CHANGELOG.md

Changelog

All notable changes to this project are documented here. The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.

[0.40.0] - 2026-07-09

Added

• scripts/live-check.sh + tests/test_live_capabilities.py — a LOCAL, single-trigger, unattended pre-PR gate that dials every advertised role endpoint+path, every id in /v1/models, checks CLI/gateway agreement, reproduces Colleague's role-discovery path, and fails on deployed-gateway version skew. It FAILS rather than skips when armed. A 429 (pressure shed) and a 503 carrying Retry-After count as reachable; only a 404, a connection failure, a Retry-After-less 503, or a bare 5xx are faults.
• lobes/gateway/_readiness.py — a bounded background probe of each backend's /health, mirroring PressureCache. Tri-state (True healthy / False reached-but-unhealthy / None unreachable), daemon thread, socket-free .current(), and a probe that degrades to None on OSError, http.client.HTTPException and ValueError.
• GET /health now reports {"version": ...}, and lobes doctor gains a gateway_version_match check that fails on skew between the deployed gateway and the CLI wheel (issue #99).
• lobes/gateway/_routing.py::is_unknown_model — a pure predicate separating "unknown model id" from "unspecified model".
• Ground-truth perception probes for the senses role: a stdlib-generated solid-colour PNG whose colour the model must name, and a Chatterbox-synthesized word the model must transcribe. Both carry negative controls; the old placeholder-media tests are relabelled as wire checks.

Changed

• No cross-backend failover. order_backends now returns at most one backend. A request naming the cortex model can never be answered by the Gemma backend, which protects the final_authority role contract from #81.
• GET /v1/models and GET /capabilities.ready are backed by the live readiness cache rather than by configuration. A wired-but-dead backend is no longer advertised.
• RoleInfo.ready is no longer an alias of loaded for cortex/senses/embedder/reranker. build_role_registry self-enforces the invariant: a supplied backend_ready map is authoritative, and a present None, a present False, and a missing key all mean not-ready.
• lobes capabilities / lobes endpoint now render the running gateway's GET /capabilities when it is reachable, falling back to an offline .env-derived view tagged "source": "offline" with ready=false on every role. The CLI and the gateway can no longer disagree, because there is now one derivation instead of two.
• A backend is wired only when its *_BASE_URL is set; the or *_SERVED_NAME clause is gone (issue #97).
• senses is documented as vision-only intake. The checkpoint declares audio support but vLLM's gemma4_unified path does not serve it (issue #101); stt remains the supported speech path.
• docs/gemma4-mtp-draft.md carries a superseded banner: DSpark does not load on vLLM 0.23 (#75). Issue #69's disabled-experiment-entry criterion is closed answered-negative.
• README's quickstart no longer claims lobes init scaffolds the single-model deployment; the fleet duo has been the default since #69.

Fixed

• #91 — a dead cortex backend no longer surfaces as a terminal 404 model does not exist. handle_post rewrote the model id once, before the failover loop, then retried the same body against a backend serving a different model, which correctly 404'd; the 4xx = client error, no failover rule relayed that as terminal and killed multi-step agent loops. A dead, unreachable or warming owner now yields 503 + Retry-After with type: backend_unavailable.
• #92 / #95 — the gateway never advertises an origin built from its internal listen port. Precedence is GATEWAY_PUBLIC_URL (an operator override for a tunnel or Host-rewriting proxy) > the request Host header > an empty endpoint. GATEWAY_PUBLIC_URL is deliberately NOT defaulted: a defaulted public_url outranks Host and would advertise loopback to every remote client.
• #96 — AUDIO_URL now reaches the gateway from the base fleet compose, so stt/tts stop advertising ready=true on a path that 404s when the audio overlay is not composed in.
• #97 — GET /v1/models no longer advertises phantom backends wired from a *_SERVED_NAME alone against a default_url naming a container that need not exist.
• An unknown model id returns 404 model_not_found instead of being silently served by the default backend under a different model's weights. Unknown-ness is decided against the routing table, never against the readiness-filtered /v1/models list, so a wired-but-dead backend still yields 503 rather than 404.
• test_live_main_text_returns_nonempty_content no longer fails on a thinking model: max_tokens=16 was consumed entirely by the reasoning trace, leaving content=None and finish_reason=length (issue #93).

[0.39.0] - 2026-07-07

Added

• preserve_thinking on the cortex/main vLLM lane — both compose templates now launch the primary/cortex service with --default-chat-template-kwargs '{"preserve_thinking": true}' next to --reasoning-parser=qwen3, so the served Qwen3.6 chat template retains all historical <think> blocks across a multi-turn conversation (default keeps only the reasoning after the last user turn). Default-on but per-request overridable; scoped to the cortex/main lane (embed/rerank/senses untouched, lobes route still forces enable_thinking=false). Closes #93.
• Reasoning-aware lobes.minor client — assistant_turn_from_response() builds an assistant history message preserving the reasoning / reasoning_content trace, and a new history= parameter on chat_completion / chat_text round-trips it back on subsequent turns (single-turn behaviour unchanged). Part of #93.
• lobes assess --preserve-thinking — a read-only two-turn token-delta diagnostic that proves the reasoning round-trip is live (prompt-token count rises when the assistant <think> history is preserved vs content-only). Part of #93.

[0.38.0] - 2026-07-04

Added

• GET /capabilities now advertises a client-reachable origin for every role's endpoint — derived from the request Host header, overridable with the new optional GATEWAY_PUBLIC_URL env (for tunnels / Host-rewriting proxies) — so a consumer can dial any role's endpoint directly instead of a non-routable internal host (vllm-primary:8000, realtime:8080). This covers all six roles, including stt/tts. Closes #87.
• The realtime bridge exposes GET /v1/health/ready, aggregating Chatterbox (TTS) + Parakeet (STT) readiness into one signal the gateway live-probes.

Changed

• lobes status is now fleet-aware: on a fleet deployment it reports per-gear container states and points at lobes fleet status / lobes capabilities, instead of the contradictory single-model state: model-gear-vllm — not created line printed next to health: ok. A single-model deployment's output is byte-for-byte unchanged. Closes #84.
• GET /capabilities reports stt/tts ready from a live probe of the audio backend (not merely AUDIO_URL being set), so an advertised-ready audio role is genuinely consumable; lobes capabilities --json keeps the configured signal since the host CLI can't reach the internal backends. Closes #89 (readiness).

Fixed

• The gateway returns a clear 503 (Retry-After) for /v1/audio/transcriptions and /v1/audio/speech when the audio backend is reachable but still warming (Chatterbox/Parakeet loading, or a poisoned-CUDA context now surfaced honestly by Chatterbox's /v1/health/ready), distinct from the 502 for a genuinely unreachable backend — closing the "advertised ready but 502s / not client-consumable" gap. Closes #89.
• The gateway's audio-readiness probe (probe_audio_ready) now degrades a malformed AUDIO_URL (a non-numeric port makes urlsplit(...).port raise ValueError) or a broken HTTP exchange (http.client.HTTPException) to "readiness unknown" instead of letting the exception crash the GET /capabilities / POST /v1/audio/* handler — mirroring open_upstream's guard. (Qodo review, #90.)
• build_role_registry clamps the stt/tts ready signal on the audio overlay being configured, so an unconfigured overlay can never report ready=True with an empty endpoint even if a caller passes audio_ready=True — the public builder now enforces the "unconfigured ⇒ not ready" invariant its docstring already promised. (Qodo review, #90.)
• cmd_status is split into _cmd_status_fleet / _cmd_status_single render helpers (mirroring the existing _cmd_status_pressure), leaving the verb as pure dispatch — dropping its Cognitive Complexity from 16 to under the gate's 15. Each helper uses a single-return if/else (like _cmd_status_pressure) rather than an early return, so it renders its one exit path without tripping python:S3516 ("always returns the same value"). Behavior-preserving; the fleet and single-model outputs are byte-for-byte unchanged. (SonarCloud python:S3776 + python:S3516, #90.)

[0.37.0] - 2026-07-03

Changed

• Under swap/iowait pressure the gateway now sheds a main/cortex or multimodal/senses request with HTTP 429 + Retry-After (busy — retry shortly) instead of silently degrading it onto a different model. The degrade-to-minor path is removed outright (no LOBES_PRESSURE_POLICY toggle); an explicit minor request is still served as the floor. Busy is disclosed via X-Lobes-Tier-Reason: busy and an OpenAI-shaped server_busy error body, distinct from the hard 502 upstream_unavailable; callers (the acp vllm-local provider, colleague, generic OpenAI SDKs) must retry with backoff. lobes status --pressure and the gateway GET /status now report the busy-policy state (mode/shed/retry_after). The trigger reuses the existing swap/iowait signal; the tunable thresholds (#86) and the /proc sampler are untouched.

Fixed

• Pressure no longer crosses capabilities: on a default fleet with minor unwired, a pressured cortex/main request previously fell through the tier upward-fallback onto the Gemma multimodal gear (a different capability), silently answering an authoritative-reasoning request with a perception model. Removing the degrade path makes that cross-capability substitution structurally impossible. Closes #85.

[0.36.2] - 2026-07-03

Fixed

• Fleet gateway now receives the pressure-policy thresholds (LOBES_SWAP_DEGRADED_THRESHOLD / LOBES_IOWAIT_DEGRADED_THRESHOLD) via its compose environment, so operators can tune the swap/iowait degrade triggers per box through .env. Previously those vars were only read from the gateway container's env, which the compose never populated, so the knobs silently stayed on the code defaults (75 / 50). Notably lets a box with an unreliable /proc/pressure/io (phantom high iowait on an idle disk, e.g. the DGX Spark GB10) raise LOBES_IOWAIT_DEGRADED_THRESHOLD to 100 instead of permanently degrading the generate lane. Documented in env.example + regression test added.

[0.36.1] - 2026-07-03

Fixed

• Gateway GET /capabilities (and lobes capabilities when read via the gateway) reported catalog-native context instead of the served --max-model-len, because the gateway container's environment lacked PRIMARY_/MULTIMODAL_/EMBED_/RERANK_MAX_MODEL_LEN. The fleet compose now passes those into the gateway service so the #81 served-context overlay resolves (cortex 131072, senses 32768); regression test added.

[0.36.0] - 2026-07-03

Added

• cortex/senses role-based Colleague contract: six first-class roles (cortex, senses, embedder, reranker, stt, tts) with responsibilities/forbidden_responsibilities (#81)
• lobes capabilities [--json] and lobes endpoint <role> — read-only role discovery over the role registry
• Gateway GET /capabilities — machine-readable {role: {endpoint, model, context, ready, responsibilities, ...}} contract for Colleague
• lobes up <role> and lobes up colleague-stack — role-based serving (dry-run by default, --apply to run; colleague-stack bundles the audio overlay)
• lobes measure [--role] [--json] — per-role runtime-only metrics (TTFT/decode-tps/prefill, docs-per-sec, RTF)
• lobes benchmark --profile {cortex-only,cortex+senses,senses-direct,qwen-nvfp4-vs-bf16,all} — comparison profiles (runtime-only)
• lobes explain roles (aliases: colleague, colleague-stack, capabilities) and docs/colleague-stack.md

Changed

• Fleet context rebalance: cortex (Qwen 27B MTP) served at 128K, senses (Gemma 4 12B) at 32K (util 0.14, provisional); default fleet budget 0.30+0.14+0.06+0.06 = 0.56
• cortex->primary and senses->multimodal added to catalog.TIER_ROLE as the primary contract; main|multimodal|hard|normal|cheap|minor kept as back-compat aliases; brain forbidden
• lobes fleet status now reports the always-on Gemma (senses) container (FLEET_MULTIMODAL added to FLEET_CONTAINERS)

Fixed

• Latent circular import between lobes.roles and lobes.gateway surfaced when lobes.roles was imported first

[0.35.0] - 2026-07-02

Added

• catalog: coolthor/gemma-4-12B-it-NVFP4A16 (Gemma 4 12B NVFP4 base it-model) as the new default multimodal gear, native MTP wired ON by default ({"method": "mtp", "model": "google/gemma-4-12B-it-assistant", "num_speculative_tokens": 1}) -- measured 28.6 tok/s decode @ 57.9% draft acceptance, the fastest Gemma config on the DGX Spark (docs/vllm-nightly-migration.md §7)
• fleet compose: opt-in vllm-multimodal-coder service (profiles: [multimodal-coder]) so the demoted coder gear stays reachable; gateway wires an opt-in MULTIMODAL_CODER_BASE_URL/MULTIMODAL_CODER_SERVED_NAME backend + a multimodal-coder alias, added only once wired

Changed

• catalog: sakamakismile/gemma-4-12B-coder-fable5-composer2.5-MTP-NVFP4 demoted from role_hint=multimodal to role_hint=candidate (kept, cite-don't-delete) -- native MTP measured only 30.8% draft acceptance on the coder fine-tune, not worth wiring by default
• gateway: _DEFAULT_MULTIMODAL now points at coolthor/gemma-4-12B-it-NVFP4A16; the multimodal/normal tier aliases resolve to it
• docs/gemma-4-12b-nvfp4.md, README.md, docs/gateway-fleet.md, docs/qwen3-14b-nvfp4.md updated to describe both Gemma gears (default base + opt-in coder) and cite docs/vllm-nightly-migration.md §7 for the benchmark evidence

[0.34.1] - 2026-07-01

Added

• docs/gemma-4-12b-nvfp4.md — first throughput/prefill benchmark for the Gemma 4 12B multimodal gear on the DGX Spark GB10: ~23 tok/s single-stream decode (23.0 sustained over 1,500 tok), prefill ~2,650 tok/s (847 tok) / ~1,954 tok/s (6,682 tok), on vLLM 0.23.1rc1.dev672 native gemma4_unified
• README acknowledgement of Mieszko Syty (FutureProofHomes; Jetson AI Lab) alongside shahizat

Changed

• docs/gemma-4-12b-nvfp4.md notes a config-drift follow-up: on the current :nightly-audio (dev672) image the default lane util 0.12 + VLLM_MEMORY_PROFILER_ESTIMATE_CUDAGRAPHS=0 no longer boots 8192 co-resident (cudagraph accounting changed) — benchmarked at util 0.15 with a trimmed cudagraph capture set

Fixed

• docs/gemma-4-12b-nvfp4.md — made the #75 speculative-decoding section internally consistent: it now reads as CLOSED (route resolved, wire/measure/verdict not implemented) throughout, matching the "Resolved" bullet, instead of framing #75 as active work (Qodo); also corrected the stale claim that the 12B lane decodes slower than the primary — the benchmark shows it out-decodes the primary single-stream (~23 vs ~18–19 tok/s)

[0.34.0] - 2026-07-01

Added

• Gemma 4 12B multimodal gear now SERVES (text + image + audio) — live-validated on the DGX Spark GB10 via vLLM nightly's native gemma4_unified class (#71/#73); catalog status promoted configured → load-tested

Changed

• Dockerfile.vllm-gemma4 rebased FROM vllm/vllm-openai nightly (pinned by digest) + the vllm[audio] extra (librosa==0.11.0 soundfile==0.14.0 av==17.1.0 soxr==1.1.0, pinned to the live-validated set) (was NGC 26.06 / vLLM 0.22.1 + a transformers overlay); vllm-multimodal compose/env now set VLLM_MEMORY_PROFILER_ESTIMATE_CUDAGRAPHS=0 and default MULTIMODAL_MAX_MODEL_LEN 8192 (co-resident util-0.12 KV holds ~24K tokens, not the 128K native)

Fixed

• Gemma 4 12B serve blocker root-caused and fixed: gemma4_unified has heterogeneous per-layer head sizes (40 sliding@256 + 8 full@512) that only vLLM's native class handles — released vLLM ≤0.22.1 fell back to the transformers backend and crashed the full-attention o_proj (4096≠8192); a TRITON_ATTN backend flag does not fix it

[0.33.1] - 2026-07-01

Added

• Issue #75 spec + plan (Gemma 4 12B gear speculative decoding) converged via devague /think + /spec-to-plan
• docs/gemma4-mtp-draft.md — resolved spec-decode draft route (DSpark draft_model first; native google/gemma-4-12B-it-assistant recorded as escalation candidate)

Changed

• docs/gemma-4-12b-nvfp4.md — added the Speculative decoding (#75) before-state / gap / scope-split subsection

[0.33.0] - 2026-06-30

Added

• Custom vLLM image Dockerfile.vllm-gemma4 (FROM nvcr.io/nvidia/vllm:26.06-py3, vLLM 0.22.1, + a pinned from-source Transformers 181beb3) so the Gemma 4 12B gemma4_unified multimodal gear loads (#71)
• MULTIMODAL_IMAGE override for the vllm-multimodal service (local build by default; optional ghcr.io/local-registry tag)
• MULTIMODAL_ATTENTION_BACKEND env (TRITON_ATTN) for Gemma 4 non-square attention

Changed

• Multimodal gear quantization corrected to compressed-tensors (was modelopt_fp4) after live validation (#71)
• Removed the invalid gemma4_mtp speculative_config from the multimodal gear (vLLM Gemma4 MTP needs a separate gemma4_assistant draft model)

Fixed

• docs/compose comments now name the correct base image and MULTIMODAL_IMAGE registry semantics

[0.32.1] - 2026-06-30

Added

• docs/specs: Gemma 4 12B multimodal-duo spec (issue #69) — /think frame for default-serving the Qwen3.6-27B-MTP + Gemma4-12B duo as main/minor/multimodal tiers (vision+audio), native-MTP on by default, DSpark draft as a disabled experiment, 14B demoted to a legacy candidate
• docs/plans: buildable plan for the Gemma duo (9 tasks across 5 dependency waves, 6 accepted-risk objects) via /spec-to-plan — covers all 26 spec targets; resolves the main/minor/multimodal pressure-ladder seam as a first-class task

Changed

• Reworded the three Gemma-risk markers in catalog.py / runtime/_parser.py from bare TODO(risk …) comments to Risk … (pending #71) — the deferred live-validation work is tracked in issue #71 (gemma4_unified won't load on released vLLM images), so the comments now cite the tracking issue instead of an untracked TODO (clears SonarCloud python:S1135).

Fixed

• Gateway: re-wire the legacy 14B middle generate backend from MIDDLE_BASE_URL / MIDDLE_SERVED_NAME in build_config(). The #69 14B demotion dropped the wiring but the compose template still ships the vllm-middle profile + those env vars, so enabling the profile silently fell back to the primary; the 14B is again reachable by its explicit served name (and, as intended, gets no tier alias). (Qodo)
• Gateway: a GATEWAY_ALIASES operator override keyed by a legacy tier alias (hard/cheap/normal) is now honoured on the pressure-aware tier path. Tier requests normalize to the new vocabulary (hard→main) before the alias lookup, which bypassed a legacy-keyed override; build_config() now mirrors a tier-keyed override onto its vocabulary synonyms (explicit keys still win). (Qodo)

[0.32.0] - 2026-06-30

Added

• Third capability tier: opt-in vllm-middle 14B-NVFP4 generate gear (COMPOSE_PROFILES=middle, GPU mem-util 0.12), inference-only (not a LoRA base).
• Gateway capability-tier aliases — callers send model=cheap|normal|hard and the gateway resolves to the 4B/14B/27B generate gears (same-task alias on top of task-family routing) with upward fallback when a tier is absent.
• Read-only host memory-pressure sampler (swap_used_percent/iowait_percent from /proc) and a swap/iowait pressure policy with a degraded-mode state machine (env-overridable thresholds).
• Pressure-aware tier downgrade at the gateway with an X-Lobes-Override bypass header; the served tier and reason cross the OpenAI boundary via X-Lobes-Tier / X-Lobes-Tier-Reason response headers.
• lobes status --pressure — read-only snapshot of the current tier ceiling, mode, reason, and live swap/iowait.
• scripts/validate-tiers.sh + docs/validate-tiers.md — operator-run live validation harness for the three-tier fleet on the Spark.

Changed

• 27B primary default served context trimmed 256K→128K (PRIMARY_MAX_MODEL_LEN=131072) and PRIMARY_GPU_MEM_UTIL lowered to 0.45 so the co-resident 14B middle gear fits within the 128GB unified-memory budget (0.45 + 0.12 + 0.10 + 0.06 + 0.06 = 0.79).

[0.31.1] - 2026-06-27

Changed

• Mutation-safety prose in lobes learn and CLAUDE.md now lists the fleet up / fleet down write verbs (was only in the --json payload).
• CLAUDE.md documents the turn-on/turn-off lifecycle explicitly (serve/stop and fleet up/down) instead of leaving it implicit in the verb names.

[0.31.0] - 2026-06-26

Added

• lobes benchmark --all-lobes --concurrency auto: per-lobe (minor + primary) performance benchmark routed through the gateway — single-stream decode tok/s, prefill TTFT, concurrent throughput with auto-ramp to the throughput knee (req/s + p50/p95 latency + ms/token), plus the logprobs cat soft-score, rendered as one combined minor-vs-primary report with per-metric deltas.
• lobes eval cat --score logprobs --mode open|closed: read-only 'Where is the cat?' temporal-reasoning probe, scored by logprobs (softmax over candidate-location full-sequence echo logprobs as the headline, with a chat first-token-mass cross-check and graceful fallback when echo is unavailable).
• lobes.bench package: cat_probe (deterministic, seeded timestamped-narrative generator with exactly one unambiguous current location; open + closed modes), cat_score (echo-softmax headline scorer + first-token cross-check + fallback), and report (per-lobe markdown report renderer with minor-vs-primary deltas).
• lobes.minor logprobs plumbing: chat_completion now forwards logprobs/top_logprobs; new completions_echo (full-sequence /v1/completions echo scoring) and gateway_supports_echo capability probe (never raises; lets callers fall back).
• lobes.assess per-lobe perf engine: measure_prefill_ttft, run_concurrent (requests/sec + p50/p95 latency + ms/token), and auto_ramp_concurrency (1→2→4→… ramp with plateau/knee detection).

[0.30.0] - 2026-06-26

Added

• The minor lobe — a cheap, warm co-resident Qwen3.5-4B small-brain (issue #64). A new switchable catalog gear Qwen/Qwen3.5-4B (role_hint="minor", served bf16 — the first unsloth-LoRA fine-tune target; multimodal, served text-only via --language-model-only), reachable both as a switchable gear and as an opt-in warm co-resident backend alongside the 27B primary.
  • New read-only verbs: lobes run minor "<prompt>" (call the minor model), lobes route "<text>" (classify a task across catalog gears with an escalate flag + confidence), and lobes eval minor --suite <path> (run a JSONL eval suite). All three default --base-url to the gateway (http://localhost:8000/v1) and reuse a new stdlib-only urllib client (lobes.minor) — no new runtime dependencies.
  • Governance + escalation (lobes.minor.governance): the minor role may prepare/classify/format/validate/suggest/summarize/route, and escalates on forbidden actions (approve/finalize/delete/deploy/architectural) or any of five escalation conditions. Role-keyed, not model-keyed.
  • Warm co-residency: an opt-in vllm-minor fleet service (compose profile minor + MINOR_BASE_URL/MINOR_SERVED_NAME gateway env gate); the gateway routes the minor model id to it with failover to the primary. Default fleet behavior is unchanged.

Changed

• runtime/_parser.py recognizes the Qwen3.5 family → qwen3_coder (it emits the XML function-call format, not Hermes JSON).
• Catalog supports an unquantized bf16 generate gear via a quantization="none" sentinel that lobes switch normalizes to "omit --quantization" and surfaces as a required compose edit.

Fixed

[0.29.0] - 2026-06-24

Added

• Memory-discipline "Conventions and workflow" section in CLAUDE.md — a per-task recall-before / remember-after convention (scope localized to this repo's nick) so the vendored remember / recall skills are actually used, not just present: /recall before non-trivial work to build on prior decisions instead of re-deriving them, and /remember when a non-obvious decision, constraint, fix-and-why, or hard-won gotcha surfaces. The section documents this repo's memory as in-repo and public — records resolve to <repo-root>/.eidetic/memory (committed, team- and mesh-shared). Inserted idempotently (skipped if already present), slotted under an existing "Conventions and workflow" heading when one exists, else appended.

Changed

• Refreshed the remember + recall wrappers from eidetic-cli 0.10.0 (cite-don't-import) — picks up eidetic's project-local store default: the files backend now resolves per record by visibility — PUBLIC records inside a git repo go to <repo-root>/.eidetic/memory (committed, team-shared), PRIVATE records (or any record outside a repo) go to $HOME/.eidetic/memory (never committed), an explicit EIDETIC_DATA_DIR still wins, and recall reads both stores and merges. Also carries the 0.9.3 hardening (interactive-stdin guard, help as a search term, SIGPIPE-safe suffix parsing). Recipe policy override (the wrappers here are NOT byte-verbatim): the injected default visibility is flipped from eidetic's private to public, so a plain /remember lands the note in ./.eidetic/memory in this repo, kept as part of the repo — pass --visibility private to route a record to $HOME instead. remember drives eidetic remember (idempotent upsert of one JSON record or an NDJSON batch on stdin); recall drives eidetic recall with four search modes (exact / approximate / keyword / hybrid). Each SKILL.md is localized only in the illustrative --scope <nick> examples (Provenance keeps "First-party to eidetic-cli"). Runtime dep: the eidetic CLI on PATH (else a local eidetic-cli checkout with uv) — eidetic >= 0.10.0 for the in-repo routing; on an older CLI the public records still work but are stored in $HOME/.eidetic/memory instead of in-repo. Propagated by rollout-cli's eidetic-memory recipe.

[0.28.1] - 2026-06-26

Added

• docs/tensorrt-llm-investigation.md — a dated desk investigation (no live run) of serving the MTP 27B primary (sakamakismile/Qwen3.6-27B-Text-NVFP4-MTP) with TensorRT-LLM (trtllm-serve) on the DGX Spark (GB10/SM121) instead of vLLM. Verdict: not yet — TRT-LLM MTP spec-decode is DeepSeek-only in stable releases and the Qwen3.6 hybrid GDN/DeltaNet kernels are RC-only (both land in 1.3.0 RC builds); serving on a stable TRT-LLM today would forfeit the ~2.4× decode win the checkpoint exists for. Records the engine-integration seam (the request path — gateway routing + lobes assess/benchmark — is already engine-agnostic, while the gateway /status vllm:* metrics path, catalog.py, switch.py, templates, and VLLM_* env vars are vLLM-specific), a feasibility table by dimension with confidence levels, a comparison against the recorded vLLM baseline, a minimal spike recipe, an explicit revisit trigger (TRT-LLM 1.3.0 stable), and 11 cited sources. Linked from the README per-model notes.

Changed

Fixed

[0.28.0] - 2026-06-23

Added

• Vendored the remember + recall memory skills from eidetic-cli (cite-don't-import) — the write/read halves of eidetic's shared ~/.eidetic/memory surface, so this agent (Claude and its colleague backend) can persist facts across sessions and recall them later, sharing one store. remember drives eidetic remember (idempotent upsert of one JSON record or an NDJSON batch on stdin, dedup by id + content hash); recall drives eidetic recall with four search modes — exact / approximate / keyword / hybrid — each hit carrying text, full provenance metadata, a relevance score, and a freshness signal. The .sh wrappers are byte-verbatim from eidetic-cli (their first-party origin); each SKILL.md is localized only in the illustrative --scope <nick> examples (Provenance keeps "First-party to eidetic-cli"). Both default to this agent's PRIVATE scope, reading the suffix from culture.yaml. Runtime dep: the eidetic CLI on PATH (else a local eidetic-cli checkout with uv). Propagated by rollout-cli's eidetic-memory recipe.

[0.27.0] - 2026-06-22

Changed

• Renamed the tool from model-gear/model to lobes/lobes-cli. The binary is now lobes (lobes switch, lobes serve, lobes assess, …), the import package is lobes, and the PyPI distribution is lobes-cli. The deployed Culture agent is renamed model-gear → lobes (culture.yaml, AGENTS.md).
• Deployment dir is now ~/.lobes (env $LOBES_DIR). The legacy $MODEL_GEAR_DIR and ~/.model-gear are still resolved as fallbacks, so a pre-rename deployment keeps working with the renamed CLI without redeploying.
• Deployment-internal names are intentionally kept as model-gear so a live fleet isn't disrupted: Docker container_names (model-gear-vllm, model-gear-gateway, …), mg-logwrap.sh, MODEL_GEAR_LOG_DIR, and the served-model id are unchanged.

Added

• model is kept as a deprecated alias command for lobes (same entry point); --version/help reflect whichever name was invoked.
• model-gear is published on PyPI as a deprecated alias of lobes-cli: a metadata-only shim package (packaging/model-gear/) that depends on lobes-cli==<same version>, plus a publish-alias job in publish.yml that builds and publishes it after the main release.

[0.26.4] - 2026-06-21

Changed

• Relicensed the project from MIT to Apache 2.0 — full Apache 2.0 LICENSE text, pyproject license/classifier metadata, and a new README License section. Aligns with sibling AgentCulture repos (e.g. colleague, data-refinery-cli).

[0.26.3] - 2026-06-21

Fixed

• Doc consistency: docs/mistral-small-3.2-24b-nvfp4.md and docs/qwen3.6-35b-a3b-nvfp4.md still framed Mistral as the default fleet fallback the gateway pairs with the primary. The fleet has run one generate backend by default since the single-backend default (#42); the warm fallback is opt-in. Reframed both docs to match — closing the drift with the model explain catalog corrected in 0.26.2.
• Corrected the Mistral doc's "How it runs in the fleet" section, which described a fallback wiring that no longer exists: FALLBACK_MODEL/FALLBACK_MAX_MODEL_LEN /FALLBACK_GPU_MEM_UTIL/… .env keys "scaffolded by model init --fleet" and a shipped model-gear-vllm-fallback service. The current templates ship no fallback service and the gateway reads only FALLBACK_URL + FALLBACK_SERVED_NAME (set after you manually add a vllm-fallback service). Following the old text produced a non-working config; rewrote it to the actual two-step opt-in, matching docs/gateway-fleet.md → "Adding a fallback".

[0.26.2] - 2026-06-21

Changed

• Doc alignment pass across the audio + fleet surfaces (no behavior change):
  • docs/chatterbox-tts.md: healthcheck shows python3.12 (not the stale python3) and the compose snippet includes container_name: model-gear-chatterbox — matching the 0.26.1 fixes.
  • docs/realtime-pipeline.md: the TTS service is chatterbox (was tts); the overlay scaffolds a Chatterbox Dockerfile too.
  • docs/openai-api.md: corrected the auth caveat — the gateway is a pass-through and is not auth-aware for any proxied endpoint (the previous wording implied it gated /v1/chat/completions).
  • docs/gateway-fleet.md: endpoint list now includes /v1/audio/*; added an "Auth (known limitation)" note.
  • model explain gateway / model explain tunnel (explain/catalog.py): added the gateway-not-auth-aware caveat; fixed the Mistral entry (opt-in fallback candidate, not the active default pairing); listed tunnel/fleet as write verbs.
  • model learn (learn.py): added an "Auth / exposure" section + an auth_exposure JSON field, and model explain tunnel/gateway pointers.

[0.26.1] - 2026-06-21

Fixed

• model init --fleet --audio now scaffolds Dockerfile.chatterbox. The Chatterbox sidecar landed in 0.25 (the compose chatterbox service builds from Dockerfile.chatterbox), but the build file was never added to AUDIO_TEMPLATES, so the scaffold omitted it and docker compose build chatterbox failed with "Dockerfile.chatterbox: no such file". Added it to the audio template set (twin of the Dockerfile.realtime / Dockerfile.parakeet wiring) so the audio overlay can actually build and serve TTS.
• model fleet status now reports the TTS gear. FLEET_TTS still pointed at the old model-gear-tts container name, but the Chatterbox sidecar renamed the container to model-gear-chatterbox — so status listed the live TTS gear as "not created". Pinned FLEET_TTS to model-gear-chatterbox and added a test that asserts every FLEET_AUDIO_CONTAINERS name matches a container_name: in the packaged audio compose (catches future rename drift).
• Chatterbox container now reports healthy. Its Dockerfile.chatterbox installs the interpreter as python3.12 (no python3 symlink), but the compose healthcheck called bare python3 — which exec-failed every interval, pinning the working container at "starting"/"unhealthy". Switched the healthcheck to python3.12 and added a test tying the healthcheck interpreter to the one the Dockerfile provides.

[0.26.0] - 2026-06-21

Documentation pass for the realtime audio overlay and the OpenAI API front: a feature doc per audio backend, a consolidated endpoint reference, and the same information surfaced through model learn, model explain, the README, and CLAUDE.md.

Added

• docs/parakeet-stt.md: per-model feature doc for the Parakeet STT backend (nvidia/parakeet-tdt-0.6b-v2, NeMo ASR) — the only audio model that lacked one. Covers the HTTP contract, the real (model-loaded + CUDA-live) readiness probe, fleet integration, the stale-CUDA-context runbook, and why it is not a switchable catalog gear.
• docs/openai-api.md: consolidated OpenAI-compatible API surface reference — every endpoint (/v1/chat/completions, /v1/completions, /v1/embeddings, /v1/rerank, /v1/score, /v1/audio/transcriptions, /v1/audio/speech, /v1/models, /v1/models/supported, /health), routing semantics (name / default / failover / SSE / audio fan-out), per-endpoint curl examples, the loaded-vs-supported split, and auth/exposure.
• model explain topics: realtime / audio (the /v1/audio/* overlay), transcribe / stt / parakeet (STT), speak / tts / chatterbox (TTS), and api / openai (the endpoint surface); linked from the explain root.
• model learn now documents the realtime audio overlay and the OpenAI API surface (text + --json realtime_audio / api_surface fields).
• README sections for Realtime audio (STT + TTS) and The OpenAI-compatible API surface, plus the two audio backends added to the per-model notes.

Changed

• CLAUDE.md: documents the realtime audio overlay alongside the fleet; the CLI package tree now lists the gateway/, realtime/, explain/, and catalog.py surfaces.

Fixed

• docs/realtime-pipeline.md: removed the stale NGC_API_KEY bring-up step (a Magpie leftover — Chatterbox needs no NGC key) and documented the TTS → STT round-trip in scripts/audio-smoke.py.

[0.25.0] - 2026-06-21

Added

• Chatterbox TTS sidecar (model_gear/realtime/chatterbox_server.py): a FastAPI HTTP server (GET /v1/health/ready, POST /v1/audio/synthesize) that wraps Resemble AI's Chatterbox model and returns raw PCM16 mono 24 kHz audio. Supports zero-shot voice cloning via a .wav reference path. Runs as the chatterbox fleet service built by the new Dockerfile.chatterbox (arm64 cu128 recipe).
• [chatterbox] optional-deps group (fastapi, uvicorn) in pyproject.toml.
• docs/chatterbox-tts.md: bake-off numbers, arm64 install recipe, sidecar HTTP contract, and integration notes.
• model_gear/templates/fleet/Dockerfile.chatterbox: arm64 CUDA build — rebased on nvidia/cuda:12.8.0-cudnn-runtime-ubuntu24.04 (no preinstalled torch) with pinned torch==2.11.0+cu128 + torchaudio==2.11.0+cu128 + Perth, fixing the NGC pytorch ABI conflict with Perth observed at runtime.
• numpy fast path in float_tensor_to_pcm16 (stdlib fallback kept for offline CI); parity test in tests/test_chatterbox_pcm16.py.
• resolve_voice() .wav check is now case-insensitive (.WAV / .Wav work).
• Dead SSML code removed from tts_client.py (_insert_ssml_breaks); stale Magpie references updated to Chatterbox across app, client, and tests; speed-ignored warning emitted when a non-default speed is passed to synthesize().

Changed

• Replaced Magpie TTS with Chatterbox across the realtime stack: protocol.py (TTS_SAMPLE_RATE 22050→24000, resolve_voice rewritten for Chatterbox — .wav path for cloning, "" for default), tts_client.py (plain JSON POST, no SSML/prosody wrapping), _settings.py (tts_url default → http://chatterbox:9000, default_voice default → ""), docker-compose.audio.yml (new chatterbox: service replaces tts:), env.audio.example (Magpie/NGC vars removed, CHATTERBOX_PORT added).

[0.24.0] - 2026-06-20

Added

• model overview --live — a live fleet dashboard. overview was a static description; --live now probes the running deployment and reports the five "what is it doing right now" views: online (per-backend health), offered (served + candidate models, task families, the endpoint list), busy (in-flight / queued requests), usage (cumulative prompt/generation tokens and finished requests by reason), and endpoints. It is read-only and HTTP-only — it works against a local deployment or a model tunnel hostname alike, and degrades gracefully when a backend or its metrics is unreachable.
• Gateway GET /status — a model-gear-native JSON aggregate. The fleet's backends are internal-only, so the gateway fans out to each one's /health + /metrics and returns {object: "model-gear.fleet_status", default_model, busy: {running, waiting}, backends: [...], endpoints: [...]}. This is the source model overview --live reads in the fleet (a bare single-model server is read directly from its /metrics + /health).
• model_gear._metrics — a small stdlib-only helper that parses vLLM's Prometheus /metrics (running/waiting, prompt/generation tokens, request_success_total by finish reason, KV-cache usage) and best-effort HTTP probes that never raise.

Changed

Fixed

[0.23.0] - 2026-06-20

Added

• Durable vLLM logs that survive restart/recreate (#50). When a vLLM container restarted, its docker logs — and any EngineCore crash trace — were lost, which blocked root-causing #50 for lack of data. model init now scaffolds mg-logwrap.sh, bind-mounted as each vLLM service's entrypoint: it tees stdout+stderr to a per-boot file <service>-<boot>.log under a host-mounted log dir (${MODEL_GEAR_LOG_DIR:-<deploy>/logs} → /logs/model-gear), then execs the real command so vLLM stays the signal target (graceful shutdown) and the exit code (and restart: policy) are unchanged. Teeing at the process-I/O level captures both Python tracebacks and native CUDA/C++ aborts; if logging can't be set up it falls back to a plain exec and never blocks serving. The crash boot is preserved as its own file. Wired into the single-model and fleet (primary/embed/rerank) compose templates. See docs/durable-logs.md.
• model logs — new read-only verb to list/tail the durable logs, reading the host files directly so it works even after the crashed container is gone: model logs (list boots), model logs <service> (tail latest), and model logs <service> --previous (tail the boot that crashed, after a restart).

Changed

• model init / model serve / model fleet up pre-create the host log dir (user-owned) before compose bind-mounts it, so logs are never root-owned.

Fixed

[0.22.1] - 2026-06-19

Fixed

• model fleet status now reports the embedding + reranker gears. FLEET_CONTAINERS listed only vllm-primary + gateway, so model fleet status silently omitted the vllm-embed / vllm-rerank containers the default fleet (#44/#47) actually runs. Added FLEET_EMBED / FLEET_RERANK to the default container set — status now lists all four (the opt-in generate fallback stays excluded, as it is not in the default compose).

Changed

• Aligned the agent/human-facing prose with the co-resident gears (#44/#47). model learn, model overview, model explain (root + fleet), model init --fleet help, the fleet docstring, the scaffolded env.example / docker-compose.yml comments, README.md, CLAUDE.md, and docs/gateway-fleet.md still described the fleet as a "2-model" / "two-container" / "single-backend" deployment. They now describe the default fleet as the generate primary plus co-resident embedding + reranker gears behind one gateway, routed by task family (generate / embed / score / rerank), with the generate fallback as the only opt-in backend. Added a "Task families & gears" section + explain embeddings / explain rerank pointers to model learn.

[0.22.0] - 2026-06-19

Added

• Embedding + reranker gears (closes #44). model-gear now serves two pooling gears alongside the chat primary, reachable through the same OpenAI-compatible gateway and routed by the request's model field:
  • Qwen/Qwen3-Embedding-0.6B — POST /v1/embeddings (vLLM --runner pooling --convert embed), native 1024-dim, MRL-truncatable via the dimensions param (Matryoshka --hf-overrides).
  • Qwen/Qwen3-Reranker-0.6B — POST /v1/rerank + /v1/score (vLLM --runner pooling --convert classify, served via the Qwen3ForSequenceClassification --hf-overrides).
  • Catalog: SupportedModel gains task (generate/embed/score), dimension, and hf_overrides; both gears surface in model overview --list and GET /v1/models/supported.
  • Fleet: vllm-embed + vllm-rerank services in the fleet compose (always-warm, small --max-model-len/--gpu-memory-utilization so they co-reside with the 27B on a single GB10), wired as gateway backends.
  • Gateway: task-aware failover — an embed/score request never fails over to a generate backend (and vice versa); chat primary↔fallback failover preserved.
  • CLI: model switch --task {generate,embed,score} for solo serving; model explain embeddings / rerank / score document the call shapes; per-model docs under docs/.
  • Boundary: model-gear serves the gears only — no vector store, index, chunker, or retrieval lands here (guarded by a test); storage + retrieval are the consumer's half (eidetic-cli).

Changed

Fixed

[0.21.1] - 2026-06-19

Fixed

• markdownlint: exempt skill prompt templates (.claude/skills/**/prompts/**) from markdownlint. These are model-facing prompts fed verbatim to a backend (first line is $ARGUMENTS or a prose instruction), so MD041 (first-line H1) and MD032 are inapplicable — a heading would be injected into the prompt. SKILL.md is still linted; only prompts/ is exempt. Unblocks the lint CI job after the ask-colleague skill was vendored in.

[0.21.0] - 2026-06-12

Changed

• The fleet is now single-backend by default (Qwen primary only); the Mistral fallback is removed. Live validation showed two ~30B NVFP4 models don't co-fit a shared GB10, so the warm dense Mistral-Small-3.2-24B fallback has been dropped from the default fleet and the primary restored to its load-tested solo headroom: PRIMARY_GPU_MEM_UTIL 0.40 → 0.6 and PRIMARY_MAX_MODEL_LEN 32768 → 262144 (full 256K). The vllm-fallback service is gone from fleet/docker-compose.yml, and FLEET_CONTAINERS no longer includes it.
• The gateway makes the fallback optional. build_config now adds a second backend only when FALLBACK_URL or FALLBACK_SERVED_NAME is set in env — so the default gateway serves the primary alone (no failover target), and a two-backend fleet still works for anyone who wires one up. Routing/failover primitives are unchanged; order_backends returns just the primary when solo.
• Mistral stays a selectable catalog candidate (model overview --list) and the documented opt-in fallback — only its role as the default fleet fallback is removed. README, docs/gateway-fleet.md, and the model explain fleet/gateway / model init --help text are updated to the single-backend default (with an "Adding a fallback" guide).

Fixed

• docs/gateway-fleet.md uses $HOME/.model-gear instead of the non-portable ~/.model-gear.

[0.20.1] - 2026-06-12

Fixed

Qodo review of #41:

• model init --fleet --audio now scaffolds _readiness.py — added fleet/_readiness.py → _readiness.py to _compose.AUDIO_TEMPLATES. The Parakeet Dockerfile.parakeet COPY _readiness.py requires it at the deployment-dir root, so a clean audio init previously produced a tree where docker compose build stt would fail. Covered by test_init.py.
• Parakeet readiness drift guard + simplification — removed the third (inline) copy of the readiness decision from listen_server.py (the scaffold now guarantees the vendored _readiness.py is present), and added a test asserting the vendored twin stays behaviourally identical to the canonical model_gear/realtime/_readiness.py.
• CUDA readiness probe failures are now logged — listen_server.health() emits a logger.warning with the exception type/message before returning 503, so operators can distinguish driver-down / OOM / stale-context.
• scripts/audio-smoke.py now exercises /v1/audio/speech (it previously claimed both routes but only tested transcriptions) and wires the formerly unused --stt-url to a direct-Parakeet transcription check.
• docs/realtime-pipeline.md uses $HOME/.model-gear instead of the non-portable ~/.model-gear.

[0.20.0] - 2026-06-12

Added

• docs/realtime-pipeline.md — the previously-missing runbook for the audio surface: that model-gear owns the live :8080 realtime facade, the model init --fleet --audio / model fleet up bring-up, the topology (gateway path-routes /v1/audio/* → realtime → Parakeet/Magpie), the drift it fixed (#39/#40), the cheap readiness probe, and the stale-Parakeet-CUDA restart runbook. Resolves a doc referenced from pyproject.toml, the audio overlay, and the realtime app docstring but never written.
• scripts/audio-smoke.py — a stdlib-only live smoke test for the audio routes: asserts GET :8080/openapi.json lists both /v1/audio/transcriptions and /v1/audio/speech, then POSTs an in-memory 16 kHz WAV and asserts 200 {text: …}. Reproduces issue #39's repro to confirm the 500→200 fix. Requires a running GPU box (not a CI unit test).
• model_gear/realtime/_readiness.py — a stdlib-only evaluate_readiness() helper backing the Parakeet /v1/health/ready cheap probe; unit-tested in CI without torch/nemo/GPU.

Fixed

• Parakeet STT healthcheck now reflects real model readiness (#39). The vendored templates/fleet/listen_server.py /v1/health/ready returned {"status": "ready"} unconditionally — process liveness only — so a container whose CUDA context had gone stale (CUDA error: unknown error, every transcription 500ing) still reported Docker "healthy". The probe now reports ready only when the NeMo model is loaded and a trivial CUDA tensor op succeeds, returning 503 otherwise (a cheap probe, not a full transcription each interval). The pure decision is vendored into the Parakeet build context and COPY'd into the image so it resolves without the wheel.

[0.19.0] - 2026-06-09

Added

• scripts/gen-api-key.py — generate or rotate the bearer key (CULTURE_VLLM_API_KEY) that gates the served API. The secret is created with the stdlib secrets module and never hardcoded, so the script is safe in the open-source repo; the key only ever lands in the gitignored deployment .env (written 0o600, best-effort). Hidden by default (no echo into logs/scrollback); --show prints it, --force rotates an existing key, and --bytes (min 16) is validated. Resolves the deployment dir like the model CLI (--dir → $MODEL_GEAR_DIR → $HOME/.model-gear), degrades gracefully on an unreadable or non-regular .env, and runs from a wheel install (no model_gear import). Referenced from the README "Expose the API" section.

[0.18.0] - 2026-06-09

Added

• model tunnel — expose the local OpenAI-compatible API from anywhere via a Cloudflare Tunnel (#35). Dry-run by default (prints the cloudflared command and the public https://<host>/v1 URL); --apply starts a standalone cloudflared tunnel run in the background (logging to cloudflared.log in the deployment dir), and --stop --apply tears it down. The public hostname resolves --hostname → $CULTURE_VLLM_PUBLIC_HOSTNAME → CULTURE_VLLM_PUBLIC_HOSTNAME in a gitignored .cf-tunnel.env; the run-token comes from CULTURE_CF_TUNNEL_TOKEN_SHUSHU (a shushu-sealed secret name, preferred) or CULTURE_CF_TUNNEL_TOKEN (plaintext fallback). The token is never placed on the process argv (so it can't leak via ps or the log) — cloudflared reads it from the TUNNEL_TOKEN environment variable, which shushu injects (sealed mode) or the launcher sets directly (fallback). The resolved hostname and sealed-secret name are validated against a conservative charset before they reach the argv (an argument-injection guard). --apply preflights that cloudflared (and shushu) is on PATH, that no tunnel is already running for the deployment, and that the local server answers /health; --stop signals the recorded process group and confirms exit (SIGTERM → SIGKILL) before clearing a PID-reuse-safe pidfile (the recorded pid is identity-checked against /proc so a reused pid can't be killed). No hostname, token, or backend checkpoint id is committed. The Cloudflare side (tunnel + ingress + DNS) is provisioned once by cultureflare remote-login --no-access.
• Optional bearer auth on the served API via CULTURE_VLLM_API_KEY, wired into the single-model docker-compose.yml as VLLM_API_KEY=${CULTURE_VLLM_API_KEY:-}. Empty (default) leaves local dev open; set it and vLLM requires Authorization: Bearer — the gate for any public exposure. Documented in env.example alongside a note that VLLM_SERVED_NAME can be a generic alias to keep the checkpoint name out of the public /v1/models.
• cf-tunnel.env.example scaffolded by model init (single + fleet), a placeholder-only template the owner copies to the gitignored .cf-tunnel.env.
• README "Expose the API from anywhere (Cloudflare Tunnel)" section and a model explain tunnel catalog entry.

[0.17.0] - 2026-06-03

Changed

• Served context raised 128K → full 256K (native) for the MTP primary on DGX Spark. The spark machine profile's max_model_len default is now 262144 (was 131072), with matching changes to the single-model env.example / docker-compose.yml defaults and the model switch --help / model explain text. Load-tested 2026-06-03 on the shared GB10 (util 0.6, --max-num-seqs 2, KV-FP8, MTP n=3): boots clean (CUDA-graph capture, PIECEWISE, 0.71 GiB in 2 s — no OOM), 17.8 tok/s decode, 74.0 % MTP draft acceptance, both model assess probes finish=stop, tool-calling probe passes, and 71,601 MiB (~70 GiB) resident — the same footprint as 32K/128K, because --gpu-memory-utilization fixes the KV-pool reservation (only the addressable context grows). vLLM reports 5.29× max concurrency at a full 256K request, well above the --max-num-seqs 2 decode cap, so there is no practical concurrency cost versus the 128K default. model switch --max-model-len <N> still overrides per deployment, and util stays a conservative 0.6 (shared box). See docs/qwen3.6-27b-text-nvfp4-mtp.md (new 256K benchmark) and docs/tuning-profiles.md.
• Catalog context string updated. The MTP primary now reads "256K native (served at full 256K on the shared GB10)".
• Scope — deliberately left at the old contexts: fleet templates stay at 32K (co-residence with the 24B fallback is a different, still-unvalidated memory regime; fleet/env.example notes this), and the thor / generic machine profiles stay at 32K (unmeasured estimates) with blackwell at 64K. The model switch native-ceiling clamp (added in 0.16.0) still pins 32K-native candidates (nvidia/Qwen3-32B-NVFP4, mmangkad/Qwen3.6-35B-A3B-NVFP4) down to their own ceilings under the new 256K spark default.

Fixed

• model switch warns when an uncatalogued model would inherit an unclamped machine context default. The native-ceiling clamp only protects catalogued models; an uncatalogued model ID (which switch supports) inherits the machine default (now spark's 262144) and would boot-fail if the checkpoint's native context is smaller. switch now emits a clear warning pointing at --max-model-len / cataloguing, rather than silently applying the high default (no silent clamp — an uncatalogued ceiling is unknown, so guessing one is wrong both ways). Addresses a Qodo reliability finding on #34.

[0.16.0] - 2026-06-03

Changed

• Served context raised 32K → 128K for the MTP primary on DGX Spark. The spark machine profile's max_model_len default is now 131072 (was 32768), with matching changes in the single-model env.example /docker-compose.yml defaults. Load-tested 2026-06-03 on the shared GB10 (util 0.6, --max-num-seqs 2, KV-FP8, MTP n=3): boots clean (no CUDA-graph-capture OOM), 18.3 tok/s decode, 73.3 % MTP draft acceptance, both model assess probes finish=stop, and 71,963 MiB (~70 GiB) resident — the same footprint as 32K, because --gpu-memory-utilization fixes the KV-pool reservation (the pool holds 9.6× a full 128K request). model switch --max-model-len <N> still overrides per deployment, and util stays a conservative 0.6 (the box is shared). See docs/qwen3.6-27b-text-nvfp4-mtp.md (new 128K benchmark) and docs/tuning-profiles.md.
• Catalog context strings clarified. The MTP primary now reads "256K native (served at 128K on the shared GB10)"; the non-served candidate / fallback entries (mmangkad/Qwen3.6-27B-NVFP4, the Mistral fallback) drop the stale per-model "capped to 32K" note and state native context only.
• Scope — deliberately left at the old contexts: fleet templates stay at 32K (the fleet runs the primary co-resident with a 24B fallback at lower util — a different memory regime the single-model 128K test does not validate; fleet/env.example notes this), and the thor / generic machine profiles stay at 32K (unmeasured estimates) with blackwell at 64K.

Fixed

• model switch clamps the machine context default to a model's native ceiling. Raising spark's max_model_len default to 131072 made it apply to every model switched to on spark — including the 32K-native catalog candidates (nvidia/Qwen3-32B-NVFP4, mmangkad/Qwen3.6-35B-A3B-NVFP4), where vLLM refuses a --max-model-len above the checkpoint's native limit (no YaRN) and the container fails to boot. SupportedModel now carries a numeric native_max_model_len, and model switch clamps the resolved context down to it when no explicit --max-model-len is given (an explicit value still wins, for opted-in YaRN configs). Fixes a Qodo correctness finding on #33.

[0.15.0] - 2026-05-31

Changed

• Fleet default primary → sakamakismile/Qwen3.6-27B-Text-NVFP4-MTP (the MTP build), replacing mmangkad/Qwen3.6-27B-NVFP4 (issue #26 follow-up). The tool-calling gate that kept it a candidate is now closed: served through the production compose it emits a valid qwen3_coder tool call, completes a full tool round-trip, keeps its reasoning trace, and runs MTP spec-decode at 78.6% draft acceptance with tool calling on — ~2.4× single-stream decode (8 → ~19 tok/s), ~71 GB footprint, both model assess probes finish=stop. Promoted across the catalog (role_hint), the gateway default (_DEFAULT_PRIMARY), whoami, both template env.example/docker-compose.yml files, and culture.yaml.
• The MTP serve flags are now baked into the compose templates (single-model + fleet vllm-primary): --speculative-config, --trust-remote-code, --language-model-only, the --tokenizer=mmangkad/Qwen3.6-27B-NVFP4 override, and --max-num-seqs=2. A fresh model init && model serve of the default now works out of the box. Quantization default is modelopt.
• model switch notices inverted. Because the template ships the MTP primary's flags, switching to a non-MTP model now prints "REMOVE these 4 command: lines" (was "add" for the MTP candidate); the MoE --moe-backend add-notice is unchanged. Switching to the MTP primary force-caps --max-num-seqs to 2.
• mmangkad/Qwen3.6-27B-NVFP4 archived to a candidate — retained as the MTP primary's tokenizer source and the only vision-capable 27B in the catalog.

Fixed

• model switch --apply no longer takes a healthy deployment down when a manual compose edit is required (Qodo review). Switching to a non-MTP model (the template ships the MTP primary's incompatible flags) now writes .env and stops before the restart, printing the lines to remove; --force overrides to recreate the container anyway.
• MTP compose flags are a single source of truth (catalog.mtp_compose_command_items()) — consumed by both model switch's removal notice and guarded against drift from the packaged templates by a new test (Qodo review).
• Security guidance for the now-default --trust-remote-code added to both compose templates and env.example: HF_TOKEN is only needed for gated repos (defaults are public) — leave it empty or use a minimal-scope read-only token, and pin trusted revisions (Qodo review). Tracking the upstream tokenizer fix that would let us drop the override in #29.

[0.14.0] - 2026-05-31

Added

• MTP (Multi-Token Prediction) candidate for the 27B (issue #26). New catalog entry sakamakismile/Qwen3.6-27B-Text-NVFP4-MTP — a text-only re-export of the 27B primary with its MTP draft head restored in bf16 so vLLM speculative decoding actually works. The lesson from the 35B MoE applied: the baseline NVFP4 export drops the MTP head (~0 % draft acceptance), and a newer vLLM isn't installable on the aarch64 GB10 — so the fix is a checkpoint that ships the MTP weights, not a newer engine. Carries a catalog speculative_config ({"method":"qwen3_5_mtp","num_speculative_tokens":3}); quantization is modelopt. Load-tested on the DGX Spark (GB10) 2026-05-31: 19.1 tok/s decode (~2.4× the baseline 27B's ~8 tok/s) at 72 % MTP draft acceptance on vLLM 0.19.0+nv26.04 — the open risk (does the stock image accept qwen3_5_mtp?) is cleared (it resolves the Qwen3_5MTP draft head). One tokenizer override is required: the checkpoint declares the newer TokenizersBackend class (absent from nv26.04), so serve with --tokenizer=mmangkad/Qwen3.6-27B-NVFP4 (the cached sibling, same vocab); model switch prints it.
  • New per-model doc docs/qwen3.6-27b-text-nvfp4-mtp.md with the serve recipe, the live benchmark table (decode tok/s + acceptance vs the baseline), and the caveats (--max-num-seqs 2 or it silently OOMs; the tokenizer override).

Changed

• model switch surfaces MTP serve-extras, not just MoE. _moe_notice → _serve_notices (now a list): a model with a catalog speculative_config prints the exact --speculative-config / --trust-remote-code / --language-model-only compose edits (+ the VLLM_MAX_NUM_SEQS=2 reminder), the same hand-edit pattern as --moe-backend. The --json dry-run replaces the moe_notice key with a compose_edits list. env.example + the explain catalog prose updated to match.

Fixed

[0.13.0] - 2026-05-31

Added

• Workload purpose + machine tuning profiles. model switch now resolves the serve config from three layers — a machine profile (--machine, default auto-detected from nvidia-smi + hostname: GPU-memory fraction, context, attention backend), a workload profile (--purpose, default balanced: the batching knobs and the shape model benchmark exercises), and the model's catalog entry — with explicit --max-model-len / --gpu-mem-util flags overriding the machine defaults.
  • New model_gear/profiles.py (pure data module, like catalog.py): WorkloadProfile (balanced ≈1K/1K, prompt-heavy ≈8K/1K, decode-heavy ≈1K/8K) and MachineProfile (spark load-tested, thor/blackwell/generic configured), guarded by tests/test_profiles.py.
  • Richer single-model template — the serve command now passes --attention-backend, --max-num-seqs, --max-num-batched-tokens (env-driven), plus static --enable-chunked-prefill / --async-scheduling. New .env keys: VLLM_PURPOSE, VLLM_MACHINE, VLLM_ATTENTION_BACKEND, VLLM_MAX_NUM_SEQS, VLLM_MAX_NUM_BATCHED_TOKENS.
  • Per-model MoE serve extras — the catalog gains moe_backend / speculative_config (set only on the Qwen3.6-35B-A3B MoE candidate). model switch to the MoE prints them as a documented compose edit (they break the dense/hybrid models and can't be defaulted in the shared template).
  • model benchmark is tied to the config — its workload shape defaults to the configured VLLM_PURPOSE (overridable with --purpose / --input-len / --output-len).
  • model whoami / model overview surface the active gear (purpose/machine); model explain tuning documents the layering; docs/tuning-profiles.md is new.
  • Credit: the serve tuning and the three workload shapes follow shahizat's cross-machine NVFP4 benchmark (NVIDIA Developer Forums) — see the README Acknowledgements and docs/tuning-profiles.md.
  • Live-replicated on the shared DGX Spark (2026-05-31) rather than trusting the post: with the new flags the 35B MoE candidate loads solo (util 0.70, marlin) and runs single-stream decode ~35 tok/s vs the 27B's ~7.8 — ~4.6× faster (the MoE's ~3B-active advantage). Numbers + method in docs/tuning-profiles.md and docs/qwen3.6-35b-a3b-nvfp4.md.

Changed

• model switch --max-model-len / --gpu-mem-util now default to the machine profile (was a fixed 32768 / 0.6); pass them explicitly to override.
• model benchmark replaces --decode-tokens with purpose-driven --input-len / --output-len.
• Catalog: dropped the MTP speculative_config from the mmangkad/Qwen3.6-35B-A3B-NVFP4 entry (kept --moe-backend=marlin). Live testing showed shahizat's MTP draft fails to load on the mmangkad/ copy (qwen3_5_mtp.py weight-shape mismatch on vLLM nv26.04) — it is tied to his nvidia/ checkpoint. model switch no longer prints a recipe that wouldn't load.

Fixed

[0.12.0] - 2026-05-30

Added

• Audio I/O behind the gateway (STT + TTS) — issue #18, part 1 of 3. model-gear now serves OpenAI-compatible POST /v1/audio/transcriptions and POST /v1/audio/speech on the same host port as the text API, fronted by the same stdlib gateway. The audio backends are the same models the standalone realtime-api stack ran — NVIDIA Parakeet STT + Magpie TTS NIM — consolidated into the fleet (no separate compose project; the realtime bridge's LLM is the fleet gateway itself, so there is no extra vLLM container).
  • New [realtime] extra + model_gear.realtime package (vendored from the realtime-api sibling, cite-don't-import): a FastAPI bridge that exposes the OpenAI audio surface (/v1/audio/speech adapts Magpie's proprietary /v1/audio/synthesize; /v1/audio/transcriptions forwards to Parakeet). The base wheel and the gateway stay stdlib-only — torch/fastapi never leak into them.
  • Gateway audio routing — /v1/audio/* is path-routed to the audio backend (AUDIO_URL) with no model rewrite and no failover; binary responses relayed streamed (chunked) so a large TTS body never buffers whole in the gateway. Unset AUDIO_URL (a text-only fleet) → those paths 404, unchanged.
  • model init --fleet --audio scaffolds the audio overlay (docker-compose.audio.yml + Dockerfile.realtime + a vendored Dockerfile.parakeet/listen_server.py) and appends the audio keys to .env. model fleet up/down/status auto-include the overlay when present.
  • Co-residence caveat: the audio services share the GPU with the LLM fleet — the overlay is opt-in so text-only boxes keep their GPU budget. See the per-model docs (PR3) for live numbers.
  • The realtime WebSocket (/v1/realtime) and the model overview/doctor/explain surface land in the follow-up PRs (parts 2 and 3).

Changed

Fixed

• Audio review hardening (PR #24 review).
  • Gateway no longer buffers whole audio bodies — /v1/audio/* responses are relayed chunked instead of read_all()'d into memory, so one large TTS WAV can't OOM the fleet's single front door.
  • TTS_CONCURRENCY / TTS_SPEED clamped to ≥ 1 — TTS_CONCURRENCY=0 previously seeded an asyncio.Semaphore(0) that hung every TTS request; a 0/negative speed emitted nonsensical rate="0%" SSML.
  • /v1/audio/speech speed clamped to OpenAI's 0.25–4.0 range before the Magpie percentage conversion, so out-of-range values no longer reach the backend as rate="{huge|negative}%" and 502.
  • SonarCloud config — coverage exclusions now mirror coverage.run omit (the [realtime]-extra modules can't be unit-imported offline), and the deployment scaffolds under model_gear/templates/** are excluded from analysis (container Dockerfiles + the vendored Parakeet server aren't package runtime). Added unit tests for realtime.protocol, the settings clamps, the speed clamp, and the streamed audio relay.

[0.11.1] - 2026-05-30

Added

• model learn --json now includes a models object (supported_catalog / loaded_now) — a machine-readable version of the catalog-vs-loaded explainer for agent consumers. (Additive field; the only observable behavior change in this release.)

Changed

• Documented "supported catalog vs. loaded now" consistently across the README, docs/gateway-fleet.md (new "Supported catalog vs. warm backends" subsection), the per-model docs, and the CLI teaching surfaces (model learn, model explain models/overview/status/whoami/root, and the overview/status/whoami/fleet status help strings). The distinction: model overview --list / GET /v1/models/supported = the gears you can switch to (tagged load-tested/configured, static); the live GET /v1/models (which model fleet status queries) = what's actually loaded now. model status / model whoami report the configured served model (from .env) + health — not a live /v1/models query. Docs + help text (no serving/runtime behavior change).

[0.11.0] - 2026-05-30

Added

• RedHatAI/Mistral-Small-3.2-24B-Instruct-2506-NVFP4 support — added to the supported-model catalog (model overview --list, GET /v1/models/supported) with a per-model doc, docs/mistral-small-3.2-24b-nvfp4.md. Load-tested on the DGX Spark (GB10): ~15 GiB weights, ~14.9 tok/s decode, prefill 2,009 tok in 1.49 s, tool calling ✅.
• mistral tool-call parser inference — model_gear.runtime._parser now maps Mistral-family ids (incl. the mistralai/ org) to the mistral parser; model switch auto-selects it.
• model switch --quantization — the served --quantization is now set per model (read from the catalog for a known model, e.g. compressed-tensors for the RedHatAI NVFP4 Mistral vs modelopt_fp4 for the nvidia/mmangkad checkpoints); --quantization overrides it. The single-model compose reads VLLM_QUANTIZATION.

Changed

• Fleet default fallback is now the dense Mistral-Small-3.2-24B, replacing the mmangkad/Qwen3.6-35B-A3B-NVFP4 MoE, which never loaded on the GB10 (OOM co-resident, stall solo — no benchmark obtained). Mistral is dense, loads reliably, and is smaller (~15 GiB weights). The fleet compose serves it with the mistral tokenizer + images limited to 0 (required for tool-call parsing on the nv26.04 build; the HF tokenizer leaks [TOOL_CALLS] markup, and the mistral tokenizer alone crashes the Pixtral profiler) and no --reasoning-parser (instruct model). The 35B MoE is demoted to a catalogue candidate.
• model_gear.gateway._config._DEFAULT_FALLBACK, the fleet docker-compose.yml / env.example FALLBACK_* defaults, docs/gateway-fleet.md, and README.md updated for the new fallback.

Fixed

[0.10.1] - 2026-05-30

Changed

• Fleet default GPU-mem utilisations rebalanced 0.55/0.30 → 0.40/0.35. Live validation on a DGX Spark (GB10) showed 0.55/0.30 OOM-crash-loops the fallback: the 27B primary alone takes ~75 GiB at util 0.6, and --gpu-memory-utilization is fraction-of-total per process (the two backends don't coordinate). The new values are a dedicated-box estimate; the templates and docs now state plainly that co-residence of two ~30B models needs a dedicated box.

Fixed

• Docs corrected against live findings (2026-05-30): docs/gateway-fleet.md gains a "Live validation findings" section (27B warm-up ~7 min, ~75 GiB footprint, 8.0 tok/s decode; co-residence not viable on a shared GB10). docs/qwen3.6-35b-a3b-nvfp4.md updated from "not yet load-tested" to the actual result — the MoE fallback does not load reliably on this box (OOM co-resident; crash/stall even solo). docs/qwen3.6-27b-nvfp4.md reframed as the fleet default primary (was "candidate") with the warm-up measurement and a corrected recommendation.

[0.10.0] - 2026-05-30

Added

• GET /v1/models/supported gateway endpoint — the "change gears" catalog. Alongside the OpenAI-standard /v1/models (which lists only the two loaded backends), the gateway now serves the full catalog of supported models a client can change gears to, each flagged loaded (a backend serves it now) and default (the gateway routes unknown/missing names there). Non-OpenAI shape ("object": "model-gear.supported_models") so /v1/models stays standard for existing clients. Pure supported_models_payload() in gateway/_routing.py.
• New packaged catalog model_gear/catalog.py — a dependency-free SUPPORTED_MODELS tuple (the 27B primary, the 32B dense candidate, the 35B-A3B MoE fallback) that is the single source of truth for both the gateway (which runs from a wheel and can't read docs/) and the CLI. model overview --list is now catalog-backed, so it is populated even in a wheel install.

Changed

• Fleet (and single-model) default primary → mmangkad/Qwen3.6-27B-NVFP4. The scaffolded default served model is now the Qwen3.6 27B (hybrid Mamba/linear-attn + ViT, 256K native context) with --tool-call-parser=qwen3_coder — matching what runs on the DGX Spark and convertible's parent model. The dense nvidia/Qwen3-32B-NVFP4 remains a supported candidate (PRIMARY_MODEL / model switch). Recomputed co-resident GPU memory: PRIMARY_GPU_MEM_UTIL=0.55 and FALLBACK_GPU_MEM_UTIL=0.30 (the 27B is heavier than the 32B). Updated the fleet + single-model templates, gateway/_config.py, whoami default, culture.yaml / AGENTS.md / CLAUDE.md (served-model coherence chain), and the per-model + gateway-fleet docs.

Fixed

[0.9.0] - 2026-05-28

Added

• Fallback model + single front OpenAI gateway ("fleet"). A new scaffold-based deployment runs two always-warm vLLM backends behind one stdlib gateway that model-gear manages as three containers (model-gear-gateway, model-gear-vllm-primary, model-gear-vllm-fallback). The gateway routes each request by its model field, defaults an unknown/missing name to the primary, and fails over to the other backend when the chosen one refuses the connection or returns a 5xx before the response body (4xx is returned verbatim; no mid-stream retry). SSE streams are relayed chunk-by-chunk. Default fallback: the MoE mmangkad/Qwen3.6-35B-A3B-NVFP4.
• New gateway package model_gear/gateway/ — a pure-stdlib (http.server + http.client, no runtime deps) reverse proxy: _routing.py (pure name/alias/default routing + failover ordering), _config.py (env → routing table + server config), server.py (the handle_post failover seam, upstream client, and ThreadingHTTPServer handler), run as python -m model_gear.gateway.
• model init --fleet scaffolds the fleet templates (docker-compose.yml + .env + Dockerfile.gateway) and pins MODEL_GEAR_VERSION to the running release; model fleet up | down | status drives the deployment (up/down dry-run by default, --apply to commit; status is read-only and reports all three containers + the gateway /health + /v1/models).
• Docs: docs/gateway-fleet.md (topology, routing/failover, memory, verbs), docs/qwen3.6-35b-a3b-nvfp4.md (the MoE fallback), a README "fleet" section, and model explain fleet / model explain gateway entries.

Changed

• model_gear/runtime/_compose.py gained a template registry (SINGLE_TEMPLATES / FLEET_TEMPLATES), a templates= argument on scaffold_plan / write_scaffold (single-model stays the default — existing callers unchanged), a compose_up_build helper, and FLEET_CONTAINERS.
• The fleet .env mirrors VLLM_MODEL / VLLM_SERVED_NAME / VLLM_TOOL_CALL_PARSER (= the primary) so the read-only single-model verbs (status / whoami / doctor) stay coherent on a fleet deployment. model switch remains single-model only.

Fixed

[0.8.1] - 2026-05-27

Changed

• SonarCloud cleanup (no behavior change). Split cmd_switch into _select_parser / _emit_dry_run / _apply_switch helpers to bring its cognitive complexity under the gate, and hoisted the repeated "(unset)" literal in model status into a _UNSET constant.

[0.8.0] - 2026-05-27

Added

• Per-model tool-call parser auto-selection. New model_gear/runtime/_parser.py infer_parser() maps a model name to its parser (qwen3_coder for Qwen3-Coder / Qwen3.6, hermes for Qwen3 dense, unknown → leave untouched). model switch now picks the right parser automatically so tool calling keeps working across a switch without the caller remembering it; --tool-call-parser still overrides (issue #13).
• Post-switch / post-start tool-calling probe. model switch --apply and model serve --apply now probe tool_choice:"auto" once the container is healthy and report PASS/FAIL (with the called tool names) — reusing the existing assess probe. --no-probe skips it; the probe never aborts the command (unreachable / HTTP 400 degrade to a FAIL result).
• model status reports the active tool_call_parser (VLLM_TOOL_CALL_PARSER), so "which gear am I in" is complete without docker inspect.

Changed

• lepenseur is retired; the deployed agent is now model-gear. The tool and the deployed agent share one identity. Updated culture.yaml (suffix: model-gear), the AGENTS.md system prompt, model whoami / learn / explain output, the posting nick (.claude/skills.local.yaml.example), the compose/.env templates, README.md, and CLAUDE.md (the former "two identities" section now describes one).

Fixed

[0.7.0] - 2026-05-27

Added

• OpenAI tool/function calling on the served vLLM model. The packaged compose template (model_gear/templates/docker-compose.yml) now serves with --enable-auto-tool-choice and --tool-call-parser=${VLLM_TOOL_CALL_PARSER:-hermes}, so tool_choice:"auto" requests return a tool_calls array instead of HTTP 400. Additive — plain chat/reasoning is unaffected, no extra GPU/memory cost. Unblocks coder-agent harnesses that drive the model entirely through tool calls (issue #9).
• VLLM_TOOL_CALL_PARSER env var (default hermes) + model switch --tool-call-parser — the parser is per-model: hermes fits Qwen3 dense (e.g. Qwen3-32B), while Qwen3-Coder / Qwen3.6 checkpoints emit the XML function format and need qwen3_coder. switch writes the var only when the flag is given, so retuning a model never clobbers its parser.
• model assess --tools — an opt-in tool-calling probe that verifies a tool_choice:"auto" request returns a tool_calls array naming a finish function. Degrades gracefully (a FAIL row, no abort) against a server that lacks the flags.

Changed

Fixed

[0.6.0] - 2026-05-27

Added

• devague workflow trio vendored under .claude/skills/ (cite-don't-import): think (idea→spec), spec-to-plan (spec→plan), and assign-to-workforce (plan→parallel implementation) — the operator chain for the deterministic devague CLI. Authored in agentculture/devague, vendored via guildmaster; each carries type: command (load-bearing on the culture/agex backend, where a SKILL.md without type: is silently skipped). They drive the devague CLI at runtime (uv tool install devague), resolved portably by the wrappers.
• docs/skill-sources.md — provenance ledger recording the citation path and authoring origin of every vendored skill (the trio plus the six steward-sourced skills).

[0.5.0] - 2026-05-27

Redesigned the repo around running, assessing, and switching the local vLLM model. The model-ops logic that lived in the model-runner skill is now a first-class CLI. lepenseur is still the deployed agent that consumes the served model; model-gear is the tool that runs it.

Added

• Model-ops verbs on the model CLI: switch <model>, serve (alias start) / stop, status, assess (correctness probes), benchmark (decode throughput + prefill), and init (scaffold a deployment dir). Write verbs (switch/serve/stop/init) are dry-run by default and require --apply (mutation-safety rule).
• Scaffold-based deployment. docker-compose.yml + env.example ship as packaged templates under model_gear/templates/; model init materialises them into ~/.model-gear (default), a TARGET, or the local folder. Every model-ops verb resolves the deployment dir via --compose-dir → $MODEL_GEAR_DIR → ~/.model-gear.
• Ported runtime modules (model_gear/runtime/ + model_gear/assess.py), stdlib-only (urllib, fixed-argv subprocess), with full unit tests.
• model overview now folds in the currently-served model and the candidate-model list, filterable with --current / --list.

Changed

• PyPI distribution renamed lepenseur → model-gear; binary lepenseur → model; Python package lepenseur → model_gear. Error class LepenseurError → ModelGearError. The lepenseur console script is removed.
• Agent-first verbs reframed for the tool: whoami reports tool/machine/served model/container health/agent; learn teaches the model-ops surface; explain catalog rewritten (switch/assess/backend/models/…).
• doctor is now real — checks docker availability, deployment scaffold, .env ↔ culture.yaml coherence, and /health reachability (a down model is a warning, not a failure).
• The model-runner skill is now a thin shim that execs model; its _assess.py was removed (the logic lives in model_gear/assess.py).
• AGENTS.md / culture.yaml clarified: they describe the deployed lepenseur agent, not the repo. README + CLAUDE.md reoriented around model-gear.

Fixed

• BREAKING: the vLLM container is renamed lepenseur-vllm → model-gear-vllm. A box running the old container must docker compose down under the old name, then model init --apply + model serve --apply.

[0.4.0] - 2026-05-27

Added

• model-runner skill (local, not vendored): switch the local vLLM runtime model and assess/benchmark it (stdlib _assess.py for correctness + throughput, host-side facts via the wrapper). Drives this repo's compose + .env; documented in CLAUDE.md and README. Mutating verbs (switch, down) are dry-run by default and require --apply (CLAUDE.md mutation-safety rule); --port defaults to .env's VLLM_PORT (then 8000).

Changed

• docs/qwen3.6-27b-nvfp4.md: filled with the live load-test (DGX Spark/GB10, 2026-05-27). mmangkad/Qwen3.6-27B-NVFP4 loads and serves under our vLLM image (no --trust-remote-code); ~7.9–8.0 tok/s decode, ~70 GB reserved, 29 GB weights. It is a hybrid Mamba/linear-attention vision-language model and is slower on decode than the 32B here — recommendation: keep the 32B. All pre-flight caveats (SGLang-only, multimodal, ModelOpt rc) validated/resolved.

[0.3.0] - 2026-05-27

Added

• docs/qwen3-32b-nvfp4.md: per-model doc for the current runtime model, with a live test on DGX Spark (GB10) — nvcr.io/nvidia/vllm:26.04-py3 (engine 0.19.0+...nv26.04), ~9.7 tok/s decode (batch=1), ~2,800 tok/s prefill, ~72 GB reserved at gpu-memory-utilization=0.6, correctness verified.
• docs/qwen3.6-27b-nvfp4.md: per-model doc for candidate mmangkad/Qwen3.6-27B-NVFP4. Its Qwen3_5ForConditionalGeneration arch is registered in the current vLLM image (so the same compose can serve it); live load-test/benchmark tracked by issue #6.
• README "Per-model notes" linking both docs.

Fixed

• docker-compose.yml: corrected the --reasoning-parser=qwen3 comment — on the nv26.04 build the <think> trace is returned in the reasoning field, not reasoning_content.

[0.2.0] - 2026-05-27

Added

• docker-compose.yml + .env.example: a local vLLM server (NGC nvcr.io/nvidia/vllm image) that serves the runtime model as an OpenAI-compatible API on :8000 for the acp backend, tuned for DGX Spark (GB10 Blackwell, 128 GB unified memory).
• README "Running the model locally (vLLM)" section.

Changed

• Switched lepenseur's runtime model from nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4 to nvidia/Qwen3-32B-NVFP4 across culture.yaml, AGENTS.md, lepenseur/explain/catalog.py, README.md, and CLAUDE.md (32B dense NVFP4 reasoning model with a thinking mode).

[0.1.0] - 2026-05-22

Added

• Initial CLI/PyPI sibling scaffold (copied and adapted from the lecodeur twin): top-level lepenseur package with the lepenseur console script.
• Read-only verbs: whoami, learn, explain, overview, and a cli noun with cli overview.
• doctor verb shipped as a rubric-shaped stub; real self-diagnosis semantics for a thinking ("non-doer") agent are deferred to a follow-up.
• Runtime identity files: AGENTS.md and culture.yaml (acp backend, vllm-local/nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4).
• CI: tests.yml (test + lint + afi cli doctor . --strict gate + version-check) and publish.yml (PyPI/TestPyPI via Trusted Publishing).
• Six vendored skills under .claude/skills/ (cicd, communicate, version-bump, run-tests, sonarclaude, doc-test-alignment), provenance: steward.