diff --git a/QUICKSTART.md b/QUICKSTART.md
index cfc7360..eabb88f 100644
--- a/QUICKSTART.md
+++ b/QUICKSTART.md
@@ -142,40 +142,52 @@ does not need any of this — it is a standard transformer with no FLA fast path
- **transformers ≥ 5.12 required** — older versions fail with
`'Qwen3_5MoeConfig' object has no attribute 'vocab_size'`. Check:
`python -c "import transformers; print(transformers.__version__)"`.
-- **Optional GPU kernels** — without them inference still works; startup may print
- `The fast path is not available…` (harmless on Windows; slower on linear-attention
- layers). Install **only for your platform**:
+- **GPU fast path (optional)** — without it inference still works; startup prints
+ `The fast path is not available…` and linear-attention layers use a slower PyTorch
+ fallback. Install **only for your platform**:
- | Platform | Install | Fast path? |
- |----------|---------|------------|
- | **Native Windows** | `pip install triton-windows` (also pulled by `meshnet-node` on Windows) | **Load only** — model imports and runs on PyTorch fallback. Do **not** `pip install flash-linear-attention[cuda] causal-conv1d` (see below). |
- | **Linux + NVIDIA CUDA** | `pip install flash-linear-attention[cuda]` | **Yes** — `causal-conv1d` optional since FLA ≥0.3.2 ships Triton conv1d; add it only if transformers still asks for it. Needs CUDA toolkit (`nvcc`) or a matching prebuilt wheel. |
- | **Linux + AMD ROCm** | `pip install flash-linear-attention[rocm]` | **Yes** — same optional `causal-conv1d` note. |
+ | Platform | Install | Notes |
+ |----------|---------|-------|
+ | **Native Windows + NVIDIA** | `pip install triton-windows` then `pip install flash-linear-attention` | **Fast path works.** FLA [officially supports `triton-windows`](https://github.com/fla-org/flash-linear-attention/pull/757) (tested Win11, PyTorch 2.10, triton-windows 3.6). Do **not** use the `[cuda]` extra on Windows — pip looks for Linux `triton` and fails. Do **not** install `causal-conv1d` — FLA ≥0.3.2 ships Triton conv1d; the separate package is Linux-only and breaks on Windows (`bare_metal_version` / nvcc errors). |
+ | **Linux + NVIDIA CUDA** | `pip install flash-linear-attention[cuda]` | `causal-conv1d` optional (same FLA built-in conv1d note). Needs CUDA toolkit (`nvcc`) matching torch, or a prebuilt wheel. |
+ | **Linux + AMD ROCm** | `pip install flash-linear-attention[rocm]` | Same optional `causal-conv1d` note. |
- On native Windows, `triton-windows` is enough for Qwen3.6-MoE to **load**. Without it,
+ **Windows verify** (after install):
+
+ ```powershell
+ python -c "import triton; import fla; print('triton', triton.__version__, 'fla ok')"
+ ```
+
+ `triton-windows` is also pulled by `meshnet-node` on Windows. Without it, Qwen3.6-MoE
startup fails with misleading `Could not import module 'Qwen3_5MoeForCausalLM'`.
-Why there is no supported Windows fast path (research notes)
+Windows fast path — what failed and what actually works
-We checked upstream docs and real install attempts (2026-07):
+The command that failed — `pip install flash-linear-attention[cuda] causal-conv1d` — mixes
+two different things:
-1. **`causal-conv1d`** — no official Windows wheels on PyPI. Source builds need `nvcc`
- plus a torch/CUDA stack that matches; without `nvcc` pip fails with
- `bare_metal_version is not defined`. Open upstream issues confirm Windows is
- unsupported / broken for most setups.
-2. **`flash-linear-attention[cuda]`** — FLA's install guide targets Linux (CUDA / ROCm /
- XPU / NPU). The `[cuda]` extra pulls PyPI `triton`, which conflicts with
- `triton-windows` on native Windows. FLA does not document or CI-test Windows.
-3. **What works today on native Windows GPU** — CUDA torch + `triton-windows` → Qwen3.6
- loads and infers via Transformers' pure-PyTorch fallback (~¾ of layers are
- linear-attention; those hops are slower). This is what we use in alpha.
-4. **If you need the fast path on a Windows PC** — run the GPU node in **WSL2 with Linux
- CUDA** (or a Linux box): `pip install flash-linear-attention[cuda]` there. Keep
- Windows native for reachability / relay if needed.
-5. **Experimental / unsupported** — community `causal-conv1d` wheels and
- `triton-windows` + pinned `flash-linear-attention --no-deps` hacks exist for narrow
- Python/torch/CUDA combos; we do not support or test these for meshnet-node.
+1. **`flash-linear-attention[cuda]` on Windows** — wrong extra. `[cuda]` pulls PyPI
+ `triton>=3.3`, which does not exist for Windows (`No matching distribution found`).
+ Use plain `pip install flash-linear-attention` **after** `triton-windows` is already
+ installed; FLA detects `triton-windows` and uses it.
+2. **`causal-conv1d`** — separate Dao-AILab CUDA extension, **not required** for FLA or
+ Qwen3.6 when FLA is installed. No official Windows wheels. Source builds need `nvcc`
+ whose major version matches torch's CUDA (e.g. torch `+cu118` needs CUDA 11.8 toolkit,
+ not 12.5). Community wheels exist for narrow Python/torch combos
+ ([PR #46](https://github.com/Dao-AILab/causal-conv1d/pull/46)) but we skip them.
+
+**Working Windows stack** (confirmed on this repo's dev machine: Python 3.12, torch
+2.7.1+cu118, triton-windows 3.7.1, flash-linear-attention 0.5.0):
+
+```powershell
+pip install triton-windows
+pip install -U flash-linear-attention
+python -c "import triton; import fla; print('ok')"
+```
+
+If the fast-path warning persists after that, upgrade FLA to ≥0.5.1 (includes the
+`triton-windows` detection from PR #757) and restart the node.
@@ -289,9 +301,8 @@ curl -sI https://ai.neuron.d-popov.com/rpc/test-peer
no HuggingFace gating. Best for first-time setup.
**Alpha model:** `qwen3.6-35b-a3b` — 40 layers, ~72 GB BF16 download, MoE with hybrid
-linear attention. Use on machines with enough RAM/VRAM; see Qwen3.5/3.6 notes above for
-`triton-windows` (Windows) or `flash-linear-attention` (Linux GPU). Tracker accepts the
-alias or full repo id (`unsloth/Qwen3.6-35B-A3B`).
+linear attention. On Windows install `triton-windows` + `flash-linear-attention`; on Linux
+GPU use `flash-linear-attention[cuda]`. Tracker accepts the alias or full repo id (`unsloth/Qwen3.6-35B-A3B`).
Downloads cache under `~/.meshnet/models/` (or `$HF_HOME` / `$env:HF_HOME`).
@@ -330,13 +341,17 @@ HF_HOME=/path/to/models .venv/bin/meshnet-node start --tracker http://localhost:
.venv/bin/meshnet-node start --tracker https://ai.neuron.d-popov.com --model Qwen/Qwen2.5-0.5B-Instruct
```
-**Alpha model (Qwen3.6, Windows GPU — PyTorch fallback, no FLA fast path):**
+**Alpha model (Qwen3.6, Windows GPU — enable fast path):**
```powershell
$env:HF_HOME = "D:\DEV\models"
+pip install triton-windows
+pip install -U flash-linear-attention
meshnet-node start --tracker http://192.168.0.179:8080 --model qwen3.6-35b-a3b --quantization bfloat16
```
+Do not add `causal-conv1d` or `flash-linear-attention[cuda]` on Windows (see Qwen3.5/3.6 notes).
+
**Alpha model (Qwen3.6, Linux GPU — with fast path):**
```bash
diff --git a/packages/tracker/meshnet_tracker/dashboard.html b/packages/tracker/meshnet_tracker/dashboard.html
index 40c6145..9fec492 100644
--- a/packages/tracker/meshnet_tracker/dashboard.html
+++ b/packages/tracker/meshnet_tracker/dashboard.html
@@ -279,6 +279,41 @@ const tps = v => (v === null || v === undefined) ? "?" : (Math.round(v * 10) / 1
const copies = v => (v === null || v === undefined) ? "?" : Number(v).toFixed(2);
const short = (s, n=14) => { s = String(s); return s.length > n ? s.slice(0, 6) + "…" + s.slice(-5) : s; };
+function modelAliasKey(value) {
+ if (!value) return "";
+ const text = String(value).trim();
+ if (!text) return "";
+ const shortName = text.includes("/") ? text.split("/").pop() : text;
+ return shortName.toLowerCase();
+}
+
+function buildModelAliasMap(map) {
+ const byAlias = new Map();
+ const register = (display, ...names) => {
+ if (!display) return;
+ for (const name of names) {
+ const key = modelAliasKey(name);
+ if (key) byAlias.set(key, display);
+ }
+ };
+ for (const entry of (map && map.recommended_models) || []) {
+ register(entry.id, entry.id, entry.hf_repo, ...(entry.aliases || []));
+ }
+ for (const entry of availableModels || []) {
+ register(entry.name || entry.id, entry.id, entry.name, ...(entry.aliases || []));
+ }
+ return byAlias;
+}
+
+function resolveModelGroup(node, aliasMap) {
+ for (const candidate of [node.hf_repo, node.model]) {
+ if (!candidate) continue;
+ const hit = aliasMap.get(modelAliasKey(candidate));
+ if (hit) return hit;
+ }
+ return node.hf_repo || node.model || "?";
+}
+
async function fetchJson(path) {
try {
const headers = {};
@@ -315,15 +350,20 @@ function renderNodes(map) {
if (!nodes.length) {
$("nodes").innerHTML = 'no nodes registered
'; return;
}
+ const aliasMap = buildModelAliasMap(map);
const byModel = {};
for (const n of nodes) {
- const key = n.model || n.hf_repo || "?";
+ const key = resolveModelGroup(n, aliasMap);
(byModel[key] = byModel[key] || []).push(n);
}
+ const modelNames = Object.keys(byModel).sort((a, b) => a.localeCompare(b));
let html = "";
- for (const [model, group] of Object.entries(byModel)) {
- const supply = group.find(n => n.model_supply && n.model_supply.served_model_copies !== undefined);
- const served = supply && supply.model_supply && supply.model_supply.served_model_copies;
+ for (const model of modelNames) {
+ const group = byModel[model];
+ const servedValues = group
+ .map(n => n.model_supply && n.model_supply.served_model_copies)
+ .filter(v => v !== null && v !== undefined);
+ const served = servedValues.length ? Math.max(...servedValues) : undefined;
html += `${esc(model)} (${group.length} node${group.length===1?"":"s"} · ${esc(copies(served))} served)
`;
html += table(["node", "shard", "tps (1h)", "queue", "served"], group.map(n => {
const modelStats = (n.throughput && (n.throughput[n.hf_repo] || n.throughput[n.model])) || {};
@@ -1552,6 +1592,19 @@ async function refresh() {
renderChatHistory();
$("refreshed").textContent = "refreshed " + new Date().toLocaleTimeString();
}
+
+const REFRESH_MS = 10000;
+
+function selectionActive() {
+ const sel = window.getSelection();
+ return sel && !sel.isCollapsed && sel.toString().length > 0;
+}
+
+async function refreshIfIdle() {
+ if (selectionActive()) return;
+ await refresh();
+}
+
refresh();
initChatSessions();
bindChatPromptShortcuts();
@@ -1559,8 +1612,12 @@ renderAccountPanel();
renderChatModels();
renderChatHistory();
renderChatAuthHint();
-setInterval(refresh, 4000);
-setInterval(() => { if (sessionToken || isLoggedIn) renderAccountPanel(); }, 8000);
+setInterval(refreshIfIdle, REFRESH_MS);
+setInterval(() => {
+ if (!sessionToken && !isLoggedIn) return;
+ if (selectionActive()) return;
+ renderAccountPanel();
+}, REFRESH_MS);