Frank
e044700ef7
Named presets silently ignore leftSegments/rightSegments - the nerd preset's baked arrays were what we saw, and the configured arrays were dead. preset: custom with explicit arrays: chip (right) = token_in, token_out, cost, context_pct, time_spent; strip (left) = session_name, vim, mode, hostname, model, path, git (branch text off - worktree branches are autogenerated; dirty badges kept), pr. Cut tok/s duplicate, clock, cache trio, context_total, subagents, session hash. Composer shape stays claude after live box/band/rule trials. models.yml display name frank-archon drops the (dankserver) suffix; id untouched. Docs: pi/statusline.md records the preset-vs-custom trap, shape->segment routing, and the context-meter truth.
233 lines
12 KiB
YAML
233 lines
12 KiB
YAML
# OMP agent model registry — single source of truth (OMP is the primary
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# harness; there is no second copy to keep in sync).
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# Conversion notes from the pi-era import:
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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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# Provider NAMES are labels, and every reference to them lives in config.yml
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# (modelRoles, agentModelOverrides) or in another model's compactionModel —
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# rename one and a missed reference fails at call time. So each label is
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# migrated with its model in the same edit. One provider per HOST:PORT, so
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# Dankclaw's second endpoint (:7771) is a separate block rather than a second
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# 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-archon
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# Model id renamed from frank-primary on 2026-10-08 (official
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# unit-name scheme; the systemd service, scripts and runbooks keep
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# the frank-primary name — service names are unchanged).
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# Server-side compaction routes to compaction.remoteEndpoint in
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# config.yml (192.168.0.143:7771 — frank-zealot's 3090); the
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# compactionModel candidate keeps the wire model id valid there.
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compactionModel: llamacpp (dankclaw-3090)/frank-zealot
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name: "frank-archon"
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# Dankserver: BOTH Radeon AI PRO R9700s (gfx1201, TP=2 over the
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# 2026-09-20 board swap's root ports, wht6 wire). Engine:
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# radiance (StillDeadcode, Apache-2.0) from the digest-pinned
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# stilldeadcode/radiance image (index digest sha256:4c3f4845..., 287
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# MiB, kernels prebuilt for gfx1201), plain `podman run` through
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# scripts/serve-frank-primary.sh, root unit (User=dank, dank's
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# rootless podman storage). Serving the OFFICIAL
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# Qwen/Qwen3.8-Flash-Next-FP8 at NATIVE modelopt block-FP8
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# precision — a rad-convert no-recipe pure copy into
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# qwen4exp-fp8-native.rad (172.76 GiB measured by plan-only), built by
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# scripts/build-frank-primary-rad.sh against a staging dir whose
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# config.json drops quantization_config so the shipped qwen4exp
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# plugin's (qwen4exp, "") selection key matches; per-weight
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# encodings are untouched. Experts stream from a 40 GiB/rank
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# pinned host pool; the 47.7 GiB PLE ngram table is pinned in RAM
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# (Frank's call); KV fp8; MTP depth 3 (the checkpoint's own mtp.*
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# head); prefix cache 4 GiB host + 64 GiB disk. Chat template:
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# froggeric's fixed qwen3.8 v22.5
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# (scripts/qwen38-froggeric-v22.5.jinja) via
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# --override-chat-template. Rollback: git tag
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# frank-primary-freetoken-pr587-20261005 (FreeToken, one card).
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contextWindow: 200000
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# maxTokens must survive the harness compaction gap AND the chars/4
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# token-estimate under-run on code-heavy prompts. The clamp is
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# max_tokens = min(maxTokens, contextWindow − estimate − 4096); the
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# estimate under-runs by ~3 % on code. Compaction fires at 85 % of
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# the window = 170,000 est; worst-case prompt + budget ≈
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# 170,000 × 1.03 + 16,384 ≈ 191,500 ≤ 200,000 — the engine
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# refuses a longer request, never truncates.
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maxTokens: 16384
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reasoning: true
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# Text + image: the official FP8 checkpoint carries the vision
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# tower (vision_config depth 27; the first blocks stay bf16 in
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# modules_to_not_convert) and the plugin declares it
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# (arch/common/rad_block_vit.h). Video preprocessing configs ship
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# too; images are the contract that matters here.
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input: [text, image]
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compat:
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# radiance's --reasoning-format defaults to auto: thinking
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# comes back in `reasoning_content` (config.cpp). No parser
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# flags exist or are needed.
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reasoningContentField: "reasoning_content"
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# The froggeric template does not gate reasoning_effort the way
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# Qwen's shipped template did, and radiance special-cases only
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# none/minimal (core/server/oai.cpp:609). The map stays as the
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# zero-cost safety net, same as frank-zealot's.
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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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# Qwen recommended sampling. extraBody is merged last and wins.
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# Every key is inside radiance's known-parameter list
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# (core/server/oai.cpp:194-205); unknown keys are refused 400.
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extraBody:
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temperature: 1.0
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top_p: 0.95
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top_k: 20
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min_p: 0.0
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presence_penalty: 0.0
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repetition_penalty: 1.0
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chat_template_kwargs:
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enable_thinking: true
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preserve_thinking: true
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"llamacpp (dankclaw)":
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baseUrl: http://192.168.0.143:7770/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-dragoon
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# Model id renamed from frank-large on 2026-10-08 (official
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# unit-name scheme; the service/unit/scripts keep the frank-large
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# name — service names are unchanged).
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name: "frank-dragoon (dankclaw-gpu)"
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# Server-side compaction runs on the compaction.remoteEndpoint in
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# config.yml (192.168.0.143:7771 — frank-zealot's 3090); this steers the
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# compaction model candidate so the wire model id stays valid
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# there even from a frank-dragoon session.
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compactionModel: llamacpp (dankclaw-3090)/frank-zealot
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# halogen-flash-server 0.16.2 (ghcr.io/peonist-ai/
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# halogen-flash-server@sha256:0c61bf84, digest-pinned) serving the
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# Quat3rnion ABLITERATED Swift1.5 finetune of peonist's
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# Qwen3.8-Flash-Next v2 checkpoint (qwen38-flash-next-v2-swift15-
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# abliterated.hgn, ~62 GiB resident, + REQUIRED n-gram table +
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# vision tower) on the Strix Halo iGPU: TWO slots at
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# HALOGEN_CTX=262144 each with the MTP head ALWAYS ON
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# (HALOGEN_SPEC_ADAPT=0 — the default adaptive policy halves
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# decode). Card: 53-57 t/s greedy, ~1,640 t/s prefill at 34k, ~72%
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# fewer thinking tokens than the censored 27B. ABLITERATED base
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# (refusals should not return) and VISION ON (OpenAI image_url
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# parts; >=256 px images or it answers "video"). Runbook:
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# serverAppConfigs-docs/LLM/frank-large-flash-server-v2-swift15.md.
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# Per-SLOT window the harness may fill — deliberately BELOW the
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# engine ceiling (HALOGEN_CTX=262144): the clamp below leaves a
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# 45k gap that absorbs the estimate error.
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contextWindow: 217088
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# The clamp is max_tokens = min(maxTokens, contextWindow − est −
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# 4096); the chars/4 est UNDER-counts the rendered prompt by ~3 %
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# on code/agent traffic (measured 2026-10-07: est 152,938 ->
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# server 157,388). Worst-case request = ctx − 4096 + 3 % est
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# error ≈ 219k, inside the 262,144 engine ceiling with ~43k
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# margin (robust to a 10 % under-count). Compaction fires at 85 %
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# of the window = 184,524 est ≈ 190k measured (Frank's 180k
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# target) and rides on frank-zealot: input ≈ threshold −
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# keepRecent(20k) ≈ 170k, inside its 200,704 slot.
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# HALOGEN_MAX_TOKENS_CAP in frank-large.env must stay >= this.
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maxTokens: 45056
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reasoning: true
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# VISION ON as of 2026-10-06: the v2 vision tower loads beside the
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# checkpoint (HALOGEN_VISION_TOWER; red-PNG probe verified on the
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# card). image_url content parts only — the legacy top-level
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# images field is the frank-secondary trap, do not repeat it here.
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input: [text, image]
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compat:
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# flash-server emits thinking as `reasoning_content` — same
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# field as every engine this endpoint has run, no harness
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# change needed.
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reasoningContentField: "reasoning_content"
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# Same Qwen3.8 template family (it ships inside the tokenizer
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# bundle): any reasoning_effort outside {xhigh, medium, low} is
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# rejected, not clamped — the fold stays mandatory.
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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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# The card's thinking recipe, on the wire. The flash-server
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# engine HAS a server-side sampler (HALOGEN_TEMPERATURE/TOP_P/
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# TOP_K = 1.0 / 0.95 / 20 in frank-large.env) — these match it,
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# so per-request values are consistent, not load-bearing.
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extraBody:
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temperature: 1.0
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top_p: 0.95
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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-zealot
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# Model id renamed from frank-secondary on 2026-10-08 (official
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# unit-name scheme; the service/unit/scripts keep the
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# frank-secondary name — service names are unchanged).
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name: "frank-zealot (dankclaw-3090)"
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# Server-side compaction routes to compaction.remoteEndpoint in
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# config.yml (192.168.0.143:7771 — this endpoint); the
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# compactionModel candidate keeps the wire model id valid there.
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compactionModel: llamacpp (dankclaw-3090)/frank-zealot
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# Dankclaw's RTX 3090: llama.cpp (master, CUDA sm_86 build) serving
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# HauhauCS/Qwen3.5-9B-Uncensored-HauhauCS-Aggressive at Q6_K
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# (7,359,259,008 B) — qwen35 dense hybrid (32 layers: 8 full-attention
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# + 24 Gated DeltaNet, 4 KV heads x 256 dim, native 262,144), TWO
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# conversations (--ctx-size 401408 / --parallel 2) at Frank's 200k cap
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# each. Exact KV: f16 K + f16 V. Vision via the publisher's OWN
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# BF16 mmproj (921,704,448 B) — the model is natively multimodal.
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# **MTP IS OFF** because the file carries no nextn/mtp tensors —
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# SPEC_TYPE=none strips both spec flags. Priced from the MiMo Q6_K
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# boots at this geometry (21,276/2,849 at ubatch 1024; 21,846/2,279 at
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# 2048). 2026-10-07 (late) swap from Gemma4-26B-A4B Q4_K_M (1x200,704).
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# Runbook:
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# serverAppConfigs-docs/LLM/frank-secondary-llama-qwen35-9b-q6k-2x200704.md.
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# contextWindow is the PER-SLOT window (Frank's cap): a longer
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# prompt needs compaction.
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contextWindow: 200704
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# Prompt + budget must fit inside the slot window; thinking counts
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# against the budget. Compaction fires at 85 % of the window =
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# 170,598 est; worst-case prompt + budget ≈ 170,598 × 1.03 +
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# 16,384 ≈ 192,100 ≤ 200,704 — a longer prompt is a 400 naming
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# the limit, never a truncation.
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maxTokens: 16384
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reasoning: true
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input: [text, image]
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compat:
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# llama.cpp --reasoning-format deepseek emits thinking as
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# `reasoning_content` — the same field the MiMo/Ornith eras used;
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# harness reads unchanged.
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reasoningContentField: "reasoning_content"
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# The Qwen3.5 chat template has NO reasoning_effort variable (unlike
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# the Qwen3.8-template endpoints, where an unmapped level is a hard
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# Jinja raise). llama.cpp passes chat_template_kwargs through to
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# Jinja, and Jinja ignores variables a template never reads — so the
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# map is inert here, kept so role-level overrides resolve identically
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# across the fleet.
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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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# The Qwen3.5 publisher recipe (model card, thinking mode):
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# temperature 0.6 / top_p 0.95 / top_k 20 / min_p 0. Non-thinking
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# mode is 0.7/0.8/20/0 — the harness runs thinking mode.
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# extraBody is merged last and wins over the harness default.
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extraBody:
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temperature: 0.6
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top_p: 0.95
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top_k: 20
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min_p: 0.0
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presence_penalty: 0.0
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chat_template_kwargs:
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enable_thinking: true
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# (no preserve_thinking: that kwarg belongs to the Qwen3.8
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# templates; nothing in this template reads it)
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