# 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, wht6 wire) on
        # the digest-pinned radiance image, plain `podman run` under a root
        # unit. Payload, shape, eras, laws, rollback:
        # serverAppConfigs-docs/LLM/frank-archon.md — and
        # scripts/frank-archon.env is the authority for every number there.
        # contextWindow MUST quote the engine's --max-model-len: radiance
        # refuses a longer prompt and never truncates.
        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 on the Strix Halo iGPU. Payload, shape, eras,
        # laws, rollback: serverAppConfigs-docs/LLM/frank-dragoon.md — and
        # scripts/frank-dragoon.env is the authority for every number there.
        # contextWindow sits deliberately BELOW the engine ceiling
        # (HALOGEN_CTX): the clamp below leaves the gap that absorbs the
        # harness's chars/4 under-count, so a window equal to the ceiling
        # would make every long request a guaranteed 400.
        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 engine ceiling with ~44k of margin
        # (robust to a ~25 % under-count). Compaction fires at 85 % of the
        # window and rides on frank-zealot: input ≈ threshold −
        # keepRecent(20k), comfortably inside its per-slot window.
        # 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 endpoint (PrismML fork build — the only
        # loader for this payload's custom tensor type). Shape, payload, eras,
        # laws, rollback: serverAppConfigs-docs/LLM/frank-zealot.md, and
        # scripts/frank-zealot.env is the authority for every number there.
        # contextWindow is the PER-SLOT window: llama.cpp divides --ctx-size
        # across slots and answers a longer prompt with a 400, never a
        # truncation, so this value must follow every reslice (live now:
        # 3 x 200,704 over f16 K + q8_0 V with the MTP drafter armed, ba69ea5).
        contextWindow: 200704
        # Prompt + budget must fit inside the slot window; thinking counts
        # against the budget. Compaction fires at 85 % of the window =
        # 170,598 est; worst-case prompt + budget ≈ 170,598 × 1.03 +
        # 16,384 ≈ 192,100 ≤ 200,704. This endpoint stays the fleet's
        # compaction hub: still the widest window in this file.
        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 — and it comes from ONE place: the tool's `voice_id`
  # argument. No setting feeds it (`speech.voice` is the speak-aloud Kokoro enum,
  # `tts.localVoice` is the local backend's), so a call that omits voice_id arrives
  # at 7772 with no `voice` key and the endpoint answers 400 "unknown voice None;
  # available voices: narrator1, narrator2, narrator3". Say the voice in the call.
  # WIRED 2026-10-18: `config.yml` `modelRoles.speech` is this row, flipped only
  # after the service was deployed and answered real synthesis (two packs,
  # different bytes, 24 kHz PCM16, RTF 0.57). Why the gate existed: `auth: none`
  # makes this provider read as AVAILABLE even while the container is down, and an
  # explicit speech role has NO fallback candidate, so a dead 7772 fails the device
  # loudly instead of quietly narrating in Kokoro. Reverting is ONE key:
  # `modelRoles.speech: local/kokoro`. `speech.voice` stays a Kokoro name either
  # way — it was never this endpoint's voice.
  # 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]