- 28 Jul, 2026 4 commits
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Stefy Lanza (nextime / spora ) authoredhf leaves a stale <sha>.<etag>.incomplete orphan in blobs/ after a resumed or retried chunk even though the real file finished — so "download complete" and the model page disagreed (settings said done, the page showed
⚠ incomplete). - model list: an .incomplete file now counts as an unfinished download ONLY when its final blob (<sha>) is absent; an orphan whose final blob exists is ignored. Fixes the false badge for ALL HF models, not just colibri. - colibri catalog: _colibri_repo_complete now tests for dangling snapshot symlinks (the true "file missing" signal) instead of .incomplete presence, so the settings "present" indicator matches reality. - colibri catalog label → the real size (~429 GB / 400 GiB). Co-Authored-By:Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01LoSpEthysqmseCc6Geizty
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Stefy Lanza (nextime / spora ) authored
The GLM-5.2 model is a DIRECTORY (an HF repo of int4 .safetensors shards + config + tokenizer/MTP head), not a single file/GGUF — so it downloads via the normal whole-repo snapshot and lives in the HF cache snapshot dir. - routes: _COLIBRI_DEFAULT_MODELS catalog (mastouri/GLM-5.2-colibri-int4-g64-with- int8-mtp) + GET /admin/api/colibri/default-models; extend /admin/api/model-add to accept `backend` and `alias` so a downloaded repo registers as a colibri-backed model in one step. - settings.html: a "Download the GLM-5.2 container" picker + button in the colibri card — full-repo snapshot (no file_pattern → a directory), then registers it as a colibri model addressable by the configured model id. - backends/colibri: _resolve_container resolves an HF repo id → its local snapshot DIRECTORY (snapshot_download local_files_only), so a normally-downloaded model serves without any manual path wiring. - models.html: add "colibri" to the per-model backend dropdown (manual add path). - app: allow the new catalog endpoint through the no-auth admin allowlist. Co-Authored-By:
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Stefy Lanza (nextime / spora ) authored
- parser: add GLMParser + parse_glm_tool_calls/strip_glm_tool_calls for GLM-5.2's <tool_call>name<arg_key>k</arg_key><arg_value>v</arg_value>...</tool_call> format (byte-compatible with colibri's own parser). Gated on the <arg_key> marker so a generic <tool_call>{json} from other families is never hijacked. Declared-type coercion keeps string args verbatim (no "12345"->int). Unclosed-box recovery for budget-truncated calls. Wired into family selection ('glm'/'colibri'), the model-agnostic ToolCallParser path, and both strip_tool_calls_from_content paths. - manager: colibri no longer seizes the whole GPU like ds4. It pins only a configurable expert tier (CUDA_EXPERT_GB) and streams the rest, so it coexists and is evicted like any other model. VRAM footprint estimated from cuda_expert_gb (+overhead) until measured. Verified: typed/untyped parse, strip, unclosed recovery, non-GLM gating, and the by-name dispatcher all correct. Co-Authored-By: -
Stefy Lanza (nextime / spora ) authored
Add JustVugg/colibri as a managed engine, mirroring the ds4 integration but driving the pure-C `colibri` binary DIRECTLY over its stdin/stdout mux protocol (docs/serve_protocol.md) instead of proxying to a Python gateway — coderai reproduces openai_server.py's engine client and GLM-5.2 chat template itself. - config: ColibriConfig (disabled by default; model is a directory container, not a GGUF — routes by model_id/alias/name, no arch sniff) - codai/api/colibri_worker.py: clone+build (make colibri CUDA=1), MuxEngine protocol client (READY handshake, SUBMIT/DATA/DONE, KV-slot pool, CANCEL, stderr log pump), per-container engine registry - codai/backends/colibri.py: ColibriBackend + render_chat (byte-exact port of colibri's GLM-5.2 template; the engine tokenizes what we send) - front routing: `colibri` capability on nvidia/cuda/auto; router/assignment/ app/engine_supervisor thread config.colibri - manager: colibri_should_handle, backend selection, /v1/models surfacing, exclusive-VRAM eviction (wants the whole GPU like ds4) - admin: config get/set + per-model overrides; settings.html card + models.html row - packaging: build.sh --colibri, OCI bundle (repo+binary, no 372GB model), entrypoint seed, CODERAI_COLIBRI_DIR, smoke test Ships OFF: no routing changes until colibri.enabled + colibri.model_path are set. Verified offline: config round-trip, routing predicate, render_chat byte-match vs colibri's own, MuxEngine end-to-end against a fake engine, CPU engine builds. Co-Authored-By:
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- 26 Jul, 2026 3 commits
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Stefy Lanza (nextime / spora ) authored
Second VPR option for REQUEST-vpr-embeddings.md, alongside the in-process EigenPlaces. SALAD is near-SOTA for place recognition but its deps (pytorch_lightning, pytorch_metric_learning) are heavy and version-sensitive, so it runs in a SEPARATE process with those deps quarantined in a --system-site-packages venv (shares the main torch, adds only the extras). The main engine venv never imports them — verified clean. - codai/api/vpr_salad_server.py: stdin image-path -> stdout JSON embedding server (same protocol as dinov2-embed), GPU-preferred with CPU fallback, announces its true width (8448) on ready. - embeddings.py: 'salad'/'dinov2-salad' -> 'vpr-server' backend that spawns the server via /cache/salad-venv/bin/python; reuses the dinov2cpp reader/cleanup. - Self-healing: _EmbeddingModel gains a respawn hook. If the manager kills the child under VRAM pressure but keeps the cached model, the embed path now respawns the server in place instead of failing forever with a stale handle (this was causing persistent "process died" 500s once salad was evicted). - packaging/setup-salad-venv.sh: idempotent creator for the isolated venv. Both VPR models pass the decisive ordering test on same-place/different-place image sets (zero overlap between same-place min and different-place max); SALAD's margin (0.516 vs 0.150) is wider than EigenPlaces' (0.479 vs 0.349). Verified: 8448-d L2=1.0, deterministic, ~0.2s warm / ~8s cold, GPU, subprocess isolation intact. Config: models.json 'salad' + alias 'dinov2-salad', engine nvidia. Co-Authored-By:
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Stefy Lanza (nextime / spora ) authored
Implements REQUEST-vpr-embeddings.md: a visual place recognition model that turns a photo into ONE L2-normalised descriptor trained so two images of the SAME place land close — the building-identity discrimination dinov2/gme/geoclip lack (they rate similar-looking houses as matches). HomeHunter uses it to match listing exteriors against Street View panoramas. Backed by EigenPlaces (gmberton, ResNet50, 2048-d) loaded via torch.hub. Chosen over SALAD/MixVPR for a clean dependency footprint: torch + torchvision only (no pytorch_lightning). ImageNet-normalised, 512x512 eval transform (matches training crops); output L2-normalised (idempotent — the net already ends in an L2 layer). On GPU (nvidia engine device); CPU only as fallback. The image data URI arrives in `input` (per the contract) or the `image` field. torch.hub cache pinned to a persistent TORCH_HOME (/cache/torchhub) with the repo + weights + trusted_list pre-seeded, so the engine loads offline and non-interactively (a cold torch.hub.load would otherwise hit an interactive trust prompt that EOF-crashes a server). Verified: 2048-d L2=1.0, deterministic, one vector/image; and the decisive ordering test — same-place pairs (min cos 0.479) rank strictly above every different-place pair (max 0.349), zero overlap. Config: models.json 'eigenplaces' + alias 'vpr', engine nvidia. SALAD (pytorch_lightning, subprocess-isolated) to follow as a second, higher-accuracy option. Co-Authored-By:
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Stefy Lanza (nextime / spora ) authored
Implements the HomeHunter request (REQUEST-geoclip-embeddings.md): two model ids on the existing embeddings endpoint, both returning 512-d L2-normalised vectors in ONE shared space so a property photo can be scored directly against candidate GPS coordinates. geoclip — image encoder (CLIP ViT-L/14 + projection MLP), on GPU. Accepts the image data URI in EITHER `input` (per the request) or the `image` field (like dinov2). geoclip-location — GPS encoder (equal-earth + RFF MLP), pinned to CPU: it is a tiny MLP, so CPU is fast, frees VRAM, and is bit-exact across batch sizes (GPU reduction order otherwise perturbs a cached coordinate by ~1e-7). Batches an array of "lat,lon" strings to one vector per element, in input order. Both sides load from the SAME bundled GeoCLIP checkpoint, so the spaces coincide by construction — the mismatched-checkpoint failure the request warns about cannot happen. Loaded as separate _EmbeddingModel('geoclip', ...) instances so each is independently evictable. transformers>=5 compat: CLIPModel.get_image_features now returns a BaseModelOutputWithPooling (768-d projected features in pooler_output) instead of a bare tensor, which breaks GeoCLIP's internal mlp() call. The image path extracts the 768-d tensor itself (tolerant of both the old tensor and the new object) before the projection MLP. geoclip (+geopy, geographiclib) added to requirements.txt; its heavy deps (torch, transformers, Pillow, pandas, numpy) are already pinned, so no ML-stack churn. Verified end-to-end: 512-d L2-normalised both sides; batching + index order; same request bit-exact; both ids in GET /v1/models; image·location scoring varies by image. Co-Authored-By:
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- 24 Jul, 2026 17 commits
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Stefy Lanza (nextime / spora ) authored
The nvidia container runtime only injects the NVIDIA Vulkan ICD when the 'graphics' driver capability is requested. --nvidia ran with the runtime default (compute,utility), so inside the container Vulkan could see only the AMD card (RADV) and llvmpipe — never the NVIDIA GPU. A Vulkan-only GGUF embedder (dinov2-embed, built with GGML_VULKAN) therefore could not run on the NVIDIA card at all; it was pinned to the AMD RX 580 or forced to CPU. Add `-e NVIDIA_DRIVER_CAPABILITIES=all` (honouring any caller-exported value) to the docker --nvidia args so the NVIDIA Vulkan ICD is injected and Vulkan enumerates the NVIDIA GPU. Enables running the GGUF dinov2 embedder on the 3090 via Vulkan under the nvidia-gguf engine (device pinned via GGML_VK_VISIBLE_DEVICES / VK_ICD_FILENAMES; the build hardcodes ggml_backend_vk_init(0)). Takes effect on the next container (re)launch. Co-Authored-By:
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Stefy Lanza (nextime / spora ) authored
POST /v1/images/generations for a masked image transformer (Z-Image) crashed with "`attn_mask` is not supported for flash-attn 2." → HTTP 500. The diffusers "active attention backend" is PROCESS-WIDE global mutable state (_AttentionBackendRegistry class attribute) shared by the image and video paths. The video path sets it to flash-attn-2. A transformer whose per-module backend is None reads that global at dispatch time; flash-attn-2 rejects attn_mask, so Z-Image (which uses caption masks) crashes. The previous fix reset the global to native before generating, but that reset is NOT atomic with the pipeline() call (it runs in a to_thread worker), so a concurrent/subsequent video generation races and flips the shared global back to flash before image attention dispatches. Fix: pin the image denoiser's per-module attention backend to native (SDPA, mask-supporting) via set_attention_backend("native"). The dispatcher passes processor._attention_backend explicitly, bypassing the shared global — so image attention is immune to the video path flipping it. Video is undisturbed (it sets its own explicit per-module 'flash'). Image pipelines were never intentionally on flash, so this is non-regressive. Global reset kept as a fallback. Verified: unsloth/Z-Image-Turbo-unsloth-bnb-4bit now returns 200 with an image; no "attn_mask is not supported" in the log. Co-Authored-By: -
Stefy Lanza (nextime / spora ) authored
POST /v1/embeddings to the GME-Qwen2-VL GGUF embedder returned HTTP 500 for large photos. The qwen2-vl vision tower turns a big image into >n_ctx image tokens (observed batches of 1012–2048), and mtmd's decode then fails — "decode: failed to find a memory slot for batch of size N" / "failed to eval chunk 1" — because the image tokens plus the chat-prompt frame exceed the model's KV context. Small images (few hundred tokens) were unaffected. mtmd's own image_max_tokens budget is NOT honoured by this llama.cpp build (2048-token batches slipped through), so add a hard, Python-side backstop: _resize_image_to_token_budget() downscales any oversize PIL image (aspect preserved, 28px patches) to at most n_ctx-128 tokens BEFORE it reaches mtmd, so a request can never overflow the context regardless of input size. The budget derives from the live n_ctx, so it self-adjusts to any model config. Pairs with raising the GME model's n_ctx (instance models.json) so a full-size image (~2048 tokens) fits comfortably; n_batch/n_ubatch follow n_ctx in the loader. Verified on the radeon Vulkan embedder: 336² / 896² / 1400² / 1680² images all return 200 (the 1400²/1680² are logged resizing to ~2400 tokens to fit the 2432 budget); previously the ≥1024-token cases 500'd. Co-Authored-By:
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Stefy Lanza (nextime / spora ) authored
On a global CPU cooldown the thermal supervisor already pauses ALL engines (want_pause = gpu_hot or cpu_hot, applied per-engine), and the logs confirm every engine gets a pause. But the Tasks page often showed only ONE engine as cooling while the others looked like they were still running. Cause: two threads write engine.cooling. The thermal loop sets it when it pauses an engine; the health poll loop overwrites it every tick with the engine's OWN self-reported cooldown (cooling=d.get("cooling")). An engine only self-reports cooling while sitting in its own wait_until_safe loop (it has an in-flight request). An engine the front paused while IDLE reports cooling=None, so the poll loop cleared the front's pause indicator — the UI then showed that engine as running mid-cooldown. Fix: while the front holds an engine paused (engine.therm_paused), pass the update_state sentinel (cooling=False, "don't touch") instead of the engine's self-report, so the front's indicator survives. Once the front resumes the engine, the engine's self-report flows through again as before. Co-Authored-By: -
Stefy Lanza (nextime / spora ) authored
A GGUF vision model (e.g. Gemma-4-14B) served correct image descriptions on its FIRST load after a restart but hallucinated an identical answer for every image on every subsequent load — the image was silently flattened to a "[image_url content]" text placeholder and the model never saw pixels. Root cause was a config-key mismatch that self-polluted the in-memory config. On-demand loads arrive as a basename (Gemma-...gguf) while the real models.json entry is keyed by full path. record_vram_delta() resolved the write target via _config_for_model_key(), which — unlike _config_for_model() — did NOT fall back to basename/alias matching, so it returned {} and then persisted a NEW basename-keyed entry holding ONLY the measured_* fields (no mmproj, no n_ctx). On the next load _config_for_model()'s exact-match hit that stripped basename entry FIRST, before the basename loop that would have found the real full-path config, so mmproj was dropped, supports_vision went False, and the vision projector never loaded. Fix: - Add _resolve_config_key(): returns the actual self.config key for a model (exact -> alias -> basename), the single canonical key readers and writers must agree on. - Route _config_for_model() through it; give _config_for_model_key() the same basename/alias fallback so it can no longer return {} for a basename. - record_vram_delta()/_persist() now read and persist measured fields under the canonical key, merging into the real config instead of spawning a stripped shadow entry. Verified: after restart the 14B loads with "mmproj ... (vision enabled)" on every reload and three distinct test images produce three distinct, accurate descriptions; the measured-VRAM writeback now logs "(force_vram_update)" (real config resolved) instead of the old "(no used_vram_gb)" (empty config). Co-Authored-By: -
Stefy Lanza (nextime / spora ) authored
Reworked engine_request_min_interval_ms from start-spacing to a proper post-completion gap: _rate_acquire holds a per-engine lock for the whole request, _rate_release frees it only `interval` ms AFTER completion (via loop.call_later, non-blocking) — so consecutive requests to the engine are ALWAYS separated by at least that idle GPU time regardless of request duration. Wired acquire/release into all 3 inference dispatch paths with release in every finally/early-return. _rate_acquire refreshes config on mtime change so a value saved in the web UI applies to the next request. Co-Authored-By:
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Stefy Lanza (nextime / spora ) authored
Adds a "Rate limit (ms)" column to the Settings per-engine overrides table for engine_request_min_interval_ms (0 = no limit). Backend GET returns the map, POST saves it via the int-override sanitizer. Co-Authored-By:
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Stefy Lanza (nextime / spora ) authored
New server.engine_request_min_interval_ms (engine name → ms, 0/unset = off). _rate_gate spaces inference dispatch STARTS to an engine by at least the interval, capping request rate and inserting idle time between GPU submissions — a stability lever for a marginal card (e.g. RX 580) that wedges under sustained back-to-back Vulkan compute. Wired into all three inference dispatch paths after the swap-gate; the request itself runs unthrottled, only the start cadence is gated. Co-Authored-By:
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Stefy Lanza (nextime / spora ) authored
New server.engine_env_overrides (engine name → {VAR: val}), merged into the engine's process env at spawn. Lets low-level driver knobs be set without hardcoding — set radeon → RADV_DEBUG=syncshaders to serialize RADV shader dispatch and test whether the Polaris compute-ring async race (ring comp_x.y.z timeout) is what wedges the RX 580 under sustained Vulkan compute. Co-Authored-By: -
Stefy Lanza (nextime / spora ) authored
At front startup _build_engines runs vulkaninfo to enumerate GPUs; on a wedged Polaris card that process blocks UNINTERRUPTIBLY (D-state), and subprocess.run(timeout=) can't kill a D-state child — so the front's uvicorn startup hung forever ("Waiting for application startup"), taking down the healthy NVIDIA engine too (502 everywhere). Add _call_with_timeout: runs the probe in a daemon thread and STOPS WAITING after a wall-clock timeout, leaving the un-killable call orphaned instead of blocking. Wrap vulkan_devices() (12s) so startup always completes, and _amd_stats() (6s) so a hung Radeon can't freeze the health/thermal poll threads that also monitor the healthy card. A wedged engine now degrades to "that engine down", not "everything down". Co-Authored-By: -
Stefy Lanza (nextime / spora ) authored
The per-engine concurrency-overrides table on the Settings page already exposes max_parallel_requests per engine; add a "GPU power lock" column (dpm level select) so dpm_force_performance_level_overrides is editable too. Backend: GET returns the map, POST saves it via a level-validating sanitizer. Takes effect on engine restart. Co-Authored-By:
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Stefy Lanza (nextime / spora ) authored
Devuan's udev rejects the 'bind' trigger action, so the udev-rule path can't reliably apply the DPM lock at boot. sysvinit's /etc/rc.local runs race-free after amdgpu binds — this root-owned helper, called from rc.local, sets power_dpm_force_performance_level=high on every AMD card by PCI vendor id. Co-Authored-By:
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Stefy Lanza (nextime / spora ) authored
An ATTR{}= assignment on the add event races the amdgpu probe (the power_dpm attribute doesn't exist yet), so it silently no-ops at boot (observed: level stayed 'auto'). Fire on the bind action — driver bound and sysfs attrs created — and write via RUN with %p devpath. Co-Authored-By: -
Stefy Lanza (nextime / spora ) authored
Devuan (sysvinit/OpenRC, no systemd) — a udev rule is the init-agnostic way to make power_dpm_force_performance_level=high stick. Matches by DRIVER==amdgpu so it survives DRM card renumbering across GPU resets. Complements the in-container best-effort apply (unprivileged, can't write root sysfs) shipped in 0.1.53. Co-Authored-By:
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Stefy Lanza (nextime / spora ) authored
New server.dpm_force_performance_level_overrides (engine name → level, e.g. {"radeon":"high"}). At engine startup the engine writes the level to every amdgpu card's power_dpm_force_performance_level — locking a Polaris/GCN card to fixed top clocks avoids the DPM power-state transitions that hang these cards under sustained Vulkan compute (current default is 'auto', the hang-prone mode). Card is matched by PCI vendor id (0x1002), robust to DRM card renumbering across resets. Best-effort: an unprivileged container logs the exact host command on PermissionError. Pairs with two config-only stability levers applied for the radeon: n_ctx reduction (qwen3 1024→512, gme 1536→1024) to keep all three embedders in real VRAM with headroom (no GTT-over-PCIe spill during compute — another Polaris hang trigger), and max_parallel_requests override {"radeon":1} to serialize Vulkan submissions. Co-Authored-By: -
Stefy Lanza (nextime / spora ) authored
A vision-capable GGUF model (gemma-4-26B + mmproj) replied "none" to real-estate photos because the client sends AVIF, and llama.cpp's mtmd chat handler decodes data-URIs with stb_image, which has no AVIF/WebP support — the model silently received no image. _normalize_vision_content now transcodes any non-stb format to PNG via PIL (which has libavif) before handing it to the handler. The radeon GME embedder was unaffected (it already decodes via PIL). Text-only models (gemma-2-9b, no mmproj) are untouched — the image is still flattened, since they have no vision tower. Co-Authored-By:
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Stefy Lanza (nextime / spora ) authored
The 0.1.50 guard only covered _read_gpu_temp_uncached's FIRST probe path; its rocm/psutil fallbacks and gpu_eval() (which reads engine_gpu_stats directly) still saw the raw 511°C and cooling-waited a healthy card forever. Filter at the real source — the amdgpu sysfs temp1_input read in gpu_detect — so temp is None for any ≥150°C reading and NO downstream reader can act on it; plus a final chokepoint in read_gpu_temp(). Co-Authored-By:
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- 23 Jul, 2026 9 commits
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Stefy Lanza (nextime / spora ) authored
After a GPU reset the amdgpu SMU can return a constant garbage reading (observed: 511°C = 0x1FF invalid-ADC, with nonsense voltage/fan values). The thermal supervisor believed it, paused the radeon engine and escalated to SIGSTOP — freezing a healthy card forever. Both readers (engine-side thermal.py and the front's supervisor loop) now discard readings ≥150°C with a one-time warning naming the broken sensor; thermal protection for that card resumes when it reads sane values. Co-Authored-By:
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Stefy Lanza (nextime / spora ) authored
Engines run in their own sessions, so supervisord's killasgroup cannot reach them; teardown relies on the front's shutdown handler + the engines' PDEATHSIG. That chain leaked a full front+engine tree across a restart — two trees then fought over one GPU (phantom VRAM, evictions freeing nothing, duplicated model workers, requests served by one tree while logs came from the other). The launcher now pkills any surviving coderai-front/engine/dinov2-embed processes before starting: at that point none can legitimately exist. Co-Authored-By:
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Stefy Lanza (nextime / spora ) authored
Multiple loaded models must each have their own queue — one model's backlog or GPU ownership must never block requests for another model that is loaded and idle: - queue admission is per model (is_full_for): 429 only when the REQUEST'S model already has queue_max_size waiters (global 4× backstop bounds memory); previously ONE hot model's backlog 429'd every other model. - GpuSwapGate: requests for a model already RESIDENT on the engine bypass the ownership gate — no swap is needed and its own per-model queue governs concurrency. The gate now serializes only requests that would actually trigger a model swap (its real purpose). Three co-resident embedders can each serve a request in parallel. Co-Authored-By:
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Stefy Lanza (nextime / spora ) authored
An engine crash re-parented its dinov2-embed child (still holding VRAM) and the respawned engine stacked a second copy — the child now gets PR_SET_PDEATHSIG (dies with the engine) and stale orphans for the same model are reaped before spawn. Engines-card footprints: a model loaded onto an already-full card measures a ~0 VRAM delta and reported "0.01 GB" — report max(measured, config estimate) instead. Co-Authored-By:
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Stefy Lanza (nextime / spora ) authored
The tooltip matched loaded_info entries (raw engine keys) against canonicalized display names client-side and mostly missed — footprints now canonicalize server-side with the same mapping as the model list, keyed by canonical id, so every loaded model shows its VRAM (+RAM when offloading). llama-vl: bound mtmd image_max_tokens (config `image_max_tokens`, default 1024) — large photos otherwise expand to thousands of vision tokens whose transient compute buffer evicts co-resident models on a small card. Co-Authored-By:
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Stefy Lanza (nextime / spora ) authored
Eviction cleanup() closed the llama ctx / killed the dinov2 subprocess while another thread was mid-encode (embeddings hold no pool ref, so _is_key_busy saw idle) — use-after-free segfaults that crash-looped the radeon engine. cleanup() now takes the same per-model lock the embed paths hold, so it waits for the in-flight call. Engines card: the loaded-models hover now lists each model with its measured VRAM footprint and, separately, the host-RAM slice when CPU-offloading. Plumbed engine-state loaded_info → supervisor → registry → engines_list → tooltip. dinov2cpp: build the quantize tool too (dinov2-large-Q8_0 is lossless at cos 0.9973 and drops 612→329 MB). Co-Authored-By:
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Stefy Lanza (nextime / spora ) authored
Co-Authored-By:
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Stefy Lanza (nextime / spora ) authored
dinov2: packaging/dinov2cpp/ builds dinov2.cpp with ggml's Vulkan backend plus a coderai `embed` stdin/stdout server (HF-matching preprocessing: short-side 256 bicubic, center-crop 224; CLS read via ggml_backend_tensor_get so GPU backends work; DINOV2_FORCE_CPU escape hatch). embeddings.py 'dinov2cpp' backend drives one persistent subprocess per model (evict = terminate, reload = respawn); dinov2 GGUF entries route there. Verified on the RX 580: cos 0.9973 vs the HF dinov2-large backend. Tasks page: every task now shows the compute device it runs on and whether the model is CPU-offloading. record_vram_delta records per-model device+offload ("GPU RTX 3090 · CPU offload 12.4 GB"); api_tasks tags each task from that map (engine default as fallback); tasks.html renders a green/amber/gray badge (GPU / offload / CPU). Co-Authored-By: -
Stefy Lanza (nextime / spora ) authored
A GGUF embedder with a configured `mmproj` now loads as a vision-capable 'llama-vl' backend: the mtmd image tower feeds an embeddings context (pooling LAST) under the GME chat prompt — the same scheme as the HF qwenvl backend, so both produce the same vector space (~0.89 cosine agreement at Q4). Runs natively on whatever the llama.cpp build targets (Vulkan on radeon). Text goes through the same prompt+last-token path so both modalities share the GME space; PIL-decoded images are passed as raw RGB bitmaps (mtmd's stb can't read AVIF). Serialized on the per-model lock; cleanup frees the mtmd ctx. Reverts the front's image-reroute-to-HF-sibling: a model pinned to an engine must be served there, not silently moved. The gme mmproj was generated from the local HF checkpoint with llama.cpp's convert_hf_to_gguf.py --mmproj (F16, 1.3 GB). Co-Authored-By:
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- 22 Jul, 2026 7 commits
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Stefy Lanza (nextime / spora ) authored
llama.cpp's embedding API has no image tower, so `image` requests to a GGUF embedder 400'd. When the same model is also registered as a non-GGUF HF entry (e.g. gme-Qwen2-VL GGUF on radeon + HF on nvidia), the front now rewrites the image request to that sibling — one model name for clients; text stays pinned to the GGUF's engine, images go to the engine that has the vision tower. Co-Authored-By:
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Stefy Lanza (nextime / spora ) authored
Two remaining GGUF crashers: (1) detokenize→retokenize is not token-count-stable, so a chunk cut at exactly n_ctx could re-inflate past it inside embed() and trip the GGML_ASSERT — chunks are now shrunk until their re-tokenized length verifiably fits (n_ctx-64 margin); (2) llama.cpp contexts are not thread-safe and parallel indexer requests segfaulted the engine (-11) — embed calls now hold a per-model lock. Co-Authored-By:
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Stefy Lanza (nextime / spora ) authored
Inputs beyond the context window are split into context-sized token windows, each embedded, and combined with a token-count-weighted mean (then normalized) — no content dropped. Single-chunk inputs keep the direct path. Pairs with raising the GGUF embedders' n_ctx in config. Co-Authored-By:
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Stefy Lanza (nextime / spora ) authored
llama.cpp embedding mode SIGABRTs (GGML_ASSERT out_ids == n_outputs) when an input exceeds the batch size — with the default n_batch=512 any long listing killed the radeon engine (front kept respawning it). Size n_batch/n_ubatch to n_ctx at load, and truncate each input to the context window before embed so no accepted input can trip the assert. Co-Authored-By:
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Stefy Lanza (nextime / spora ) authored
A .gguf path in the embedding registry now loads through llama_cpp (embedding=True, n_ctx/n_gpu_layers from the model config) instead of failing in SentenceTransformer. Per-token outputs are mean-pooled, vectors normalized, matryoshka `dimensions` truncation reuses the shared path. cleanup() closes the llama ctx so eviction frees VRAM. Text-only: image requests against a GGUF embedder keep returning 400. Co-Authored-By:
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Stefy Lanza (nextime / spora ) authored
A bulk indexer's burst of first-requests all saw model_obj None and EACH loaded its own copy of the embedder (observed pool_instances=3 for the 4B model + 6 for DINOv2 ≈ the whole card) — this duplicate stacking, not a single load, was what exhausted VRAM and poisoned the measured footprint (26.8 GB recorded for an 8 GB model, measured across overlapping loads). Loads now take a per-model asyncio lock with a re-check, so one request loads and the rest reuse the loaded model. Co-Authored-By:
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Stefy Lanza (nextime / spora ) authored
Teardown leak: _free_pipeline_vram now records the pipeline's CUDA storage pointers up front and, after the normal teardown+gc, walks gc.get_objects() and nulls any surviving tensors out of their list/dict referrers (the proven pass from the CUDA text backend's cleanup) — the "~22 GB untracked (teardown leak; referenced elsewhere)" VRAM is now reclaimed instead of poisoning every later load until a restart. Names the holders when it fires so future leak sources identify themselves. Tracking: a video pipeline only registers in manager.models AFTER its multi-minute load, so concurrent loads raced the free-VRAM check into mutual OOM. note_loading()/clear_loading() reserve the model's estimate for the whole load window; _get_free_vram_gb subtracts reservations and the orphan-VRAM check counts them (no more false "teardown leak" for an in-progress load). The embeddings OOM-retry waits out active reservations (bounded 300s) before evicting and retrying. Co-Authored-By:
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