serverAppConfigs/serverAppConfigs-docs/LLM/frank-small-llama-swift15-iq3s-mtp-131072.md

---
type: reference
title: "frank-small on Dankclaw — llama.cpp, Swift-1.5-27B GSQ-RCO IQ3_S-mtp, 1 x 131072, q8_0 KV"
description: "The active frank-small runbook (2026-10-05 evening) — llama.cpp master (CUDA sm_86) serving ukisai's Swift-1.5-Qwen3.8-27B GSQ-RCO IQ3_S WITH the in-file MTP head on the RTX 3090: one 131,072-token conversation at Frank's 128k cap, q8_0 K + q8_0 V (llama.cpp's FP8 class), vision through the base-model projector (proven byte-identical tower), and the arithmetic that makes one slot the maximum."
tags:
  - llm
  - frank-small
  - dankclaw
  - llama-cpp
  - gguf
  - rtx3090
  - qwen35
  - mtp
  - vision
timestamp: 2026-10-05
---

# frank-small — llama.cpp Swift-1.5-27B IQ3_S-mtp, 1 × 131,072, q8_0 KV

> **ACTIVE since 2026-10-05 (evening).** A 27B replaces the 9B on the same
> 24 GiB card, bought by Frank's explicit contract for this swap: context
> capped at 128k per conversation, KV at FP8 class, vision on, "as many of
> those contexts as you can" — the answer turned out to be **one**, by
> arithmetic, not by choice (see *Sizing*). This supersedes BOTH earlier
> 2026-10-05 payloads: the live MiMo-V2.6 Q6_K shape
> ([[frank-small-llama-mimo-q6k-2x200704]], now retired) and the Ornith-1.5-9B
> Q8_0 swap that was committed (77decb5..380aafa) but never deployed.

## Identity

| | |
|---|---|
| Host / device | Dankclaw (192.168.0.143), RTX 3090, 24,576 MiB |
| Endpoint | `http://192.168.0.143:7771` — alias `frank-small`, `/v1` OpenAI surface |
| Runtime | llama.cpp master (0.5.0-dev, commit 5fc4f3c), CUDA sm_86 build `…/llama-master/build-cuda/bin/llama-server` (`GGML_CUDA_FA_ALL_QUANTS=ON` — required for quantized V) |
| Unit | root systemd `frank-small.service` ([`scripts/frank-small.service`](../../scripts/frank-small.service)), `User=dank`, `Restart=on-failure`, `LimitMEMLOCK=infinity` |
| Serve script | [`scripts/serve-frank-small.sh`](../../scripts/serve-frank-small.sh), tunables [`scripts/frank-small.env`](../../scripts/frank-small.env) |
| Weights | `/home/dank/models/Swift15-IQ3S/Swift-1.5-Qwen3.8-27B-GSQ-RCO-IQ3_S-mtp.gguf` — ukisai/Swift-1.5-Qwen3.8-27B-GSQ-RCO-GGUF **IQ3_S -mtp**, 12,120,016,896 B, sha256 `9aecf1cd…` (qwen35 hybrid: 64 real layers, 16 full-attention + 48 Gated DeltaNet, native 262,144; **MTP head in the file**: blk.64.nextn.*, `nextn_predict_layers=1`) |
| Projector | `…/Swift15-IQ3S/mmproj-Qwen3.8-27B-F16.gguf` — unsloth/Qwen3.8-27B-GGUF `mmproj-F16.gguf`, 927,607,488 B, sha256 `cbb841a9…` (27-layer ViT, 1152 hidden, `qwen3vl_merger` → 5120) |
| Fetch | [`scripts/download-frank-small.sh`](../../scripts/download-frank-small.sh) — `-f`, byte count + LFS sha256 verified per file; both verified on the host before deploy |
| Seam | `tests/frank_small_unit.bats` (argv assertions against a stub engine: payload path, slot geometry, KV pair, MTP pair, batch pair, projector opt-out) |

## Why a base-model projector is legal here

No Swift repo ships a projector — ukisai's GSQ-RCO release is
language-model-only by their own README ("A Swift 27B vision projector has
not been verified for this release"). The substitution is proven, not
assumed: Swift-1.5 is an RL/OPD post-train of `Qwen/Qwen3.8-27B`, and its
vision stack was hash-compared against the base on 2026-10-05 over HTTP
range reads of the two BF16 repos — **333/333 visual tensor names match and
6/6 sampled tensors are byte-identical** (merger `linear_fc1`/`linear_fc2`,
blocks 0/13/26 attn/mlp). Swift's post-training touched text only, so the
base projector IS the model's own projector. The mmproj header matches
Swift's `vision_config` field-for-field (depth 27, hidden 1152, patch 16,
merge 2, ffn 4304, 16 heads, out 5120).

## Shape and KV contract

`--ctx-size 131072 --parallel 1` — ONE conversation at Frank's 128k cap
(the GSQ publisher's own benchmark protocol ran at 131,072). `CTX_SIZE`
stays divisible by `PARALLEL`; the seam enforces it.

**KV at FP8 class: `q8_0` K + `q8_0` V (Frank, 2026-10-05 evening).**
llama.cpp has no true fp8 KV dtype; q8_0 is the 8-bit class (1.0625 B/elem
⇒ 8.5 effective bits) and the only FP8-class lever this runtime moves. The
order supersedes the same-day f16/f16 rule, which was written for the
Ornith 9B fit — at 64 KiB/token f16 the single 131,072 slot alone would
cost 8.0 GiB of KV. At q8_0 the rate is **34 KiB/token** (16 attn layers ×
4 KV heads × 256 dim × 1.0625 B, K and again V). `--flash-attn on` is
pinned: quantized V requires it, and the build's
`GGML_CUDA_FA_ALL_QUANTS=ON` (2026-09-17) makes the path real rather than a
silent f16 fallback.

**One slot is the maximum, by arithmetic.** Card 24,576 MiB; weights
11,561 MiB; projector ~1,350 MiB (927,607,488 B at the measured ~1.45× card
law); compute buffers at ubatch 1024 ~1.6 GiB; CUDA ~500 MiB; DeltaNet
state ~150 MiB/slot (48 linear layers × 48 value heads × 128×128 × 4 B); MTP
draft context ~0.3–0.5 GiB. Non-KV ≈ 15.5 GiB. One q8_0 slot = 4,352 MiB ⇒
free ≈ 4.5 GiB. A second slot wants +4,352 MiB ⇒ ~0.2 GiB free, far under
the 2–3 GiB floor. The pool is capped by the 128k law, not by the card —
leftover headroom is honest slack, not an undersized pool.

## MTP (speculative decoding)

The MTP head ships **in the file** — the `-mtp` GGUF carries
`blk.64.nextn.{enorm,hnorm,shared_head_norm}` + the head layer's tensors at
Q6_K/F32 (publisher RECIPE: "MTP_OK verified"),
`qwen35.nextn_predict_layers=1`. `--spec-type draft-mtp` loads it against
the main model's own weights — no `--model-draft`, no second draft KV pool
(an MTP head can NEVER load as a separate draft model on this stack; two
context-backed drafters cannot share a process). Depth 3
(`--spec-draft-n-max 3`) matches the fleet's radiance MTP depth. The flags
move as a set: a no-spec baseline must strip BOTH. Acceptance reads from
the response `timings` object (`draft_n`, `draft_n_accepted`) — measure on
real traffic, never on a synthetic repeat (greedy-text acceptance lies).

## Vision contract

`--mmproj` is the entire vision path (the GGUF carries no vision tensors).
Probes: a solid-color PNG **≥ 256 px** answers its exact color — a tiny
probe undersupplies image tokens and the model invents ("video"); the
legacy top-level `images` field is IGNORED, only the OpenAI `image_url`
content part reaches the model. Same traps the MiMo eras documented.

## Client contract

- Reasoning in `reasoning_content` (`--reasoning-format deepseek`); thinking
  tokens COUNT against `max_tokens`.
- The embedded chat template gates `reasoning_effort` to
  `{xhigh, medium, low}` — anything else is a raise/HTTP 500, so
  `models.yml` `compat.reasoningEffortMap` folds the harness levels onto
  that set (mandatory, same as every Qwen3.8-template endpoint).
- Sampling rides the wire (`extraBody`): Swift's publisher recipe temp 1.0
  / top_p 0.95 / top_k 20 / min_p 0 (the card was evaluated at exactly
  this recipe at ctx 131,072).
- Swift 1.5 is a reasoning-efficient post-train: −58.5 % median thinking
  tokens vs base at equal-or-better accuracy. Token budgets go further;
  the 32,768 `maxTokens` is unchanged from the MiMo era and still clears
  the compaction invariant (91,750 + 32,768 ≤ 131,072).
- The GSQ-RCO "IQ3_S" tier is a **per-tensor allocation mix** (IQ2_XS 144 /
  IQ3_XXS 96 / Q6_K 78 / F32 360 / BF16 96 / Q3_K_M 39 …), all stock quant
  types — stock llama.cpp loads it. Publisher KLD vs Swift BF16: 0.0513
  (development), held-out 0.024–0.052 per domain; the MTP packaging is NOT
  separately evaluated by the publisher.

## Rollback

Two payloads on disk, both fetchable by the same script's git-history
recipes:

1. **MiMo-V2.6 Q6_K 2×200,704 f16 KV** — the payload that was LIVE until
   this cutover: `/home/dank/models/MiMo-Q6K/`; revert
   [[frank-small-llama-mimo-q6k-2x200704]] (env trio at commit e5893dc or
   380aafa's parent), one `MODEL_FILE` + shape edit.
2. **Ornith-1.5-9B Q8_0** — the committed-but-never-deployed swap:
   `/home/dank/models/Ornith-Q8/`; revert to 380aafa's tree (ONE native
   262,144 slot, f16/f16 KV).

## Trail

- The Ornith-1.5-9B Q8_0 swap (77decb5 → 380aafa, earlier 2026-10-05) was
  committed and pushed but its deploy never completed (blocked at the
  whole-tree docs gate); the live service was still MiMo Q6_K when Swift
  cut over. Both Ornith payloads were verified on disk before Swift.
- Swift header facts (block_count 65, interval 4, KV geometry, nextn) were
  read from the Hub file by HTTP range + struct parse — no download
  needed — before the payload was fetched.