# LiteLLM model routing. model_name is what clients request; the
# litellm_params block is how LiteLLM reaches the backend.
#
# `model: openai/<served-name>` tells LiteLLM to use its
# openai-compatible adapter and forward <served-name> to the backend.
# api_base is the backend's /v1 root reachable from inside the LiteLLM
# container (host.docker.internal = host's docker0 IP via the
# extra_hosts entry in litellm.yml).
#
# Backend running-state matters: requests to a stopped backend return
# 503/connection-refused. By design — no fallback chain, since these
# backends compete for GPU and silently routing "qwen3-235b" to the 30B
# would be more confusing than failing fast.
#
# Edits here require `./run.sh` on the Mac to push to the box, then
# `docker compose restart litellm` on the box to reload.

model_list:
  # Daily-driver coding model. Ollama with gfx1100-coerced ROCm —
  # currently the default opencode provider. Always-resident
  # (OLLAMA_KEEP_ALIVE=24h).
  - model_name: qwen3-coder
    litellm_params:
      model: openai/qwen3-coder:30b
      api_base: http://host.docker.internal:11434/v1
      api_key: dummy

  # Same weights as qwen3-coder above but served via llama.cpp on the
  # kyuz0 rocm-7.2.2 image (native gfx1151 + rocWMMA). LL-P0 measures
  # whether the eval_tps win justifies switching default opencode to
  # this. Manual start until then.
  - model_name: qwen3-coder-llama
    litellm_params:
      model: openai/qwen3-coder
      api_base: http://host.docker.internal:8080/v1
      api_key: dummy

  # Long-context chat (no tool calling) via vLLM. P0 verified at 32K;
  # context ramp tracked in kimi-linear/NEXT_STEPS.md.
  - model_name: kimi-linear
    litellm_params:
      model: openai/kimi-linear
      api_base: http://host.docker.internal:8000/v1
      api_key: dummy

  # Long-task model — Qwen3-235B-A22B-Instruct-2507 UD-Q2_K_XL via
  # llama.cpp on port 8081. ~5-10 tok/s decode; manual start only
  # (can't coexist with the other GPU services). Requests will fail
  # with connection refused when the container is down — that's the
  # intended UX: a stopped service is a clear signal.
  - model_name: qwen3-235b
    litellm_params:
      model: openai/qwen3-235b
      api_base: http://host.docker.internal:8081/v1
      api_key: dummy

litellm_settings:
  # Forward all client params to the backend, even unrecognized ones.
  # Default drops unknown OpenAI params — fine for hosted models, but
  # our backends vary (vLLM, llama.cpp, Ollama) and each accepts a
  # slightly different superset. Let the backend reject what it can't
  # use rather than LiteLLM silently filtering.
  drop_params: false
  # No /v1/models caching — the list is short and we want stop/start
  # of backends to reflect immediately.
  cache: false
  # Log proxy requests at info; tail with `docker compose logs litellm`.
  set_verbose: false

general_settings:
  # Disable LiteLLM's database mode — we're stateless. No user/key/spend
  # tracking needed for a single-user trusted LAN setup.
  database_url: null