rob thijssen
8a636c687f
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Resolves #62. opencode's helexa provider discovers a model's serving budget from /v1/models and uses it to size context, trigger compaction, and show spend with no hand-configuration. Each model entry now carries: - limit { context, input?, output } — operator-declared in models.toml - cost { input, output, cache_read?, cache_write? } — USD per 1M tokens - tool_call / reasoning — runtime-detected by the candle harness and OR-ed in from each serving neuron Composition: the catalogue profile supplies limit/cost (Pass 1); the poller carries the neuron's detected tool_call/reasoning into ModelEntry, which the gateway unions onto the entry (Pass 2); aliases propagate every field (Pass 4). Wire types extend ModelInfo / ModelProfile / CortexModelEntry additively (serde default + skip_serializing_if), so older neurons and clients are unaffected. helexa-bench's ModelInfo constructor and the gateway test fixtures are updated for the new fields. Adds tests/model_limits.rs asserting /v1/models surfaces limit + cost (catalogue) and tool_call + reasoning (runtime), and that max_model_len is gone. Removes max_model_len. It was write-only with no consumer — opencode's source references it nowhere and it is not an OpenAI /v1/models field — and doubly misleading: vLLM's max_model_len means total sequence length, but cortex populated it from NEURON_MAX_PROMPT_TOKENS, a prompt-only cap. The limit{} contract replaces it. The neuron's max_prompt_tokens remains the enforced prompt cap (neuron-side); cortex just stops re-advertising a derived, mis-named copy. Closes #66 — its stale-max_model_len premise is moot once the field is gone. limit/cost are operator-declared (catalogue) per #62's design; auto- deriving the advertised budget from each neuron's reported cap is a tracked follow-up. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
98 lines
3.8 KiB
TOML
98 lines
3.8 KiB
TOML
# models.example.toml — model catalogue
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#
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# Copy to /etc/cortex/models.toml and adjust for your environment.
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# Describes how to serve each model. Cortex matches these profiles
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# against discovered neuron topologies for placement decisions; the
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# resulting `(catalogue × topology)` set is what `GET /v1/models`
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# returns and what the router can cold-load on demand.
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#
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# Field reference:
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# id - Repo id in the source registry (e.g. "Qwen/Qwen3.6-27B").
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# Exact match.
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# harness - which engine handles inference (currently "candle").
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# quant - GGUF quantisation tag for the file in the HF repo
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# (e.g. "Q4_K_M"). Omit/empty for the dense
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# safetensors path. TP requires dense.
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# vram_mb - rough estimate; advisory only, not enforced.
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# min_devices - GPU count this profile needs. TP profiles use
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# the same value as the tensor-parallel size.
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# min_device_vram_mb - each device must meet this VRAM floor for the
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# neuron to be considered "feasible".
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# pinned_on - optional whitelist of neuron names. Non-empty
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# narrows feasibility to just those neurons and
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# protects the model from LRU eviction there.
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# source - optional source scheme ("huggingface", "helexa",
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# operator mirror tag). When set, cortex forwards
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# the load to neuron as `scheme:id` so the daemon
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# fetches from the right registry. Omit to let
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# neuron substitute its own `default_source`.
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# Tensor-parallel target — needs a neuron with at least 2 large GPUs.
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# The example pins to a specific neuron name; adjust or remove the
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# pinned_on entry for your own fleet.
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[[models]]
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id = "Qwen/Qwen3.6-27B"
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harness = "candle"
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vram_mb = 54000
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min_devices = 2
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min_device_vram_mb = 24000
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pinned_on = ["your-multi-gpu-neuron"]
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# Token budget: context wall, compaction trigger (input headroom), max output.
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limit.context = 32768
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limit.input = 28672
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limit.output = 4096
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# Pricing in USD per 1M tokens — 0.0 for self-hosted.
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cost.input = 0.0
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cost.output = 0.0
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# Static capability hints (unioned with runtime-detected flags).
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capabilities = ["text", "reasoning"]
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# Mid-size dense model — fits on any single GPU with ≥16 GB VRAM.
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[[models]]
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id = "Qwen/Qwen3-8B"
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harness = "candle"
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vram_mb = 18000
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min_devices = 1
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min_device_vram_mb = 16000
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limit.context = 16384
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limit.output = 4096
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# Small GGUF quantised — runs on any small GPU.
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[[models]]
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id = "unsloth/Qwen3-0.6B-GGUF"
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harness = "candle"
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quant = "Q4_K_M"
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vram_mb = 500
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min_devices = 1
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min_device_vram_mb = 4000
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limit.context = 8192
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limit.output = 2048
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# Helexa registry model — `source` pins this entry to the helexa
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# scheme so cortex forwards `helexa:Helexa/Qwen3.6-27B-Uncensored` to
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# neuron's /models/load. Requires the neuron config to declare a
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# matching [harness.candle.sources.helexa] entry pointing at the
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# helexa registry endpoint (see neuron.example.toml).
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#
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# [[models]]
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# id = "Helexa/Qwen3.6-27B-Uncensored"
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# harness = "candle"
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# source = "helexa"
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# vram_mb = 54000
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# min_devices = 2
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# min_device_vram_mb = 24000
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# -- Tier aliases ------------------------------------------------------------
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# Optional. Clients can request inference against an alias (e.g.
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# `model: "helexa/small"` in /v1/chat/completions) and cortex
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# transparently routes to the concrete model id below — including
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# rewriting the body's model field so neuron sees a name that matches
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# its loaded handle. Both the alias and the target appear in
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# /v1/models so clients can discover either. Operators can swap
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# targets here without changing client code.
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#
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# [aliases]
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# "helexa/small" = "Qwen/Qwen3-1.7B"
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# "helexa/balanced" = "Qwen/Qwen3-8B"
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# "helexa/large" = "Qwen/Qwen3.6-27B"
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