# OMP agent model registry — single source of truth (OMP is the primary
# harness; there is no second copy to keep in sync).
# Conversion notes from the pi-era import:
# - apiKey "EMPTY" -> auth: none (keyless local endpoint)
# - thinkingLevelMap {max: xhigh} -> compat.reasoningEffortMap
providers:
  # Provider NAMES are labels, and every reference to them lives in config.yml
  # (modelRoles, agentModelOverrides) or in another model's compactionModel —
  # rename one and a missed reference fails at call time. So each label is
  # migrated with its model in the same edit. One provider per HOST:PORT, so
  # Dankclaw's second endpoint (:7771) is a separate block rather than a second
  # model inside the :7770 one.
  "llamacpp (dankserver)":
    baseUrl: http://192.168.0.235:7770/v1
    api: openai-completions
    auth: none
    models:
      - id: frank-archon
        # Model id renamed from frank-archon on 2026-10-08 (official
        # unit-name scheme; the systemd service, scripts and runbooks keep
        # the frank-archon name — service names are unchanged).
        # Server-side compaction routes to compaction.remoteEndpoint in
        # config.yml (192.168.0.143:7771 — frank-zealot's 3090); the
        # compactionModel candidate keeps the wire model id valid there.
        compactionModel: llamacpp (dankclaw-3090)/frank-zealot
        name: "frank-archon"
        # Dankserver: BOTH Radeon AI PRO R9700s (gfx1201, TP=2 over the
        # 2026-09-20 board swap's root ports, wht6 wire). Engine:
        # radiance (StillDeadcode, Apache-2.0) from the digest-pinned
        # stilldeadcode/radiance image (index digest sha256:4c3f4845..., 287
        # MiB, kernels prebuilt for gfx1201), plain `podman run` through
        # scripts/serve-frank-archon.sh, root unit (User=dank, dank's
        # rootless podman storage). Serving the OFFICIAL
        # Qwen/Qwen3.8-Flash-Next-FP8 at NATIVE modelopt block-FP8
        # precision — a rad-convert no-recipe pure copy into
        # qwen4exp-fp8-native.rad (172.76 GiB measured by plan-only), built by
        # scripts/build-frank-archon-rad.sh against a staging dir whose
        # config.json drops quantization_config so the shipped qwen4exp
        # plugin's (qwen4exp, "") selection key matches; per-weight
        # encodings are untouched. Experts stream from a 40 GiB/rank
        # pinned host pool; the 47.7 GiB PLE ngram table is pinned in RAM
        # (Frank's call); KV fp8; MTP depth 3 (the checkpoint's own mtp.*
        # head); prefix cache 4 GiB host + 64 GiB disk. Chat template:
        # froggeric's fixed qwen3.8 v22.5
        # (scripts/qwen38-froggeric-v22.5.jinja) via
        # --override-chat-template. Rollback: git tag
        # frank-archon-freetoken-pr587-20261005 (FreeToken, one card).
        contextWindow: 200000
        # maxTokens must survive the harness compaction gap AND the chars/4
        # token-estimate under-run on code-heavy prompts. The clamp is
        # max_tokens = min(maxTokens, contextWindow − estimate − 4096); the
        # estimate under-runs by ~3 % on code. Compaction fires at 85 % of
        # the window = 170,000 est; worst-case prompt + budget ≈
        # 170,000 × 1.03 + 16,384 ≈ 191,500 ≤ 200,000 — the engine
        # refuses a longer request, never truncates.
        maxTokens: 16384
        reasoning: true
        # Text + image: the official FP8 checkpoint carries the vision
        # tower (vision_config depth 27; the first blocks stay bf16 in
        # modules_to_not_convert) and the plugin declares it
        # (arch/common/rad_block_vit.h). Video preprocessing configs ship
        # too; images are the contract that matters here.
        input: [text, image]
        compat:
          # radiance's --reasoning-format defaults to auto: thinking
          # comes back in `reasoning_content` (config.cpp). No parser
          # flags exist or are needed.
          reasoningContentField: "reasoning_content"
          # The froggeric template does not gate reasoning_effort the way
          # Qwen's shipped template did, and radiance special-cases only
          # none/minimal (core/server/oai.cpp:609). The map stays as the
          # zero-cost safety net, same as frank-zealot's.
          reasoningEffortMap:
            minimal: low
            low: low
            medium: medium
            high: xhigh
            max: xhigh
          # Qwen recommended sampling. extraBody is merged last and wins.
          # Every key is inside radiance's known-parameter list
          # (core/server/oai.cpp:194-205); unknown keys are refused 400.
          extraBody:
            temperature: 1.0
            top_p: 0.95
            top_k: 20
            min_p: 0.0
            presence_penalty: 0.0
            repetition_penalty: 1.0
            chat_template_kwargs:
              enable_thinking: true
              preserve_thinking: true

  "llamacpp (dankclaw)":
    baseUrl: http://192.168.0.143:7770/v1
    api: openai-completions
    auth: none
    models:
      - id: frank-dragoon
        # Model id renamed from frank-dragoon on 2026-10-08 (official
        # unit-name scheme; the service/unit/scripts keep the frank-dragoon
        # name — service names are unchanged).
        name: "frank-dragoon (dankclaw-gpu)"
        # Server-side compaction runs on the compaction.remoteEndpoint in
        # config.yml (192.168.0.143:7771 — frank-zealot's 3090); this steers the
        # compaction model candidate so the wire model id stays valid
        # there even from a frank-dragoon session.
        compactionModel: llamacpp (dankclaw-3090)/frank-zealot
        # halogen-flash-server 0.16.2 (ghcr.io/peonist-ai/
        # halogen-flash-server@sha256:0c61bf84, digest-pinned) serving the
        # Quat3rnion NATIVE v2 ABLITERATED conversion of orcarouter's pinned
        # BF16 Qwen3.8-Flash-Next (qwen38-flash-next-v2-abliterated.hgn,
        # ~62 GiB resident, + REQUIRED n-gram table + vision tower, both
        # hardlinked from the swift15 dir) on the Strix Halo iGPU: TWO slots
        # over ONE 401,408-position KV pool (= 2 x ctx, raised 2026-10-18) at
        # HALOGEN_CTX=200704 (the ceiling lowered from the card's 262,144 on
        # 2026-10-17; ~10.8 GiB reserved where 200,704 cost 5.5, 262,144 cost
        # 7.2 and 524,288 measured 14.6), with the MTP head ALWAYS ON
        # (HALOGEN_SPEC_ADAPT=0 — the default adaptive policy halves
        # decode). Measured 2026-10-17: 50 t/s greedy decode, 1,604 t/s
        # cold prefill at 59k, NIAH 18/18. ABLITERATED base (no refusals:
        # 0/5 on the hostile census) and VISION ON (OpenAI image_url
        # parts; >=256 px images or it answers "video"). Runbook:
        # serverAppConfigs-docs/LLM/frank-dragoon-flash-server-v2-native.md.
        # Window the harness may fill — deliberately BELOW the engine
        # ceiling (HALOGEN_CTX=200704, lowered from 262,144 on 2026-10-17):
        # the clamp below leaves the 45k gap that absorbs the estimate error.
        # Both slots SHARE one 401,408-position KV pool — exactly two full
        # windows — so TWO maximum-length conversations can be resident at the
        # same time (at the old pool == ctx 200,704 only two of <=100k could).
        # The per-request ceiling is unchanged, so this window is unchanged.
        contextWindow: 155648
        # The clamp is max_tokens = min(maxTokens, contextWindow − est −
        # 4096); the chars/4 est UNDER-counts the rendered prompt by ~3 %
        # on code/agent traffic (measured 2026-10-07: est 152,938 ->
        # server 157,388). Worst-case request = window − 4096 + 3 % est
        # error ≈ 156.2k, inside the 200,704 engine ceiling with ~44k
        # margin (robust to a ~25 % under-count). Compaction fires at 85 %
        # of the window = 132,300 est ≈ 136k measured — the 180k target of
        # the 262,144 era is unreachable now (the window itself is 155,648)
        # — and rides on frank-zealot: input ≈ threshold − keepRecent(20k)
        # ≈ 112k, inside its 200,704 slot.
        # HALOGEN_MAX_TOKENS_CAP in frank-dragoon.env must stay >= this.
        maxTokens: 45056
        reasoning: true
        # VISION ON as of 2026-10-06: the v2 vision tower loads beside the
        # checkpoint (HALOGEN_VISION_TOWER; red-PNG probe verified on the
        # card). image_url content parts only — the legacy top-level
        # images field is the frank-zealot trap, do not repeat it here.
        input: [text, image]
        compat:
          # flash-server emits thinking as `reasoning_content` — same
          # field as every engine this endpoint has run, no harness
          # change needed.
          reasoningContentField: "reasoning_content"
          # Same Qwen3.8 template family (it ships inside the tokenizer
          # bundle): any reasoning_effort outside {xhigh, medium, low} is
          # rejected, not clamped — the fold stays mandatory.
          reasoningEffortMap:
            minimal: low
            low: low
            medium: medium
            high: xhigh
            max: xhigh
          # The card's thinking recipe, on the wire. The flash-server
          # engine HAS a server-side sampler (HALOGEN_TEMPERATURE/TOP_P/
          # TOP_K = 1.0 / 0.95 / 20 in frank-dragoon.env) — these match it,
          # so per-request values are consistent, not load-bearing.
          extraBody:
            temperature: 1.0
            top_p: 0.95

  "llamacpp (dankclaw-3090)":
    baseUrl: http://192.168.0.143:7771/v1
    api: openai-completions
    auth: none
    models:
      - id: frank-zealot
        # Model id renamed from frank-zealot on 2026-10-08 (official
        # unit-name scheme; the service/unit/scripts keep the
        # frank-zealot name — service names are unchanged).
        name: "frank-zealot (dankclaw-3090)"
        # Server-side compaction routes to compaction.remoteEndpoint in
        # config.yml (192.168.0.143:7771 — this endpoint); the
        # compactionModel candidate keeps the wire model id valid there.
        compactionModel: llamacpp (dankclaw-3090)/frank-zealot
        # Dankclaw's RTX 3090: llama.cpp PrismML fork build (vendor 10777,
        # commit e3de71522 — the ONLY loader for this payload's custom tensor
        # type 142 + prism.hadamard.* metadata, and the minimum build that
        # pairs MTP with a quantized V cache) serving
        # prism-ml/Ternary-Bonsai-2-9B-gguf at PQ2_0 (2,675,410,720 B) WITH the
        # MTP head in the file — qwen35 hybrid, 32 layers (8 full-attention +
        # 24 Gated DeltaNet), 4 KV heads x 256 dim, native 262,144 — in THREE
        # conversations (--ctx-size 786432 / --parallel 3), each slot at the
        # model's TRAINED window.
        # KV is q8_0 K + q8_0 V (17 KiB/token over the 8 full layers =
        # 13,056 MiB pool, ledger-exact); f16 KV would bill 26,112 MiB and
        # cannot boot this card. Boot ledger 17,876 used / 6,249 free. Vision
        # through the publisher's Q8_0 mmproj (624,229,664 B — Q8_0 is the
        # publisher's requirement for concurrent image requests), whose
        # worst-case lands outside the engine's fit projection. **MTP OFF —
        # measured, not assumed**: the draft context bills its own f16 KV over
        # the WHOLE pool (3,072 MiB) and at ubatch 2048 that squeeze halves
        # decode (167.2 → 109.2 t/s); SPEC_TYPE=draft-mtp + UBATCH_SIZE=1024 is
        # the 213.5 t/s arm if the card ever has room. 2026-10-18 swap from
        # Ternary-Bonsai-2-27B PQ2_0 at 2 × 200,704. Measured: 167.2 t/s
        # single-stream greedy decode, 280.2 t/s across three concurrent
        # streams, 2,877 t/s prefill at 106,418. Base-alignment: censored base
        # lineage; unlike the 27B there is NO CRACK re-export matching this
        # file. Runbook:
        # serverAppConfigs-docs/LLM/frank-zealot-llama-prism-bonsai9-mtp.md.
        # contextWindow is the PER-SLOT window (= the native 262,144): a longer
        # prompt needs compaction — llama.cpp answers a 400, never truncates.
        contextWindow: 262144
        # Prompt + budget must fit inside the slot window; thinking counts
        # against the budget. Compaction fires at 85 % of the window =
        # 222,822 est; worst-case prompt + budget ≈ 222,822 × 1.03 +
        # 16,384 ≈ 245,891 ≤ 262,144. This endpoint is the fleet's
        # compaction hub: the widest window in the fleet.
        maxTokens: 16384
        reasoning: true
        input: [text, image]
        compat:
          # llama.cpp --reasoning-format deepseek emits thinking as
          # `reasoning_content` — the same field the MiMo/Ornith eras used;
          # harness reads unchanged.
          reasoningContentField: "reasoning_content"
          # Bonsai-2's template DOES read reasoning_effort (the Qwen3.5-9B
          # template did not — the map used to be inert here): it accepts ONLY
          # xhigh (default), medium, low and calls raise_exception on anything
          # else, so an unmapped harness level is a hard HTTP 500 on the role's
          # first request, naming Jinja and not the model. The fold below is
          # therefore load-bearing, identical to the frank-archon/dragoon law.
          # llama.cpp passes chat_template_kwargs through to Jinja.
          reasoningEffortMap:
            minimal: low
            low: low
            medium: medium
            high: xhigh
            max: xhigh
          # The payload's OWN sampling metadata (general.sampling.* in the
          # GGUF): temperature 1.0 / top_p 0.95 / top_k 20 — thinking-mode
          # recipe, the same trio the other Qwen3.8-family endpoints carry.
          # (The old 0.6 was the Qwen3.5-9B publisher's card, not this file.)
          # extraBody is merged last and wins over the harness default.
          extraBody:
            temperature: 1.0
            top_p: 0.95
            top_k: 20
            min_p: 0.0
            presence_penalty: 0.0
            chat_template_kwargs:
              enable_thinking: true
              # The template DOES read preserve_thinking: when false it drops
              # prior assistants' <think> blocks from history. Keep them —
              # multi-turn tool traffic on this endpoint is the majority.
              preserve_thinking: true

  # Speech endpoint — the fleet's TTS, NOT a chat provider. `api:
  # openai-speech` is a runner API (catalog kind `tts`), so this row never
  # enters the chat picker, the Ctrl+P cycle, or any chat role: it is
  # reachable only through the `speech` model role and the tts device.
  # frank-tts (host port 7772) is the OpenAI /v1/audio/speech façade that
  # resolves a voice BY NAME; its engine (frank-tts-engine, container-internal
  # 8880, never published) is arm A qwentts.cpp 5916bf7 — measured 0.55-0.67
  # RTF warm and 86-451 ms to the first audio byte on Dankclaw's CPU, with
  # mainline llama-tts (TTS_BACKEND=llama-tts-cli) kept as the rollback.
  # Transport contract, read out of the harness itself: the request body is
  # {model, input, response_format, voice?, speed?}; `voice` is forwarded ONLY
  # when explicitly set, and sampleRate/bitRate are REJECTED outright (the
  # transport throws "openai-speech does not support sampleRate"), so the
  # codec comes from the output_path suffix and the only tunable that matters
  # is the voice name — which must be a real pack name (narrator1/2/3); an
  # unknown name is an engine error, not a fallback.
  # DELIBERATELY NOT WIRED YET: config.yml `modelRoles.speech` still points at
  # local/kokoro. Flip it to "frank-tts (dankclaw)/frank-tts" only after the
  # service is deployed and has answered a real synthesis, because `auth: none`
  # makes the provider read as AVAILABLE even while the container is down — the
  # role would then fail instead of falling back to Kokoro. The same flip must
  # re-key `speech.voice` (now am_fenrir, a Kokoro voice) to a pack name.
  # SCHEMA LAW, learned the hard way (2026-10-18): provider-level `api` must be
  # a CHAT transport — the models.yml schema accepts only the chat/judge APIs
  # there, and naming a runner API (`openai-speech`) at provider level fails the
  # file's validation, which DISABLES EVERY custom provider: all three fleet
  # endpoints vanish from the registry, not just this one (`omp models` prints
  # "models.yml validation failed — custom providers disabled" and lists only
  # local/kokoro). The runner api belongs on the MODEL entry only, which is what
  # the block below does — `auth: none` covers the credential requirement, and
  # `contextWindow`/`maxTokens` stay unset because the check only fires when they
  # are provided (the bundled Kokoro row is null on both). `omp models --kind tts`
  # still prints 128K/16K for this row — the registry fills display defaults — and
  # the speech transport never reads them: its whole request body is the five
  # fields named above. Verified 2026-10-18 with `omp models --kind tts` (row
  # present) and `omp models` (frank-tts absent from the chat roster, all three
  # fleet providers still listed).
  "frank-tts (dankclaw)":
    baseUrl: http://192.168.0.143:7772/v1
    auth: none
    models:
      - id: frank-tts
        name: "frank-tts (dankclaw) — cloned voices"
        api: openai-speech
        input: [text]