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esh-pfi-infrastructure/stacks/litellm/conf/config.yaml
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vh 1d3b80169a fix(nevermore): repoint onto live aliases — its LLM pass had been dead 8 days 
nevermore pinned LLAMA_SWAP_MODEL=granite-4.1-8b, an alias retired with the
granite seat on 2026-08-12. Every summarization call since then failed: 67
consecutive status=failure rows, 0 tokens, twice daily, entirely silently. The
briefing had been rendering with no LLM pass at all. Nothing alerts on
status=failure in the spend logs, so it took an unrelated question about
reranker VRAM to surface it.

It was also pinned to NEVERMORE_RERANK_MODEL=qwen3-reranker -- the incumbent
Brokkr R43 measured harming 80/90 fleet queries -- and was its ONLY caller,
while the production `reranker` alias sat at 0 calls for 4 days. The R43
cutover repointed the alias but never moved the consumer.

  nevermore/.env  LLAMA_SWAP_MODEL       granite-4.1-8b -> summarizer
                  NEVERMORE_RERANK_MODEL qwen3-reranker -> reranker
                  (server-only; .env is excluded from the mirror both ways)

Verified against nevermore's exact call shape: summarizer returns clean content
with 0 reasoning chars at temperature 0.2 / max_tokens 4000; reranker scores
0.95 on-topic vs ~1e-5 off-topic; embedding returns dim-1024.

Retired alongside it:

  vllm-rerank    :8002  Qwen3-Reranker-0.6B + the qwen3-reranker alias
  vllm-rerank-a4 :8014  gte-reranker-modernbert + its alias
  vllm-granite   :8004  Exited 8 days, dead service block

and vllm-rerank-a3 was promoted from a throwaway `docker run` into this stack
(the selection ledger's own open follow-up). Healthy in 55s. It keeps the
bake-off arm name so the ledger, memory and R43 record stay valid.

VLLM_VERSION is pinned latest -> v0.24.0. Every service in the stack shares that
one variable, so a bare `compose up -d` could have silently upgraded all of
them at once; both tags resolved to the same local image (4091d5593f77), so the
pin changed nothing at runtime.

GPU1 is down to 81,448 of 97,887 MiB -- 13.9 GB reclaimed tonight.

Correction: an earlier claim that A4 had no gateway alias was wrong. It did.
LiteLLM serves both config-defined and DB-defined models -- live showed 32
against config.yaml's 26 -- and grepping the file cannot see the difference.
/v1/models and /model/info (which flags db_model) are the ground truth. DB
models delete hot via POST /model/delete with no restart.

Left alone: reranker-a3-bge-v2-m3, a zero-call duplicate of `reranker` on the
same backend. It is Brokkr's cutover-verification handle -- redundant rather
than broken, and another agent's tooling is not mine to delete unilaterally.
2026-08-20 23:50:53 -07:00

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# LiteLLM gateway config — fronts the vLLM services on ana-ml2
# (10.250.50.54) and logs every request + response so they're
# inspectable in the Logs UI at http://10.250.50.70:4000/ui.
#
# Deploys to /opt/docker/conf/litellm/config.yaml (mounted read-only
# into the container at /app/config.yaml).
#
# Model-name → upstream mapping:
# phi4-mini → vLLM :8004 (generative chat)
# qwen3-embedding → vLLM :8001 (/v1/embeddings)
# reranker → vLLM :8013 (/rerank; bge-v2-m3. The old qwen3-reranker
# alias on :8002 was retired 2026-08-20.)
# * (wildcard) → llama-swap :9292 (the swappable generative zoo)
#
# The wildcard fronts llama-swap so its whole model zoo logs through the
# gateway without per-model registration. The vllm-reward classifier
# (:8003) is a pooling /classify endpoint with no first-class LiteLLM
# route — left direct; see README.
model_list:
# --- Granite 4.1 8B (generative chat) — production summarizer + dreaming
# agent. Replaced phi4-mini 2026-06-05 (beat it on precision in brokkr's
# R15 P03 eval). vLLM on ana-ml2 GPU 1, official FP8, 50K ctx. Explicit
# entry shadows the "*" wildcard's llama-swap route for this name. Full
# prompt + completion captured per call. ---
# --- granite-4.1-8b RETIRED 2026-08-12 (seat downed, GPU1 reclaimed for RP context) ---
# - model_name: granite-4.1-8b
# litellm_params:
# model: hosted_vllm/granite-4.1-8b
# api_base: http://10.250.50.54:8004/v1
# api_key: os.environ/VLLM_API_KEY
# temperature: 0
# model_info:
# mode: chat
# alias: summarizer -> gen (repointed 2026-08-12, granite retired) (operator 2026-06-19). Duplicate-entry alias
# (not router_settings.model_group_alias — that's hidden from /v1/models and can be
# silently ignored in config per litellm #15020/#5524). Keep api_base in sync above.
- model_name: summarizer
litellm_params:
model: hosted_vllm/qwen3.8-27b-uncensored
api_base: http://10.250.50.54:8015/v1
api_key: os.environ/VLLM_API_KEY
temperature: 0
extra_body:
chat_template_kwargs:
enable_thinking: false
model_info:
mode: chat
# alias: classifier -> gen (repointed 2026-08-12, granite retired) (operator 2026-06-19). Light/fast classification
# + triage endpoint; same backend as summarizer. Keep api_base in sync above.
- model_name: classifier
litellm_params:
model: hosted_vllm/qwen3.8-27b-uncensored
api_base: http://10.250.50.54:8015/v1
api_key: os.environ/VLLM_API_KEY
temperature: 0
extra_body:
chat_template_kwargs:
enable_thinking: false
model_info:
mode: chat
# alias: summarizer-large -> gen / qwen3.8-27b-uncensored (operator 2026-07-05). For heavier
# summarization that wants the 35B-A3B heretic `gen` model instead of granite-8b. Thinking OFF
# (matches gen). Keep api_base (:8015) + enable_thinking in sync with the gen record below.
- model_name: summarizer-large
litellm_params:
model: hosted_vllm/qwen3.8-27b-uncensored
api_base: http://10.250.50.54:8015/v1
api_key: os.environ/VLLM_API_KEY
presence_penalty: 1.5
temperature: 0.7
top_p: 0.8
extra_body:
top_k: 20
chat_template_kwargs:
enable_thinking: false
model_info:
mode: chat
# --- image-judge / qwen-image-bench — T2I quality JUDGE aliases. The dedicated
# Qwen-Image-Bench NVFP4 backend (ana-ml2 GPU 1, :8014) was RETIRED 2026-07-15
# (operator: reclaim ~30GB GPU1) after the arbo→gen hero-judge switch. Both
# aliases now REPOINT to the gen backend (:8015, qwen3.8-27b-uncensored,
# vision-intact), held at deterministic judge sampling (temp 0 / top_k 1) with
# enable_thinking:false (a reasoning preamble breaks json_object). Revert =
# `docker compose start` stacks/qwen-image-bench on ana-ml2 + repoint api_base
# back to :8014 + model hosted_vllm/qwen-image-bench. ---
- model_name: qwen-image-bench
litellm_params:
model: hosted_vllm/qwen3.8-27b-uncensored
api_base: http://10.250.50.54:8015/v1
api_key: os.environ/VLLM_API_KEY
temperature: 0
top_p: 1.0
extra_body:
top_k: 1
repetition_penalty: 1.05
chat_template_kwargs:
enable_thinking: false
model_info:
mode: chat
- model_name: image-judge
litellm_params:
model: hosted_vllm/qwen3.8-27b-uncensored
api_base: http://10.250.50.54:8015/v1
api_key: os.environ/VLLM_API_KEY
temperature: 0
top_p: 1.0
extra_body:
top_k: 1
repetition_penalty: 1.05
chat_template_kwargs:
enable_thinking: false
model_info:
mode: chat
# --- Qwen3.6-35B-A3B heretic (llmfan46, uncensored, NVFP4 ModelOpt Experts-Only, VISION-INTACT)
# — the general / `gen` model on ana-ml2 GPU 0, at /tank/aimodels/qwen36-35b-a3b-heretic-nvfp4.
# Displaced AEON-27B 2026-07-08 (which had displaced qwopus3.5-122b 2026-07-05). MoE 35B-A3B
# (256 experts / 8 active), qwen3_5_moe GDN-hybrid, native MTP preserved but served MTP-OFF
# (spec-decode hurts concurrent aggregate). Served on :8015 via vLLM,
# served-name qwen3.8-27b-uncensored. Thinking split = chat_template_kwargs.enable_thinking +
# --reasoning-parser qwen3; tool-calling qwen3_coder. gen / gen-reasoning + summarizer-
# large route here; -reasoning enables thinking. Keep api_base (:8015) in sync.
# RETIRED with the displacement (→ 404, callers migrate to gen): qwen3.5-122-a10b
# [-reasoning] + qwen-large[-reasoning] — they named a 122B that no longer exists;
# aliasing a 27B under those is the naming footgun the qwen36-vl stack warns against.
# presence_penalty: 1.5 — Qwen3.6 README anti-repetition rec for BOTH non-thinking and
# thinking (dvalin-smithy canonical 2026-07-08, validated vs Qwen guidance). gen
# non-thinking temp 0.7/top_p 0.8; gen-reasoning thinking temp 1.0/top_p 0.95 (the
# GENERAL thinking profile, not the 0.6 coding sub-profile). docs/pfi/model-sampler-defaults.md. ---
# CANONICAL Qwen3.8 INSTRUCT (non-thinking) sampling set, verified 2026-08-16
# against BOTH upstreams, which are byte-identical on this:
# Qwen/Qwen3.8-27B card "Best Practices" §1 and unsloth/Qwen3.8-27B §1
# temperature=0.7, top_p=0.80, top_k=20, min_p=0.0,
# presence_penalty=1.5, repetition_penalty=1.0
#
# ⚠️ presence_penalty=1.5 is canonical BUT is the one value upstream itself
# hedges on, verbatim: "you can adjust the presence_penalty parameter between
# 0 and 2 to reduce endless repetition. However, using a higher value may
# occasionally result in LANGUAGE MIXING and a slight decrease in model
# performance." 1.5 sits high in that 0-2 band. If short/degraded replies
# reappear on long multi-turn conversations, THIS is the first dial to move
# (try 0.0-0.5) — operator's own hypothesis 2026-08-16, and upstream's caveat
# supports it. Left at canonical for now so the baseline is defensible rather
# than hand-tuned.
- model_name: gen
litellm_params:
model: hosted_vllm/qwen3.8-27b-uncensored
api_base: http://10.250.50.54:8015/v1
api_key: os.environ/VLLM_API_KEY
presence_penalty: 1.5
temperature: 0.7
top_p: 0.8
extra_body:
top_k: 20
min_p: 0.0
repetition_penalty: 1.0
chat_template_kwargs:
enable_thinking: false
model_info:
mode: chat
- model_name: gen-reasoning
litellm_params:
# Distinct served-name so a thinking-off `gen` request can't mutate this deployment's
# enable_thinking (shared-config-mutation footgun). Same backend :8015, different model id.
model: hosted_vllm/qwen3.8-27b-uncensored-thinking
api_base: http://10.250.50.54:8015/v1
api_key: os.environ/VLLM_API_KEY
# CANONICAL Qwen3.8 THINKING sampling set (Qwen + unsloth "Best Practices"
# §1, identical in both): temperature=1.0, top_p=0.95, top_k=20,
# min_p=0.0, presence_penalty=0.0, repetition_penalty=1.0.
#
# ⚠️ presence_penalty was 1.5 here until 2026-08-16 — the INSTRUCT-mode
# value applied to a THINKING deployment. Canonical for thinking mode is
# 0.0, and upstream warns a high presence_penalty can cause language
# mixing and degrade performance. Corrected to 0.0.
presence_penalty: 0.0
temperature: 1.0
top_p: 0.95
extra_body:
top_k: 20
min_p: 0.0
repetition_penalty: 1.0
chat_template_kwargs:
enable_thinking: true
model_info:
mode: chat
# char-rp -> MeroMero-v2 NON-THINKING prose seat (:8016, vLLM, meromero-charrp stack on
# ana-ml2 GPU 0). G4-MeroMero-v2-31B NVFP4A16, **Gemma-4 base** (google/gemma-4-31B-it),
# 256K ctx, in-house quant. Replaced the GGUF/llama.cpp Magidonia-24B seat 2026-08-12.
#
# THIS SEAT EXISTS BECAUSE THE QWEN BASE THINKS INCESSANTLY. char-rp-reasoning is a
# Qwen3.x derivative and emits ~5-6k chars of CoT per turn no matter which Qwen RP tune
# is loaded — that is the base family, not the finetune, and no swap within it fixes it
# (measured 2026-08-16: Dark-Scarlett 6036 ch vs Fable-Fusion 5323 ch on the same
# prompts). Gemma-4 gives a genuinely non-thinking prose seat. Reach for THIS one when
# you want prose without a reasoning trace; reach for char-rp-reasoning when you want
# the deliberation. Best-of-breed per seat — deliberately NOT the same model.
#
# Serving flags are load-bearing (commit b8f0f4c): `--tool-call-parser gemma4
# --enable-auto-tool-choice --reasoning-parser gemma4` AND
# `--default-chat-template-kwargs '{"enable_thinking": false}'`. That last flag is
# MANDATORY, not decorative — the gemma4 parser defaults enable_thinking to True, which
# pre-initialises the engine to REASONING and returns null `content` for all plain RP
# prose. Before it was set, every tools-bearing request also 400'd (no parser at all).
# Verified 0 chars reasoning / clean prose end-to-end 2026-08-16.
#
# Sampler note: the temp 1.1 / min_p 0.10 / top_k 0 values below were A/B-tuned
# 2026-07-08 against the retired Mistral-family Magidonia seat, NOT against MeroMero.
# They have not been re-tuned for Gemma-4 — treat as inherited, not canonical.
# Callers may override. docs/pfi/model-sampler-defaults.md; stacks/meromero-charrp/.
- model_name: char-rp
litellm_params:
model: hosted_vllm/char-rp
api_base: http://10.250.50.54:8016/v1
api_key: os.environ/VLLM_API_KEY
temperature: 1.1
top_p: 0.95
extra_body:
min_p: 0.10
top_k: 0
model_info:
mode: chat
# char-rp-reasoning -> GGUF managed-REASONING seat (:8018, llama.cpp, char-rp-gguf stack).
# Qwen3.5-27B-Deckard-PKD-Heretic-Uncensored-Thinking i1-Q5_K_M — DavidAU creative tune.
# Reasoning ON server-side (--reasoning on): CoT surfaces in reasoning_content, content stays
# clean prose, budget-capped. DRY server-side (sampler order = dry after temperature) tames looping.
# A/B WINNER 2026-07-08: 0/30 loops + 0/30 refusals; beat RpR-v4 (1/30 loop, forbids DRY),
# Pantheon-27B (7/30 explicit refusals), Snowdrop + Gembrain (llama.cpp template-incompat).
# Deckard decode: temp 1.0, top_p 0.95, top_k 40, min_p 0.05 (dvalin-CONFIRMED canonical 2026-07-08;
# NO presence/rep penalty; DRY 0.8 server-side). Tuning ladder: flat prose→min_p 0.08, loops→DRY 0.9,
# over-damped→DRY 0.6/off. Do NOT import RpR/QwQ sampler rules (different family). NOT the same model as
# char-rp (best-of-breed per seat) — see stacks/char-rp-gguf/README.md.
# ⚠️ TEMPORARY REPOINT 2026-08-16 (operator-directed evaluation window).
# char-rp-reasoning currently resolves to FABLE-FUSION 711 on :8019, NOT to
# Dark-Scarlett. DS v1.0 is DOWN — GPU1 is zero-sum and Fable-Fusion occupies
# her slot. This is a deliberate, explicit substitution for hands-on testing;
# it is NOT a silent alias swap, and it is not the permanent seat decision.
# Address the seat unambiguously as `char-rp-fable` below; `char-rp-reasoning`
# is kept live only so existing consumers keep working during the window.
#
# WHY: DS v1.0 is a plain finetune of stock Qwen3.6-27B with NO abliteration,
# so cold prompts revert to safety-tuned base behaviour. Measured three-arm
# A/B (services/refusal-probe/): under a bare instruction with no character
# card, DS refuses 92.5% (37/40) and Fable-Fusion 15.8% (6/38); with a
# character card both sit at ~0%. Fable-Fusion is Heretic-abliterated.
#
# ROLLBACK (restores Dark-Scarlett):
# ssh infra-ops@10.250.50.54 'cd /opt/docker/compose/fablefusion-charrp-probe && sudo docker compose down'
# ssh infra-ops@10.250.50.54 'cd /opt/docker/compose/darkscarlett-charrp-reasoning && sudo docker compose up -d'
# then revert this block to api_base :8018 / model hosted_vllm/char-rp-reasoning
# and restart litellm (~52s).
#
# Samplers below are the model card's thinking-mode recommendation (temp 1.0 /
# top_p 0.95 / top_k 20) and are unchanged from the DS entry. Verified the FF
# chat template honours `enable_thinking` (chat_template.jinja:44) rather than
# ignoring it — the mismatch that returned null content on the MeroMero seat.
- model_name: char-rp-reasoning
litellm_params:
model: hosted_vllm/char-rp-probe
api_base: http://10.250.50.54:8019/v1
api_key: os.environ/VLLM_API_KEY
temperature: 1.0
top_p: 0.95
extra_body:
top_k: 20
chat_template_kwargs:
enable_thinking: true
model_info:
mode: chat
# char-rp-fable -> the SAME Fable-Fusion seat under its own honest name, so the
# evaluation can address it without relying on the temporary repoint above.
# Distinct model_name = distinct litellm_params object, which avoids the
# shared-deployment param mutation that bleeds sampler overrides between
# variants.
- model_name: char-rp-fable
litellm_params:
model: hosted_vllm/char-rp-probe
api_base: http://10.250.50.54:8019/v1
api_key: os.environ/VLLM_API_KEY
temperature: 1.0
top_p: 0.95
extra_body:
top_k: 20
chat_template_kwargs:
enable_thinking: true
model_info:
mode: chat
# --- Selene 1 Mini 8B (AtlaAI judge, FP8) — restored on GPU1 after the
# llama-swap teardown (was the Q6_K GGUF in the swap zoo). vLLM dynamic fp8,
# :8011. Explicit entry shadows the "*" wildcard (which used to reach it via
# llama-swap). Hallucination/RAG-faithfulness judge; callers set temp ~0.01. ---
- model_name: selene-1-mini-8b
litellm_params:
model: hosted_vllm/selene-1-mini-8b
api_base: http://10.250.50.54:8011/v1
api_key: os.environ/VLLM_API_KEY
temperature: 0.6
top_p: 0.9
model_info:
mode: chat
# --- Qwen3 embeddings ---
- model_name: qwen3-embedding
litellm_params:
model: hosted_vllm/Qwen/Qwen3-Embedding-0.6B
api_base: http://10.250.50.54:8001/v1
api_key: os.environ/VLLM_API_KEY
model_info:
mode: embedding
# --- qwen3-reranker RETIRED 2026-08-20. It named Qwen3-Reranker-0.6B on :8002,
# the incumbent the R43 bake-off replaced on 2026-08-06 after measuring it
# HARMING 80/90 fleet queries (no-reranker beat it 89/90 vs 56/90). The alias
# was kept as the rollback path and, for 13 days, was the ONLY reranker
# actually receiving traffic: nevermore was pinned to it by name, so the
# cutover moved `reranker` but never moved nevermore. Fixed at the consumer
# (nevermore now pins `reranker`), then the seat and this alias were retired.
# Use `reranker` -> bge-reranker-v2-m3 :8013. ---
# --- Worldtree capability aliases (role→capability gateway swaps, ADR-0012).
# Stable role-named aliases so consumers bind the CAPABILITY, not a concrete
# model; swap the backing model here and callers are unaffected. NO generic
# `embedding` alias ON PURPOSE — embedding vectors are model-specific (not
# swap-transparent), so that capability stays `qwen3-embedding` above. ---
# chat-judge → generative LLM-as-judge (Selene); WT selene-judgment role.
- model_name: chat-judge
litellm_params:
model: hosted_vllm/selene-1-mini-8b
api_base: http://10.250.50.54:8011/v1
api_key: os.environ/VLLM_API_KEY
temperature: 0.6
top_p: 0.9
model_info:
mode: chat
# reranker → generic capability name for rerank. THE reranker alias — the only
# one left as of 2026-08-20. Every consumer pins this name, never a model name.
- model_name: reranker
litellm_params:
model: hosted_vllm/BAAI/bge-reranker-v2-m3
api_base: http://10.250.50.54:8013/v1
api_key: os.environ/VLLM_API_KEY
model_info:
mode: rerank
# --- coder-fast → Qwen2.5-Coder-1.5B (BASE), FIM code-completion seat (ana-ml2
# GPU1 :8020, vLLM; deep-research pick 2026-07-27). For Zed editor inline
# edit-predictions via the LEGACY /v1/completions endpoint with Qwen FIM
# markers (<|fim_prefix|>/<|fim_suffix|>/<|fim_middle|>). BASE not -Instruct
# (FIM is a pretraining objective; base completions are cleaner). Apache-2.0.
# mode: completion — this is text-completion, not chat. Reached KEYLESS from
# Vuong's Mac via the zed-fim-proxy (separate port on ana-docker) which injects
# a coder-fast-scoped virtual key; the proxy's model-allowlist + the scoped key
# bound the blast radius. Runner-up was Qwen2.5-Coder-3B (higher HumanEval-FIM,
# non-commercial Qwen-Research license). ---
- model_name: coder-fast
litellm_params:
model: hosted_vllm/qwen2.5-coder-1.5b
api_base: http://10.250.50.54:8020/v1
api_key: os.environ/VLLM_API_KEY
model_info:
mode: completion
# --- z.ai GLM (cloud API) — fronted for unified logging across local
# + cloud inference. Explicit entries, so they win over the "*"
# wildcard below (no collision with llama-swap's glm4.7-flash etc.
# — different model IDs). NOTE: paid API; only gateway-keyed callers
# can reach these, but they DO spend z.ai credits. Key in .env. ---
# glm-5.1: thinking DISABLED by default (2026-06-11, operator call). LiteLLM
# strips a top-level `thinking` param (drop_params), but forwards `extra_body`
# verbatim to z.ai, where the native thinking:{type:disabled} control lands —
# verified reasoning_tokens→0. Reasoning is opt-in via glm-5.1-reasoning below.
- model_name: glm-5.1
litellm_params:
model: openai/glm-5.1
api_base: https://api.z.ai/api/coding/paas/v4
api_key: os.environ/Z_AI_API_KEY
temperature: 1.0
top_p: 0.95
extra_body:
thinking:
type: disabled
# glm-5.1-reasoning: identical upstream, thinking ENABLED (opt-in reasoning).
- model_name: glm-5.1-reasoning
litellm_params:
model: openai/glm-5.1
api_base: https://api.z.ai/api/coding/paas/v4
api_key: os.environ/Z_AI_API_KEY
temperature: 1.0
top_p: 0.95
extra_body:
thinking:
type: enabled
# glm-5.2 (released ~2026-06; canonical z.ai id `glm-5.2`, confirmed via /models +
# a live completion with our key). Mirrors the glm-5.1 pattern: thinking DISABLED
# by default (consistency with the 2026-06-11 operator call), opt-in reasoning via
# glm-5.2-reasoning. extra_body.thinking is forwarded verbatim to z.ai.
# CANONICAL LIMITS (probed live vs z.ai 2026-07-05): 1,048,576-token (1M, 2^20)
# INPUT context; 131,072 (128K) MAX OUTPUT (z.ai max_tokens range [1,131072]).
# NO gateway-side cap — pure z.ai passthrough, so these are the effective limits.
- model_name: glm-5.2
litellm_params:
model: openai/glm-5.2
api_base: https://api.z.ai/api/coding/paas/v4
api_key: os.environ/Z_AI_API_KEY
temperature: 1.0
top_p: 0.95
extra_body:
thinking:
type: disabled
- model_name: glm-5.2-reasoning
litellm_params:
model: openai/glm-5.2
api_base: https://api.z.ai/api/coding/paas/v4
api_key: os.environ/Z_AI_API_KEY
temperature: 1.0
top_p: 0.95
extra_body:
thinking:
type: enabled
# gen-frontier / gen-frontier-reasoning: capability aliases for the PAID
# frontier tier (GLM 5.2 @ z.ai), mirroring glm-5.2 / glm-5.2-reasoning
# (thinking off / on). Worldtree binds these for frontier-grade generation
# / reasoning; swap the backing frontier model here, callers unaffected.
# PAID — only all-proxy-models / explicitly-scoped keys reach them; the free
# all-agents-local key is fenced off z.ai spend and cannot.
- model_name: gen-frontier
litellm_params:
model: openai/glm-5.2
api_base: https://api.z.ai/api/coding/paas/v4
api_key: os.environ/Z_AI_API_KEY
temperature: 1.0
top_p: 0.95
extra_body:
thinking:
type: disabled
- model_name: gen-frontier-reasoning
litellm_params:
model: openai/glm-5.2
api_base: https://api.z.ai/api/coding/paas/v4
api_key: os.environ/Z_AI_API_KEY
temperature: 1.0
top_p: 0.95
extra_body:
thinking:
type: enabled
- model_name: glm-5-turbo
litellm_params:
model: openai/glm-5-turbo
api_base: https://api.z.ai/api/coding/paas/v4
api_key: os.environ/Z_AI_API_KEY
temperature: 1.0
top_p: 0.95
- model_name: glm-4.7
litellm_params:
model: openai/glm-4.7
api_base: https://api.z.ai/api/coding/paas/v4
api_key: os.environ/Z_AI_API_KEY
temperature: 1.0
top_p: 0.95
- model_name: glm-4.5-air
litellm_params:
model: openai/glm-4.5-air
api_base: https://api.z.ai/api/coding/paas/v4
api_key: os.environ/Z_AI_API_KEY
temperature: 0.6
top_p: 0.95
# --- Kimi K3 — CODING endpoint (Kimi Code / Vivace membership). THE PRIMARY
# Kimi arm the Heid cross-frontier panel plan uses. OpenAI-compatible base
# https://api.kimi.com/coding/v1 → openai/ provider, upstream model id `k3`
# (1M-context; the coding lineup also carries k3-256k, kimi-for-coding,
# kimi-for-coding-highspeed — ids confirmed live via /models 2026-07-25).
# PAID (Vivace subscription); key KIMI_CODE_API_KEY in .env. CONSTRAINT
# (verified live 2026-07-25): k3 accepts ONLY temperature=1 — any other value
# 400s ("only 1 is allowed for this model") — so it is pinned here; callers
# must NOT override it. k3 is also a REASONING model (thinking-effort tiers
# low/high/max per Kimi Code docs): CoT returns in `reasoning_content`, the
# answer in `content` — give it adequate max_tokens or content returns EMPTY
# (reasoning eats a tiny budget). ---
- model_name: kimi-k3
litellm_params:
model: openai/k3
api_base: https://api.kimi.com/coding/v1
api_key: os.environ/KIMI_CODE_API_KEY
temperature: 1
model_info:
mode: chat
# --- Kimi K3 — GENERAL Moonshot API endpoint (https://api.moonshot.ai/v1),
# kept as the `-gen-api` variant. The plan uses the CODING endpoint above;
# this is the general-platform route (originally wired then demoted when the
# coding endpoint became canonical). OpenAI-compatible, upstream `kimi-k3`,
# key MOONSHOT_API_KEY. Same temperature=1 + reasoning-model constraints as
# the coding k3 (verified live through the gateway 2026-07-25, 17+25→"42"). ---
- model_name: kimi-k3-gen-api
litellm_params:
model: openai/kimi-k3
api_base: https://api.moonshot.ai/v1
api_key: os.environ/MOONSHOT_API_KEY
temperature: 1
model_info:
mode: chat
# --- (removed 2026-06-20, operator call) the `*` wildcard → llama-swap
# (ana-ml2:9292). llama-swap is decommissioned (:9292 confirmed down), so
# the wildcard routed every unmatched / typo'd / stale model name to a DEAD
# backend → a misleading "Connection error" instead of a clean "model not
# found". This is the footgun that silently swallowed Worldtree's defunct
# model names. Removed so unknown models now fail loudly (404). Re-add an
# explicit per-model entry if a swappable zoo ever returns. ---
# --- lfm2.5-2.6b -> RETIRED PERMANENTLY 2026-08-20 (operator directive). The
# LiquidAI LFM2.5-2.6B seat (ana-ml2 GPU1 :8021) was an EVAL-ONLY bake-off
# against granite-4.1-8b that never got its operator ruling; its comparator
# was retired 2026-08-15 and spend logs showed 0 calls in the 4 days to
# 2026-08-21. Container removed, service deleted from stacks/vllm. The alias
# is deleted rather than repointed so the name 404s cleanly. ---
general_settings:
master_key: os.environ/LITELLM_MASTER_KEY
database_url: os.environ/DATABASE_URL
store_model_in_db: true
# THE log switch: persists full request messages + response bodies into
# SpendLogs so they render in the Logs UI. Without this you get metadata
# (tokens, latency, model) but not the prompt/completion text.
#
# ⚠️ TURNED OFF 2026-08-16 (operator: "I don't need any of that information").
# With this TRUE the SpendLogs table stored every prompt+completion body and
# grew to 6.0 GB (of a 6.08 GB DB). Off = lightweight cost/usage rows only
# (tokens, latency, model, cost) — the cross-project spend tracking survives,
# the bulky bodies do not. Re-enable ONLY for a bounded debugging window, not
# standing.
store_prompts_in_spend_logs: false
# HARD CAP on SpendLogs growth (operator: "if there's a way to cap it, CAP
# it"). The retention job deletes rows older than the period on the interval
# cadence, so the table is bounded by ~7 days of lightweight rows rather than
# unbounded. Names verified against LiteLLM docs (proxy/spend_logs_deletion).
maximum_spend_logs_retention_period: "7d"
maximum_spend_logs_retention_interval: "1d"
# scalar-judge → Skywork-Reward-V2 (scalar reward model; vLLM pooling on
# ana-ml2:8003). LiteLLM has no reward/pooling MODE, so this is a passthrough,
# not a model_list alias. Gateway-key-gated. Consumers POST the reward body to
# /scalar-judge/<route> (e.g. /pooling or /classify), forwarded to :8003.
# SWAP-SENSITIVE: a different reward model shifts the score scale, so consumers
# must recalibrate thresholds after a backing swap.
pass_through_endpoints:
- path: "/scalar-judge"
target: "http://10.250.50.54:8003"
forward_headers: true
include_subpath: true
litellm_settings:
# vLLM rejects some OpenAI params other backends accept; drop silently
# rather than 400 the caller.
drop_params: true
# Custom pre-call hook: strip an empty `tools: []` (+ orphaned tool_choice)
# before forwarding upstream. vLLM 400s on empty tools arrays ("tools must
# not be an empty array"); drop_params doesn't catch empty VALUES, only
# unsupported params. Runs on every request → fixes it for all vLLM models.
# File mounted at /app/strip_empty_tools.py; reference is module.instance,
# resolved relative to this config's directory.
callbacks: ["strip_empty_tools.strip_empty_tools_instance"]
# Langfuse trace export RETIRED 2026-06-20 (operator). Its ClickHouse member spewed
# ~94 GB of unrotated logs and filled ana-docker's disk; the trace UI was redundant
# with LiteLLM's native spend_logs (store_prompts_in_spend_logs: true → full
# prompts/responses/tokens/cost at :4000/ui). Gateway observability stays fully
# native. Re-add success_callback/failure_callback here if Langfuse ever returns.
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