agent: repoint roles to frank-medium/dankclaw-3090; sync niri tweaks, memories, managed skills
- config.yml: default/commit -> frank-medium (dankserver); task/tiny/smol/vision -> frank-small (dankclaw-3090); vision xhigh - models.yml: add llamacpp (dankclaw-3090) provider; frank-large preserve_thinking + context notes - niri: focus-ring/border/shadow layout, hot-corners off, re-add noctalia.kdl include; blankpad eDP-1 scale 1.0 - memories: flush rollouts; retire frank_large + marreta_authentik skills; add auth-chain/kv-pool/quadlet-log skills - managed-skills: 40+ new fleet runbook skills - agent/skills: import built-in skill set
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Frank
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@@ -2,19 +2,52 @@
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# - apiKey "EMPTY" -> auth: none (keyless local endpoint)
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# - thinkingLevelMap {max: xhigh} -> compat.reasoningEffortMap
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providers:
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# The provider NAMES are historical (they were llama.cpp endpoints when the
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# fleet ran llama.cpp): frank-medium is vllm-radiance now, and frank-large is
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# halogen-flash-server again. Renaming is deliberately avoided because every
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# modelRoles entry in config.yml embeds the provider name, and a partial
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# rename fails at call time — the label is cosmetic, the references are not.
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# One provider per HOST:PORT, so Dankclaw's second endpoint (:7771) is a
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# separate block rather than a second model inside the :7770 one.
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"llamacpp (dankserver)":
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baseUrl: http://192.168.0.235:7770/v1
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api: openai-completions
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auth: none
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models:
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- id: frank-small
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name: "frank-small (dankserver)"
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contextWindow: 262144
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maxTokens: 262144
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- id: frank-medium
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name: "frank-medium (dankserver)"
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# Dankserver: both Radeon AI PRO R9700 (gfx1201) serving one model —
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# mainline llama.cpp, `--split-mode layer`, on
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# ajgazin/Swift-Qwen3.8-27B-Uncensored-Dynamic-MTP at UD-Q8_K_XL with
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# the z-lab DFlash2 Q8_0 drafter. Tensor parallelism is impossible on
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# this host (the second card's chipset path has no PCIe atomic routing,
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# so it cannot dispatch an RCCL collective — and vLLM's pipeline
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# parallelism needs the same p2p primitive), which is why one process
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# layer-splits across both cards instead. Since 2026-09-16 this is the
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# box's only LLM runtime; the vllm-radiance replica pair is retired.
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contextWindow: 196608
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# The KV pool is 393216 positions over TWO slots, so one conversation
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# carries 196608 -- the house rule (see frank-large) is to declare the
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# per-conversation window, not the pool. The checkpoint's trained
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# window is 262144 and the GGUF has no rope scaling, so the top 131k
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# of a conversation extrapolates on the base rope. 32768 keeps prompt
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# + budget comfortably inside it; thinking counts against the budget.
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maxTokens: 32768
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reasoning: true
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input: [text]
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input: [text, image]
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compat:
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# Qwen3.8's chat template accepts ONLY xhigh, medium and low for
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# reasoning_effort and RAISES on anything else — verified against this
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# endpoint on 2026-09-16: low/medium/xhigh answer 200, `high` returns
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# a 500 from the template. The harness's levels must therefore be
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# folded onto that set, exactly as the sibling frank-small entry does,
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# or any role whose level is unmapped fails on its first request
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# (subagents run at `high`).
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reasoningEffortMap:
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minimal: low
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low: low
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medium: medium
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high: xhigh
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max: xhigh
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"llamacpp (dankclaw)":
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@@ -24,20 +57,25 @@ providers:
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models:
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- id: frank-large
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name: "frank-large (dankclaw-gpu)"
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contextWindow: 200000
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# Halogen caps max_tokens at HALOGEN_MAX_TOKENS_CAP=65536 (a request
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# above it is a 400). Admission reserves prompt + max_tokens against
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# the KV pool (401,408 positions since 2026-09-13): 200,000 + 65,536
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# = 265,536, comfortably inside. Overshooting the pool hangs for the
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# 3600 s queue timeout before a 503.
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# halogen-flash-server. The model's native window is 262,144, but this
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# deployment caps a conversation at 200,192 — exactly half of its
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# 400,384-position KV pool, so two 200k conversations are resident at
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# once. Declaring the half rather than the native window is what keeps
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# prompt + budget inside one conversation's region of the pool.
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contextWindow: 200192
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# HALOGEN_MAX_TOKENS_CAP: above this a request is a 400 rather than a
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# silent truncation, and thinking counts against the budget.
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maxTokens: 65536
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reasoning: true
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input: [text, image]
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# Text only as of 2026-09-16: the vision tower moved to frank-small and
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# this server refuses image content parts.
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input: [text]
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compat:
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reasoningEffortMap:
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max: xhigh
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# Qwen3.8-Flash-Next recommended sampling; halogen has no server-side
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# sampler defaults, so without this the endpoint decodes greedy.
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# Qwen3.8-Flash-Next recommended sampling. Halogen has no server-side
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# sampler defaults (a request that sends no temperature decodes
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# greedily), so this override is what makes ordinary calls sample.
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# extraBody is merged last (after compat policy) and wins.
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extraBody:
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temperature: 1.0
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@@ -47,5 +85,52 @@ providers:
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presence_penalty: 0.0
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repetition_penalty: 1.0
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# Keep prior thinking blocks in the rendered prompt across turns;
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# halogen honours it top-level (verified: 275-token prompt delta vs false).
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# halogen honours this key top-level (verified: 275-token prompt
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# delta with it false).
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preserve_thinking: true
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"llamacpp (dankclaw-3090)":
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baseUrl: http://192.168.0.143:7771/v1
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api: openai-completions
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auth: none
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models:
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- id: frank-small
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name: "frank-small (dankclaw-3090)"
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# Dankclaw's RTX 3090: mainline llama.cpp serving
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# HauhauCS/Gemma4-12B-QAT-Uncensored-HauhauCS-Balanced at Q4_K_M.
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# The MTP head ships as a separate assistant sidecar
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# (mtp-gemma-4-12B-it.gguf) loaded via --model-draft and chained
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# behind the draftless ngram-mod matcher; q8_0 KV with the model's
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# sliding-window pattern keeps each full 262144-token window cheap,
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# and the explicit CTX_SIZE=1048576 / PARALLEL=4 gives FOUR full
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# windows (measured 22.3 GiB of the 24 GiB card at load; a 5th slot
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# does not fit). The BF16 projector (with audio encoder) is loaded
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# too, so this stays multimodal (frank-medium gained vision on
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# 2026-09-17 as well; frank-large has no vision tower).
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contextWindow: 262144
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# Each slot IS the full trained window: prompt + budget must fit
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# inside it, and thinking counts against the budget.
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maxTokens: 32768
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reasoning: true
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input: [text, image]
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compat:
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# Gemma4's chat template accepts every reasoning_effort level as of
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# 2026-09-17 (all seven probed: 200), so this map is inert. Kept
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# anyway: it cost nothing, and it is the safety net if the template
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# ever gates again the way Qwen3.8's did (subagents run at `high`).
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reasoningEffortMap:
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minimal: low
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low: low
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medium: medium
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high: xhigh
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max: xhigh
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# Gemma's reference local-recipe sampling (temp 1.0 / top_p 0.95);
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# the server's own default is greedy for clients that stay silent.
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# Thinking is already retained across turns by the server's
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# --reasoning-preserve default, and the key below is accepted
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# top-level (verified 200, not a 400), so it is passed to pin the
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# behaviour at the request rather than rely on a server default.
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extraBody:
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temperature: 1.0
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top_p: 0.95
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preserve_thinking: true
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