf>` — arm the draft source with a grammar. Byte-level GBNF
docs/metal.md
+1 -1
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@@ -9,7 +9,7 @@ Token-exact vs the CPU path.
```bash
cd c
make glm METAL=1 # macOS only; no Xcode needed (shader compiles at runtime)
make colibri METAL=1 # macOS only; no Xcode needed (shader compiles at runtime)
make metal-test # standalone kernel/attention correctness vs CPU reference
COLI_METAL=1 COLI_MODEL=/path/glm52_i4 ./coli chat --ram 96
```
docs/quickstart.md
+6 -12
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@@ -48,23 +48,17 @@ Inside you'll find:
| File | What it is |
|---|---|
| `colibri-<version>-windows-x86_64.exe` | **the engine** — the C program that actually runs the model |
| `colibri.exe` | **the engine** — the C program that actually runs the model |
| `coli` | the command-line launcher (`chat`, `serve`, `convert`, `doctor`, …) |
| `openai_server.py`, `resource_plan.py`, `doctor.py` | Python support for the API server and placement planner |
Two setup steps:
1. **Rename the engine to `glm.exe`** so the launcher can find it (it looks for a
binary named `glm`):
```powershell
Rename-Item colibri-*-windows-x86_64.exe glm.exe
```
2. **Install Python 3** from [python.org](https://www.python.org/downloads/) — the
`coli` launcher and the API gateway are Python scripts (the engine itself is
pure C and needs nothing).
One setup step: **install Python 3** from
[python.org](https://www.python.org/downloads/) — the `coli` launcher and the
API gateway are Python scripts (the engine itself is pure C and needs nothing).
No renaming, no configuration: the launcher finds `colibri.exe` next to itself.
Then continue to [step 3](#3-get-the-model). Prefer to skip the launcher? You can
run the engine directly — `.\glm.exe` reads the model path from the `SNAP`
run the engine directly — `.\colibri.exe` reads the model path from the `SNAP`
environment variable (see [docs/windows.md](windows.md)) — but `coli chat` is the
easy path.
docs/windows.md
+12 -12
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@@ -31,7 +31,7 @@ Use the container recommended in the README (with **int8 MTP heads** — int4 he
From a normal PowerShell, in the repo's `c\` directory:
```powershell
make glm.exe ARCH=native # ARCH=native unlocks AVX-VNNI on Alder Lake+/Arrow Lake
make colibri.exe ARCH=native # ARCH=native unlocks AVX-VNNI on Alder Lake+/Arrow Lake
make iobench.exe # disk benchmark, useful before committing to the download
```
@@ -39,10 +39,10 @@ Warnings about `#pragma comment` and unused variables are normal (MSVC-isms gcc
### ⚠️ Smart App Control will block your fresh binary
On Windows 11 machines with **Smart App Control** enforced (`VerifiedAndReputablePolicyState = 1`), running your self-compiled `glm.exe` fails with:
On Windows 11 machines with **Smart App Control** enforced (`VerifiedAndReputablePolicyState = 1`), running your self-compiled `colibri.exe` fails with:
```
Program 'glm.exe' failed to run: An Application Control policy has blocked this file
Program 'colibri.exe' failed to run: An Application Control policy has blocked this file
```
This is not Defender and not Mark-of-the-Web — SAC blocks *all* unsigned, unknown binaries, which includes anything you compile yourself. **Fix:** Windows Security → App & browser control → Smart App Control settings → **Off**, then **reboot** (the policy only reloads on restart). Note SAC is one-way: re-enabling later requires resetting Windows. If the settings page is missing, the registry equivalent is setting `HKLM:\SYSTEM\CurrentControlSet\Control\CI\Policy\VerifiedAndReputablePolicyState` to `0` (admin PowerShell), then rebooting. Check your current state before touching anything:
@@ -64,13 +64,13 @@ nvcc needs MSVC as host compiler, so this one step must run from a shell with th
```cmd
make cuda-dll CUDA_ARCH=sm_120 # match your GPU: sm_120 Blackwell, sm_89 Ada, ...
make glm.exe CUDA_DLL=1 ARCH=native # relink host with the runtime loader
make colibri.exe CUDA_DLL=1 ARCH=native # relink host with the runtime loader
```
Two pitfalls, both fixed on current `dev` (#314) but worth knowing on older checkouts:
- **Spaces in `CUDA_HOME`** (`C:\Program Files\...`) used to break the recipe → fixed; nvcc now comes from PATH and `"$(NVCC)"` is quoted.
- **`make glm.exe CUDA_DLL=1` after a CPU-only build** used to report `up to date` and silently keep the CPU-only binary (GPU tier never engages, no error). Current `dev` has a build-config stamp that forces the relink. On older trees: delete `glm.exe` first.
- **`make colibri.exe CUDA_DLL=1` after a CPU-only build** used to report `up to date` and silently keep the CPU-only binary (GPU tier never engages, no error). Current `dev` has a build-config stamp that forces the relink. On older trees: delete the binary (`colibri.exe`; `glm.exe` pre-rename) first.
Sanity check: first GPU run should print `[CUDA] device 0: <your GPU>, ... sm_XX` and `[CUDA] mode: routed experts + resident dense tensors`.
@@ -99,10 +99,10 @@ Size `CUDA_EXPERT_GB` so dense (~10 GB) + experts + working set stays under your
| Symptom | Cause | Fix |
|---|---|---|
| `'printf' is not recognized` / `The system cannot find the path specified` during `make glm.exe` | scoop MinGW has no `sh.exe`; make fell back to cmd.exe (#478) | §0 — use MSYS2/w64devkit, or `set PATH=%PATH%;C:\msys64\usr\bin` |
| `'printf' is not recognized` / `The system cannot find the path specified` during `make colibri.exe` | scoop MinGW has no `sh.exe`; make fell back to cmd.exe (#478) | §0 — use MSYS2/w64devkit, or `set PATH=%PATH%;C:\msys64\usr\bin` |
| `An Application Control policy has blocked this file` | Smart App Control | §2 — turn SAC off + **reboot** |
| `cuda-dll ... Error 1` immediately | old tree: spaced CUDA_HOME / MSVC rejects `-Wextra` | update to current `dev` (#314) |
| `glm.exe is up to date` but GPU never engages | old tree: stale CPU-only binary | update to `dev`, or delete `glm.exe` and rebuild |
| `colibri.exe is up to date` but GPU never engages | old tree: stale CPU-only binary | update to `dev`, or delete the binary and rebuild |
| `cl.exe (MSVC) not in PATH` | built from plain PowerShell | use the x64 Native Tools prompt |
| `nvcc fatal: unsupported gpu architecture 'sm_120'` | CUDA < 12.8 | install CUDA 12.8+ |
| MTP `0% (0/0)` on CPU path | int4 MTP heads in the container | use the int8-MTP container |
@@ -116,15 +116,15 @@ Size `CUDA_EXPERT_GB` so dense (~10 GB) + experts + working set stays under your
# dot-product instruction (VPDPBUSD) the engine can use for ~1.3x faster
# quantized matmul. The x86-64-v3 default (portable AVX2) compiles it out;
# build for THIS machine to enable it:
make glm.exe ARCH=native # banner prints "idot: avx-vnni"
make colibri.exe ARCH=native # banner prints "idot: avx-vnni"
# Verify (tiny model, 2.4 MB):
pip install torch transformers safetensors huggingface_hub
python tools/make_glm_oracle.py # generate tiny oracle
SNAP=./glm_tiny TF=1 ./glm.exe 64 16 16 # expect "32/32 positions"
SNAP=./glm_tiny TF=1 ./colibri.exe 64 16 16 # expect "32/32 positions"
# Run with real model:
SNAP=D:\glm52_i4 ./glm.exe 64 4 16 # batch inference
SNAP=D:\glm52_i4 ./colibri.exe 64 4 16 # batch inference
python coli chat --model D:\glm52_i4 # interactive chat
python coli serve --model D:\glm52_i4 # OpenAI-compatible API
```
@@ -149,7 +149,7 @@ completion.
On Windows the engine is built with MinGW gcc but CUDA kernels require MSVC +
nvcc. The split is clean: build the CUDA backend into a standalone
`coli_cuda.dll` (nvcc + MSVC), then the host `glm.exe` loads it at runtime via
`coli_cuda.dll` (nvcc + MSVC), then the host `colibri.exe` loads it at runtime via
`LoadLibrary` (`c/backend_loader.c`). The host never links cudart directly; if
the DLL is absent the engine falls back to CPU without error.
@@ -161,7 +161,7 @@ make cuda-dll CUDA_HOME="C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.
# Build the host with the runtime loader (CUDA_DLL=1 adds -DCOLI_CUDA and
# links backend_loader.o instead of cudart):
make glm.exe CUDA_DLL=1 ARCH=native
make colibri.exe CUDA_DLL=1 ARCH=native
# Run with the GPU expert tier (8 GB VRAM budget here; scale to your free VRAM):
$env:COLI_CUDA="1"; $env:COLI_GPU="0"; $env:CUDA_EXPERT_GB="8"