docs: rename frank-default-large/small/sightsound to frank-large/small/npu across vault

Mechanical identity rename across the knowledge base, AGENTS.md, README, the
hermes config example, and homepage services.json. No behavior change; served
aliases on the LLM endpoints change with the deploy of commit 69f87c8.
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Frank committed 2026-09-03 16:17:14 -07:00
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@@ -1,20 +1,20 @@
---
type: configuration
title: FreeToken frank-default-small Runbook
description: FreeToken v0.1.2 serving batsclamp/Huihui-Qwen3.6-35B-A3B-abliterated-FP8 as frank-default-small on Dankserver (RTX 3090).
title: FreeToken frank-small Runbook
description: FreeToken v0.1.2 serving batsclamp/Huihui-Qwen3.6-35B-A3B-abliterated-FP8 as frank-small on Dankserver (RTX 3090).
tags:
- freetoken
- dankserver
- frank-default-small
- frank-small
- qwen3.6
- moe
- fp8
timestamp: 2026-08-30
---
# FreeToken frank-default-small Runbook
# FreeToken frank-small Runbook
The `frank-default-small` service on Dankserver is served by
The `frank-small` service on Dankserver is served by
**FreeToken v0.1.2** — an edge-native MoE serving engine that keeps
expert weights in host RAM with an LRU GPU cache. Model history on this
runtime: official `Qwen/Qwen3.6-35B-A3B-FP8` (since 2026-09-02) →
@@ -130,8 +130,8 @@ modelopt block-fp8 remains the only FP8 path that loads.
| File | Purpose |
|------|---------|
| `scripts/serve-frank-small.sh` | Serve script (launches `ft serve`) |
| `scripts/frank-default-small.env` | Tunables (model path, pool sizes) |
| `scripts/frank-default-small.service` | Systemd unit |
| `scripts/frank-small.env` | Tunables (model path, pool sizes) |
| `scripts/frank-small.service` | Systemd unit |
## Limitations
@@ -150,7 +150,7 @@ modelopt block-fp8 remains the only FP8 path that loads.
- **`model` field is required** in `/v1/chat/completions` bodies —
FreeToken rejects requests without it with a 422 (`Field required`),
unlike llama.cpp's server which defaults to the loaded model. Send
`"model": "frank-default-small"`.
`"model": "frank-small"`.
- Load on this swap took well under the old llama.cpp numbers:
weights → experts (parallel build) → CUDA graph capture → Uvicorn
in ~2 minutes; the 2026-08-30 swap verified ~40 tok/s decode on a