frank-medium: serve the new R9700s, retire frank-small

The RTX 3090 left Dankserver; two AMD Radeon AI PRO R9700 cards (Navi 48,
gfx1201, 32,624 MiB each) replaced it. FreeToken/CUDA cannot run there at all,
so the `frank-small` service is retired and `frank-medium` takes the endpoint.

New service: charlie12345/ROCmFPX llama.cpp (built for gfx1201 against the
fork's self-contained ROCm 7.14 toolchain, so the host's stale /opt/rocm-6.3.3
never participates) serving kingjones777/Qwen3.8-27B-ROCmFPX-Q8_0-AGENT-GGUF --
a Q8_0_ROCMFPX_AGENT (ftype 115) checkpoint that stock llama.cpp cannot load.
Placeholder config: --split-mode tensor across both cards, 131072 context,
1 slot, f16 KV, MTP draft head on ROCm0.

deploy-remote.sh grows retire_root_llm_small(): the CUDA-era unit is stopped,
disabled and moved to ~/.AGENTTRASH/20260915-frank-small-retire/host-units
before frank-medium starts, because `systemctl disable` has been observed to
delete the unit file itself on this host (2026-08-26 incident).

The retired frank-small repo files (env, unit, serve script) move to
scripts/.AGENTTRASH/, and the planner, dispatcher, service allow-list and the
deployment test seams now name frank-medium.

scripts/frank-small.env

@@ -1,56 +0,0 @@
-# Tunables for frank-small (batsclamp/Huihui-Qwen3.6-35B-A3B-
-# abliterated-FP8, qwen3_5_moe hybrid MoE, Dankserver, RTX 3090).
-#
-# Runtime: FreeToken v0.1.2 (edge-native MoE serving engine).
-# Installed via uv into /home/dank/freetoken/.venv (CPython 3.12).
-# FreeToken keeps expert weights in host RAM with an LRU GPU cache;
-# KV is paged with radix prefix sharing.  Only the 10 full-attention
-# layers page KV (~20 KiB/token); the 30 GDN layers use constant-size
-# per-sequence state (mamba slot pool).
-#
-# Model: batsclamp/Huihui-Qwen3.6-35B-A3B-abliterated-FP8 (HuggingFace
-# safetensors).  Abliterated (refusal-removed) Huihui finetune of
-# Qwen3.6-35B-A3B, quantized to the SAME native format the previous
-# official checkpoint used: modelopt block-fp8 e4m3, dynamic
-# activations, 128x128 weight_block_size, bf16 weight_scale_inv;
-# lm_head/embeds/norms/router gates/visual/MTP kept BF16.  Un-fused
-# per-expert layout matches the official Qwen FP8, so the FreeToken
-# loader path is identical; ~35 GB on disk.  Ships the fixed Qwen3.6
-# chat template (froggeric/Qwen-Fixed-Chat-Templates) — FreeToken
-# reads it from the checkpoint; no template override needed.
-# Replaced Qwen/Qwen3.6-35B-A3B-FP8 on 2026-08-30; pool numbers
-# unchanged — same arch, same weight mass.
-#
-# Pool sizing: GPU budget (~17.6 GiB after weights + fixed caches) is
-# split MoE-first.  MOE_CACHE_SIZE pins resident expert slots; NUM_TOKENS
-# pins the KV pool.  At 2048 slots / 453005 tokens the split is ~20%
-# residency (2048/10240) and ~8.6 GiB KV.  Live resize via
-# `ft ctl cache --kv N --moe M` can grow KV by shrinking MoE without
-# restart (the reverse requires a restart).
-#
-# WARNING: do not name any variable TEMP, HOST, or GPU_MAX_HEAP_SIZE.
-# The ROCm HIP runtime reads those bare env vars (see the 2026-08-13 TEMP
-# incident).  This box is NVIDIA/CUDA, but the rule keeps the env portable.
-
-# Model directory (safetensors checkpoint, ~35 GB, block-fp8).
-MODEL_DIR=/home/dank/models/huihui-qwen36-35b-a3b-abliterated-fp8
-
-# FreeToken venv (uv-managed CPython 3.12, ft CLI, flashinfer, torch+cu130).
-FT_VENV=/home/dank/freetoken/.venv
-CUDA_HOME_DIR=/home/dank/freetoken/.venv/lib/python3.12/site-packages/nvidia/cu13
-
-ALIAS=frank-small
-BIND_ADDR=0.0.0.0
-LISTEN_PORT=7770
-
-# Expert residency: 2048 of 10240 total expert instances (20%).
-# Each slot ~3 MiB → ~6.0 GiB VRAM for the MoE cache.
-MOE_CACHE_SIZE=2048
-
-# KV pool: 453005 tokens (~8.6 GiB at ~20 KiB/token).
-# Covers 4 concurrent sessions at ~113k unique context each
-# (more with radix prefix sharing for common prefixes).
-NUM_TOKENS=453005
-
-# Max concurrent decoding requests (also caps mamba slot allocation).
-MAX_RUNNING_REQUESTS=4