# 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-primary on 2026-10-08 (official
        # unit-name scheme; the systemd service, scripts and runbooks keep
        # the frank-primary 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-primary.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-primary-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-primary-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-large on 2026-10-08 (official
        # unit-name scheme; the service/unit/scripts keep the frank-large
        # 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 ABLITERATED Swift1.5 finetune of peonist's
        # Qwen3.8-Flash-Next v2 checkpoint (qwen38-flash-next-v2-swift15-
        # abliterated.hgn, ~62 GiB resident, + REQUIRED n-gram table +
        # vision tower) on the Strix Halo iGPU: TWO slots at
        # HALOGEN_CTX=262144 each with the MTP head ALWAYS ON
        # (HALOGEN_SPEC_ADAPT=0 — the default adaptive policy halves
        # decode). Card: 53-57 t/s greedy, ~1,640 t/s prefill at 34k, ~72%
        # fewer thinking tokens than the censored 27B. ABLITERATED base
        # (refusals should not return) and VISION ON (OpenAI image_url
        # parts; >=256 px images or it answers "video"). Runbook:
        # serverAppConfigs-docs/LLM/frank-large-flash-server-v2-swift15.md.
        # Per-SLOT window the harness may fill — deliberately BELOW the
        # engine ceiling (HALOGEN_CTX=262144): the clamp below leaves a
        # 45k gap that absorbs the estimate error.
        contextWindow: 217088
        # 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 = ctx − 4096 + 3 % est
        # error ≈ 219k, inside the 262,144 engine ceiling with ~43k
        # margin (robust to a 10 % under-count). Compaction fires at 85 %
        # of the window = 184,524 est ≈ 190k measured (Frank's 180k
        # target) and rides on frank-zealot: input ≈ threshold −
        # keepRecent(20k) ≈ 170k, inside its 200,704 slot.
        # HALOGEN_MAX_TOKENS_CAP in frank-large.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-secondary 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-large.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-secondary on 2026-10-08 (official
        # unit-name scheme; the service/unit/scripts keep the
        # frank-secondary 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 (master, CUDA sm_86 build) serving
        # HauhauCS/Qwen3.5-9B-Uncensored-HauhauCS-Aggressive at Q6_K
        # (7,359,259,008 B) — qwen35 dense hybrid (32 layers: 8 full-attention
        # + 24 Gated DeltaNet, 4 KV heads x 256 dim, native 262,144), TWO
        # conversations (--ctx-size 401408 / --parallel 2) at Frank's 200k cap
        # each. Exact KV: f16 K + f16 V. Vision via the publisher's OWN
        # BF16 mmproj (921,704,448 B) — the model is natively multimodal.
        # **MTP IS OFF** because the file carries no nextn/mtp tensors —
        # SPEC_TYPE=none strips both spec flags. Priced from the MiMo Q6_K
        # boots at this geometry (21,276/2,849 at ubatch 1024; 21,846/2,279 at
        # 2048). 2026-10-07 (late) swap from Gemma4-26B-A4B Q4_K_M (1x200,704).
        # Runbook:
        # serverAppConfigs-docs/LLM/frank-secondary-llama-qwen35-9b-q6k-2x200704.md.
        # contextWindow is the PER-SLOT window (Frank's cap): a longer
        # prompt needs compaction.
        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 — a longer prompt is a 400 naming
        # the limit, never a truncation.
        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"
          # The Qwen3.5 chat template has NO reasoning_effort variable (unlike
          # the Qwen3.8-template endpoints, where an unmapped level is a hard
          # Jinja raise). llama.cpp passes chat_template_kwargs through to
          # Jinja, and Jinja ignores variables a template never reads — so the
          # map is inert here, kept so role-level overrides resolve identically
          # across the fleet.
          reasoningEffortMap:
            minimal: low
            low: low
            medium: medium
            high: xhigh
            max: xhigh
          # The Qwen3.5 publisher recipe (model card, thinking mode):
          # temperature 0.6 / top_p 0.95 / top_k 20 / min_p 0. Non-thinking
          # mode is 0.7/0.8/20/0 — the harness runs thinking mode.
          # extraBody is merged last and wins over the harness default.
          extraBody:
            temperature: 0.6
            top_p: 0.95
            top_k: 20
            min_p: 0.0
            presence_penalty: 0.0
            chat_template_kwargs:
              enable_thinking: true
            # (no preserve_thinking: that kwarg belongs to the Qwen3.8
            # templates; nothing in this template reads it)