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cortex/models.example.toml
rob thijssen 24e20dcb5c
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feat(catalogue,gateway): model aliases (helexa/small, helexa/balanced, helexa/large)
Operators can now define tier aliases in models.toml:

  [aliases]
  "helexa/small" = "Qwen/Qwen3-1.7B"
  "helexa/balanced" = "Qwen/Qwen3-8B"
  "helexa/large" = "Qwen/Qwen3.6-27B"

A client request for `model: "helexa/small"` is resolved to the concrete
model id at routing time. The gateway also rewrites the proxied body's
`model` field to the concrete id so neuron sees a name that matches its
loaded handle (otherwise the harness rejects the request).

Motivated by the finger-in-the-wind benchmark: same "what's the capital
of Georgia" probe runs in 2.5s on the 1.7B vs 6.7s on the 27B with
identical correctness. Aliases let clients pick a latency tier without
hardcoding model ids, and let operators swap targets without changing
client code.

Changes:
  * cortex-core: `ModelCatalogue` gains `aliases: HashMap<String, String>`
    + `resolve_alias(&str) -> &str`. Unit tests cover the basic
    resolution + TOML round-trip.
  * cortex-gateway:
    * `RouteDecision` gains `resolved_model_id: String`. `router::resolve`
      consumes aliases at entry and threads the concrete id through.
    * Handlers (chat_completions, completions, anthropic_messages
      streaming + non-streaming) rewrite the body's `model` field with
      `rewrite_model_in_body` before proxying, using the resolved id
      for metrics labels, LRU touch, and the body itself.
    * `/v1/models` (Pass 4) emits each alias as its own entry mirroring
      the target's `loaded` flag, feasible_on, and locations — clients
      browsing the endpoint see both names and can pick either.
  * `models.toml` declares the three tier aliases; `models.example.toml`
    documents the section as opt-in.
  * Integration tests verify: end-to-end alias→concrete request flow,
    alias surfacing in /v1/models, and no-op fall-through for
    non-alias model ids.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-26 16:10:41 +03:00

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# models.example.toml — model catalogue
#
# Copy to /etc/cortex/models.toml and adjust for your environment.
# Describes how to serve each model. Cortex matches these profiles
# against discovered neuron topologies for placement decisions; the
# resulting `(catalogue × topology)` set is what `GET /v1/models`
# returns and what the router can cold-load on demand.
#
# Field reference:
# id - HuggingFace model id, exact match.
# harness - which engine handles inference (currently "candle").
# quant - GGUF quantisation tag for the file in the HF repo
# (e.g. "Q4_K_M"). Omit/empty for the dense
# safetensors path. TP requires dense.
# vram_mb - rough estimate; advisory only, not enforced.
# min_devices - GPU count this profile needs. TP profiles use
# the same value as the tensor-parallel size.
# min_device_vram_mb - each device must meet this VRAM floor for the
# neuron to be considered "feasible".
# pinned_on - optional whitelist of neuron names. Non-empty
# narrows feasibility to just those neurons and
# protects the model from LRU eviction there.
# Tensor-parallel target — needs a neuron with at least 2 large GPUs.
# The example pins to a specific neuron name; adjust or remove the
# pinned_on entry for your own fleet.
[[models]]
id = "Qwen/Qwen3.6-27B"
harness = "candle"
vram_mb = 54000
min_devices = 2
min_device_vram_mb = 24000
pinned_on = ["your-multi-gpu-neuron"]
# Mid-size dense model — fits on any single GPU with ≥16 GB VRAM.
[[models]]
id = "Qwen/Qwen3-8B"
harness = "candle"
vram_mb = 18000
min_devices = 1
min_device_vram_mb = 16000
# Small GGUF quantised — runs on any small GPU.
[[models]]
id = "unsloth/Qwen3-0.6B-GGUF"
harness = "candle"
quant = "Q4_K_M"
vram_mb = 500
min_devices = 1
min_device_vram_mb = 4000
# -- Tier aliases ------------------------------------------------------------
# Optional. Clients can request inference against an alias (e.g.
# `model: "helexa/small"` in /v1/chat/completions) and cortex
# transparently routes to the concrete model id below — including
# rewriting the body's model field so neuron sees a name that matches
# its loaded handle. Both the alias and the target appear in
# /v1/models so clients can discover either. Operators can swap
# targets here without changing client code.
#
# [aliases]
# "helexa/small" = "Qwen/Qwen3-1.7B"
# "helexa/balanced" = "Qwen/Qwen3-8B"
# "helexa/large" = "Qwen/Qwen3.6-27B"