Dankclaw's endpoints are re-split. frank-large is the halogen-flash-server
quadlet again -- the 2026-09-13 unit file was never touched by the two-day
ik_llama.cpp interlude, so the runtime was still in the repo and its image is
still in local storage -- retuned to the shape asked for: HALOGEN_CTX 200192
with a 400,384-position pool and two slots, exactly two 200k conversations.
frank-small is new: mainline llama.cpp on the RTX 3090 serving
ISTA-DASLab/Qwen3.8-27B-GSQ-RCO-GGUF at IQ3_S, using the -mtp build (it carries
blk.64.nextn.*, which is what lets --spec-type draft-mtp run on the main
model's own weights instead of a second 12 GiB copy) plus the BF16 vision
projector, as one 131,072-token slot with f16 KV on port 7771.
The host is shared now, and that is what the halogen retune pays for:
HALOGEN_MAX_TOK 24576 (arena 20.6 -> 16.4 GiB, ~4-5% prefill) and the vision
tower dropped (1.97 GiB) hand ~6 GiB back to the host, and the reserve rises to
20 to keep the fit check honest. Vision moved wholesale to frank-small.
Retired to scripts/.AGENTTRASH/: the ik trio (env, service, serve script), the
Qwen-Sharp template only that runtime read, and four benchmark harnesses bound
to retired runtimes. Because retiring a file makes any test that greps it pass
by absence, the seams assert the archive exists AND the live path does not.
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.
frank-default-large -> frank-large (Dankclaw :7770), frank-default-small ->
frank-small (Dankserver :7770), frank-default-sightsound -> frank-npu
(Dankclaw :7771). Units, env files, serve scripts, deploy-plan mappings,
deploy-remote dispatch (stop/disable old units + relink + enable new), and the
test seams move together. Retired frank-default-npu.* artifacts moved to
scripts/.AGENTTRASH/ (tracked-in-history; ignored on disk).
Served aliases become frank-large / frank-small via --alias; the NPU endpoint
serves the whole FLM registry, so its rename changes no served tags.
Two live-verified loader failures on Dankserver (2026-09-01), each a
clean exit at load (no swap-thrash, 37 GB fits RAM):
1. ornith-ai/Ornith-1.5-35B-A3B-FP8 (compressed-tensors PER-CHANNEL fp8):
ValueError: Missing MoE expert source layers: {'gate_up': [0, 1, ...]}
— nonstandard expert tensor naming, provider finds no expert sources.
2. lamianlbe/Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive-fp8
(compressed-tensors BLOCK-fp8 128x128, standard per-expert naming):
RuntimeError: The size of tensor a (2048) must match the size of
tensor b (...) in _dequant_fp8_weight — the dequant path multiplies
weight * weight_scale directly and cannot expand compressed-tensors
block scales.
Empirical matrix for FreeToken v0.1.2 on qwen3_5_moe (RTX 3090):
modelopt block-fp8 (quant_method 'fp8' + weight_scale_inv) -> LOADS
compressed-tensors per-channel FP8 -> fails
compressed-tensors block-fp8 128x128 -> fails
batsclamp/Huihui-...-abliterated-FP8 is the proven uncensored checkpoint;
restoring it until a modelopt/NVFP4 requant of a wanted model exists or
FreeToken learns compressed-tensors.
Ornith-1.5-35B-A3B-FP8 failed to load on FreeToken v0.1.2: its
compressed-tensors checkpoint uses a nonstandard expert tensor layout
(FreeToken: 'Missing MoE expert source layers: gate_up'). Retarget to
the HauhauCS Aggressive FP8 finetune of the SAME Qwen3.6-35B-A3B arch
Huihui used, which carries compressed-tensors BLOCK-fp8 (128x128) with
the standard per-expert naming (mlp.experts.N.gate_proj/up_proj/down_proj
+ .weight_scale) that FreeToken's MoE loader reads.
- MODEL_DIR -> /home/dank/models/hauhaucs-qwen36-35b-a3b-aggressive-fp8
- use the checkpoint's own chat_template.jinja (drop the Sharp override;
forcing a template the finetune wasn't trained on risks format drift)
- pool plan unchanged (same ~35 GB FP8 mass / qwen3_5_moe arch)
- MODEL_DIR -> /home/dank/models/ornith-1.5-35b-a3b-fp8 (37 GB,
compressed-tensors per-channel FP8, same qwen3_5_moe arch/mass as
Huihui so the pool plan carries unchanged)
- CHAT_TEMPLATE_FILE -> scripts/chat-template.jinja (Qwen-Sharp, shared
with frank-default-large); the checkpoint's stock template
mis-serializes reasoning per its own model card
- env/serve/unit headers updated; loader-risk + BF16-does-not-fit note
- same qwen3_5_moe arch and native block-fp8 format (e4m3, 128x128
weight_block_size, un-fused per-expert layout) as the official
Qwen FP8 checkpoint: identical FreeToken loader path, pool numbers
unchanged (2048 MoE slots / 453005 KV tokens / 4 requests)
- checkpoint downloaded to /home/dank/models/huihui-qwen36-35b-a3b-abliterated-fp8
(26 shards, ~35G); official checkpoint kept for rollback
- ships froggeric fixed Qwen3.6 chat template, read from checkpoint
- also refresh both .service Descriptions (large still said STRIX_LEAN)
--ctx-size is the total pool divided among --parallel slots, so with
PARALLEL=3 the 160k target must be a 491520 pool (163840/slot) to
actually deliver 160k context per slot. Env, serve default, bats updated.
Reconfigure to 3 parallel 160000-ctx slots (was 1 slot). KV cache is
now asymmetric: K at q8_0, V at q5_0, both with Hadamard. The 3 slots
triple the KV, so expert blocks 27-34 are pinned back to CPU (freed
~8.7 GiB) leaving output-adjacent 35-40 on CUDA. Q8_0 model,
split-mode none, spec disabled (ik limits spec to 1 slot). AGENTS.md:
drop the scratch-port rule (test/bench processes cannot share the
single GPU with the running service anyway). Env, serve script, bats,
docs updated.
Switch to CRACK Q8_0 model (37.8 GB, mmproj f16). Servt as a single
160000-ctx slot (PARALLEL=1); speculative decoding stays disabled. KV
cache is q6_0 + Hadamard. iq5_k is not a valid KV-cache type in ik_llama
(cache whitelist excludes iq5_*); q8_KV reproduces the ggml.c:5427 view
assert even at PARALLEL=1/no-spec, so q6_0 is the verified working 6-bit
choice. Env, serve script, tests, and KB updated. Deployment was left
down after the isolation test; this commit redeploys it with the
verified Q8_0/160k/q6_0 config.
--jinja: ik_llama only renders a custom --chat-template-file as real Jinja
when --jinja is passed (inputs.use_jinja ? apply_jinja : non-jinja
fallback). Without it the Qwen-Sharp template's thinking/image/tool
formatting is silently dropped. Add USE_JINJA=1 -> --jinja.
Experts: the RTX 3090 had ~5.4 GiB headroom; pull expert blocks 30,32,33,34
back to CUDA (each block ~630 MiB) from CPU for lower latency. Update the
OVERRIDE_EXPERT regex, its test, and the KB with the headroom-check note.
ik_llama limits speculative decoding to a single server slot (-np 1) and
hard-exits at load if a spec stage is configured with --parallel > 1:
"Speculative decoding is currently limited to a single server slot
(-np 1)." This service keeps PARALLEL=3 (one slot per user), so set
SPEC_TYPE=none to disable speculation. It is a low-parameter MoE (3B
active of 35B), so throughput is fine without it. MTP stays disabled.
KB and tests updated.
The ik_llama fast q8_KV cache type crashes at model init on this
QWEN3MOE/MTP + multimodal arch at commit b166e269 with a ggml_view size
assert (ggml.c:5427) right after the output buffer is created, looping
the service. Empirically isolated by toggling cache type: q8_0 and f16
load and serve (smoke-tested correct output), q8_KV asserts. Switch to
q8_0 + Hadamard (K/V), which still gives the 8-bit cache + Hadamard
quality win. KB updated with the crash lesson.
--split-mode graph requires >= 2 devices in ik_llama.cpp and errors hard
at model load on a single RTX 3090 ("It is not possible to use split mode
'graph' with less than 2 devices"), looping the service. Set SPLIT_MODE
=none (the default), which works with the --override-tensor CPU expert
offload. KB updated with the two-device requirement so we do not assume
graph degrades gracefully on one GPU.
Switch frank-default-small on Dankserver from upstream llama.cpp (b10121)
to ikawrakow/ik_llama.cpp (b166e269, built with the staged pip cu13
toolkit). The model (Ornith-1.5-35B-A3B CRACK Q6_K) is unchanged and the
old binary is kept for rollback.
Runtime changes:
- KV cache quantized to q8_KV with --k-cache-hadamard/--v-cache-hadamard
- --split-mode graph (single-GPU no-op, retained per operator pref)
- migrate ngram speculation to canonical
--spec-type ngram-mod:n_min=48,n_max=64,ngram_size_n=24
(legacy --spec-ngram-mod-* are rejected in ik main)
- N_GPU_LAYERS 999 (ik -ngl rejects the literal "all"); experts stay
pinned to CPU via --override-tensor
Build: GGML_CUDA_FA_ALL_QUANTS=ON + GGML_IQK_FA_ALL_QUANTS=ON mandatory
for the quantized KV cache; recipe + reasoning baked into
docs/LLM/ikllama-ornith.md. Tests and both indices updated.
- frank-default-small (Dankserver): Ornith-1.5-35B-A3B-CRACK Q8_0, bundled
native draft-mtp (n_max 4), MoE expert FFN tensors pinned to CPU via an
explicit --override-tensor \.ffn_(up|down|gate|gate_up)_(ch|)exps=CPU
(replaces --n-cpu-moe), Qwen-Sharp chat template.
- frank-default-large (Dankclaw): kingjones Qwen3.8-27B-Uncensored-Q6_0
ROCmFP4 AGENT dense GGUF, standalone mtp-Qwen3.8-27B-Q4_0 draft head
(--model-draft, draft-mtp n_max 4), Qwen-Sharp chat template. Drop the
retired PromptForge .pfs sidecar lanes.
- Track scripts/chat-template.jinja (v22.4.0 Sharp) and apply via
--chat-template-file on both services.
- Docs: new kingjones-strix-mtp and dealign-ornith-crack runbook pages;
update index, deployment, and both system specs; models.json names.
Smoke-tested both models on the real hardware on scratch ports before
commit: model loads, MTP draft attaches, Sharp template applies, chat
completion returns.
The LLM fleet serve scripts, systemd units, env tunables, and bench
harnesses join the deploy scripts. The vllmScripts deploy.sh is
removed here; its functionality is folded into the unified
scripts/deploy.sh in a follow-up commit.
bench-spec-stack.sh gains the executable bit (required by the
scripts_syntax test seam).
Frank