homepage+docs: frank-archon runs our own GPTQ-w4 build, link our HF publication

serverAppConfigs-docs/Infrastructure/Homepage.md

@@ -226,6 +226,13 @@ its Pi-hole specifics — family members off the LAN cannot use
 house-DNS advice; the bullet now just says the names resolve on the
 home network.
 
+**Archon card runs the fleet's own model (2026-10-08):** the
+`frank-archon` homepage card and docs Model bullet now say the payload
+is our in-house GPTQ-w4 quant of Swift1.5-Qwen3.8-Flash-Next and link
+our HF publication (`frank8465/Swift-1.5-Qwen3.8-Flash-Next-GPTQ-W4A8-Radiance-AMD-R9700`)
+in place of the FP8-era base-model link; capacity line says three
+submissions (`--max-num-seqs 3`).
+
 ## Design decision — 2026-08-22
 
 The live up/down check runs server-side. A checker on Dankclaw probes each backend and writes a same-origin `status.json`. The page fetches only that file and renders the badges. The browser never calls a backend directly. The checker runs as a systemd timer every 3 minutes.

shared/homepage/docs.html

@@ -113,9 +113,9 @@
 
   <h2>frank-archon — the big one <em>(api id: frank-primary)</em></h2>
   <ul>
-    <li><strong>Model:</strong> <a href="https://huggingface.co/Qwen/Qwen3.8-Flash-Next-FP8" target="_blank" rel="noopener">Qwen3.8-Flash-Next</a> at native FP8 precision (radiance, 173&nbsp;GiB) on twin AMD R9700s with more than 200&nbsp;GB of system RAM — the biggest brain on the network and the fastest runtime.</li>
+    <li><strong>Model:</strong> our own build — a GPTQ-w4 quant of <a href="https://huggingface.co/frank8465/Swift-1.5-Qwen3.8-Flash-Next-GPTQ-W4A8-Radiance-AMD-R9700" target="_blank" rel="noopener">Swift1.5-Qwen3.8-Flash-Next</a>, quantized and tuned in-house (radiance, ~114&nbsp;GiB) on twin AMD R9700s with more than 200&nbsp;GB of system RAM — the biggest brain on the network and the fastest runtime.</li>
     <li><strong>Equivalent:</strong> Claude Opus 4.8 / OpenAI-Terra (Summer 2026 frontier intelligence).</li>
-    <li><strong>Capacity:</strong> 200k-token context, two submissions at a time; sees images.</li>
+    <li><strong>Capacity:</strong> 200k-token context, three submissions at a time; sees images.</li>
     <li><strong>Inputs:</strong> text, image.</li>
     <li><strong>Best for:</strong> software engineering, system administration, and instruction tasks. <strong>&#9888; Ask Frank for permission before use</strong> &mdash; this is his primary model; he uses it more than anything else on the fleet.</li>
   </ul>

shared/homepage/services.json

@@ -366,13 +366,13 @@
     "model": {
       "unit": "archon",
       "role": "Frank's strongest and fastest model runtime",
-      "hf": "https://huggingface.co/Qwen/Qwen3.8-Flash-Next-FP8",
+      "hf": "https://huggingface.co/frank8465/Swift-1.5-Qwen3.8-Flash-Next-GPTQ-W4A8-Radiance-AMD-R9700",
       "tagline": "...Power Overwhelming...",
       "sfx": [
         "archon-power.ogg"
       ],
       "equiv": "Equivalent to Claude Opus 4.8 / OpenAI-Terra (Summer 2026 frontier intelligence).",
-      "spec": "Qwen3.8-Flash-Next at native FP8 precision (radiance, 173 GiB) on twin AMD R9700s with more than 200 GB of system RAM — 200k-token context, two submissions at a time; sees images.",
+      "spec": "Our own build: a GPTQ-w4 quant of Swift1.5-Qwen3.8-Flash-Next, quantized and tuned in-house (radiance, ~114 GiB) on twin AMD R9700s with more than 200 GB of system RAM — 200k-token context, three submissions at a time; sees images.",
       "inputs": [
         "text",
         "image"