mirror of
https://github.com/mudler/LocalAI.git
synced 2026-09-13 06:45:26 -04:00
f0fa4a7b1fdd67e3bb0fa951a91af21b21820122
58
Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
 localai-org-maint-botandlocalai-org-maint-bot
|
d85577ff5c |
docs: add Apache APISIX reverse proxy example (#11294)
docs: add APISIX reverse proxy example Document the route settings needed for forwarded headers, streaming responses, and long-running inference behind Apache APISIX. Closes #11215 Assisted-by: Codex:gpt-5 Co-authored-by: localai-org-maint-bot <306269227+localai-org-maint-bot@users.noreply.github.com> |
||
|
localai-org-maint-botandlocalai-org-maint-bot
|
8f56e4e042 |
fix(vram): persist remote probe metadata (#11487)
* fix(vram): persist remote probe metadata The startup warmer repeated remote size and GGUF metadata probes after every restart because both caches lived only in memory. Store successful HTTP probes for 24 hours so frequent restarts reuse the prior results. Bound the cache, reject invalid records, and purge it when gallery data changes. Local model files continue to bypass persistence. Assisted-by: Codex:gpt-5 * fix(vram): check temporary file cleanup The lint gate rejects the unchecked cleanup call in the persistent cache writer. Assisted-by: Codex:gpt-5.6 [golangci-lint] * fix(vram): make persistent cache optional Remote metadata probes can transfer enough data that operators need control over disk reuse and startup warming. Gallery autoload now gates both behaviors, and the runtime setting applies changes immediately. Assisted-by: Codex:gpt-5 * fix(ui): expose gallery startup pre-warm The existing gallery autoload setting also gates the startup metadata warmer. Name both effects in Settings so operators can find the requested boot control. Assisted-by: Codex:gpt-5 --------- Co-authored-by: localai-org-maint-bot <306269227+localai-org-maint-bot@users.noreply.github.com> |
||
|
Richard Palethorpe
|
f8d3f31594 |
fix(vram): contain malformed GGUF metadata (#11374)
Recover parser panics at metadata boundaries, skip unneeded remote arrays, and use the parser's overflow-hardened release. Keep detached gallery workers and CrispASR probes from terminating their processes on malformed GGUF input. Disable startup warming in the provided Compose files as an operational fallback. Assisted-by: Codex:gpt-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> |
||
|
mudler's LocalAI [bot]andEttore Di Giacinto
|
1aa97381f3 |
perf(gallery): warm variant descriptions alongside VRAM estimates (#11297)
Follow-up to #11288, which warmed the VRAM estimate caches at startup and left the variant picker paying its own way. Describing an entry's variants probes the weight files of every build it offers, so the first time a model is opened costs 1.2-1.9s against a cold cache. That is the same cost as an estimate wearing a different hat, and it lands in the same caches underneath, so it belongs in the same pass rather than in a second mechanism. The warm-up now describes variants for the entries it walks. Entries that declare none cost nothing: the call is gated on HasVariants rather than attempted and discarded. The host resolve env is derived once for the run, since it describes the machine rather than the entry. Failure handling matches the estimate half. An entry whose variants cannot be described is logged at debug and skipped, and the estimate for that same entry is unaffected, because neither half is allowed to fail the other. Measured against a live instance with 1,595 models, first ever call to /api/models/variants/:id after a cold boot: before 1.2-1.9s after 2ms The warm-up's own cost barely moves: 3m0s to 3m19s for 300 entries, of which 40 declared variants. It stays bounded by the same knobs, and LOCALAI_VRAM_WARM_LIMIT=0 still turns the whole thing off. Assisted-by: Claude:claude-opus-5 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
||
|
mudler's LocalAI [bot]andEttore Di Giacinto
|
74b7ea2829 |
feat(ui): replace the gallery and inventory tables with a rail and a detail pane (#11288)
* feat(ui): rename the Install Models nav entry to Discover "Install Models" named the action rather than the destination, and it was the only multi-word entry in a rail of one-word ones (Home, Chat, Studio, Talk, Build, Operate). A bare "Models" was the obvious fix but it collides with the installed-models view under Host, which is a different page for a different job. "Discover" keeps the rhythm and says what the page is for. The icon moves from a download arrow to a compass for the same reason: the page is browsed before it is installed from. Translated in all seven locales rather than left to fall back, so a locale switch does not leave the entry in English. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-5 [Claude Code] * feat(ui): replace the gallery table with a rail and a detail pane The eight-column table was not the real problem; the click-to-expand row underneath it was. Variants, files and a VRAM estimate never fitted inside a <tr>, so they were pushed into a drawer that could hold one model at a time, could not be linked to, and had no room to say anything useful. The gallery is now a rail to scan and a pane that answers. The pane has two states and no third: with nothing selected it is the discovery page, and with a model selected it is that model's detail. Selection lives in the URL, so a model is linkable and Back steps out of the detail instead of off the page. The rail groups by capability while browsing and flattens to results the moment a term is typed. That is a rule rather than a toggle: once someone has said what they are looking for, the buckets are between them and the answer, and making the user choose would be handing them our problem. The detail pane plots VRAM against context length with the host's own limit drawn across it. This is new information, not a restyle. A single number invites "so will it run?", and the honest answer is usually "yes, up to a 32k context", which is a shape rather than a number. The estimates were already fetched for every context size, so it costs no new request. Backends that take no context length say so instead of being given a meaningless chart, and a host with no GPU gets no chart at all rather than bars with nothing to compare against. The split-button variant menu goes with the actions column. The pane lists every build with its backend, quantization, size, fit and a details disclosure, each installable, which is what the dropdown was a cramped substitute for. Its tests move onto that list; the three contracts it alone carried (fetch-once caching, the loading state, an unfit build staying installable) are backfilled against the pane. RecommendedModels moves inside the pane, where it has the width to argue for a model instead of listing one, and keeps its own dismissal and collapse. Rail entries carry no description. Two lines is the budget and the second is better spent on whether the thing will run; the stripped-Markdown contract moves to the pane's lede, tooltip included. e2e: 123 passing across models-gallery, navigation, recommended-panel, model-artifact-operation, operations-strip and page-render-smoke. Inline styles in Models.jsx drop from 82 to 41. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-5 [Claude Code] * refactor(ui): extract the split view into shared components Discover shipped its rail, pane and detail header as private functions inside Models.jsx. Backends and Host have the same defect and want the same shape, so leaving them there guarantees three rails that drift. SplitView, EntityRail, DetailHeader and StatGrid now live under components/split/. EntityRail is deliberately data-driven: a surface maps its own entity onto { id, name, icon, meta, stripe, groupId } and keeps its vocabulary to itself, which is what stops the rail learning about models, backends and loaded state all at once. The CSS moves with it. What was .discover__rail is .entity-rail, .discover__ pane is .split-view__pane and so on, because a class named after one page is a lie on the next two. Only what is genuinely Discover's stays behind the old prefix: the shelves, the hero and the VRAM-by-context chart. Two additions the shared rail needs and Discover did not: a state stripe, for surfaces read by condition before they are read by name, and an empty label. Discover passes neither. No behaviour change. e2e 100 passing across models-gallery, navigation and models-recommended-panel. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-5 [Claude Code] * feat(ui): put the backend gallery on the split view Same defect as the model gallery, so the same shape: a seven-column table over a click-to-expand row that was the only place the repository, licence, tags and links could go. The rail groups backends by the use case they serve, sharing Discover's taxonomy on purpose: a backend is the runtime a use case needs, so "vision" ought to mean the same thing one level down. It flattens on a query for the same reason it does on Discover. The zero state is the one real departure. A backend's fitness is not free memory, it is the accelerator and platform it was built for, so the pane leads with what this host is, then what is not installed yet, then whether anything installed has gone stale. The table listed 37 runtimes and left "which of these can even run here" entirely to the reader. Distribution moves into the pane, which is the one thing a row could never carry: which nodes hold a copy and which do not, with the install-on-more control next to it rather than squeezed against a chip. The distributed and target-node action logic is unchanged, including the guard that keeps a hardware-specific build off the fan-out path. The split-button popover loses its per-row anchoring because there are no rows; one pane, one anchor. Selection lives in ?backend=, preserving the ?target= scope rather than clobbering it. e2e: 139 passing across models-gallery, navigation, backends-management, models-recommended-panel, nodes-per-node-backend-actions, page-render-smoke, operations-strip and model-artifact-operation. The backends spec gains six split-view tests; its three description-cell tests move onto the pane lede. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-5 [Claude Code] * feat(ui): put the Host inventory on the split view The last of the three surfaces, and the one that is not a catalog. Both tabs had the same click-to-expand row, so the shell transfers; what does not transfer is the zero state, because there is nothing to discover in your own inventory. With nothing selected the pane reports what is happening: how many models are loaded, what failed, what has an update, and which models are holding VRAM right now. Every number was already on the page. None of them had been assembled into one statement, so "what is going on" was a question the tabs could not answer however long you looked at them. The rail buckets by state rather than capability - Running, Idle, Disabled for models; Update available, Installed for backends - which is the opposite of the galleries and deliberately so: nobody opens Host wondering which of their models does vision. Entries carry a state stripe for the same reason. Load and Stop are promoted out of the kebab, because that is what an operator came for; the rest stays behind the menu rather than diluting it. Adopted, pinned and alias badges follow the model into the pane: they are facts about the thing, not about its state, and the rail line is spent on state. Deliberately NOT done: folding the two tabs into one rail, as the mock had it. It costs five URL parameters, the manage-tab localStorage key and the stat-card shortcuts, all of which are live deep-links today. The tabs stay as the group selector; merging them is a follow-up with its own migration. e2e: full suite 355 passing. New host-split-view spec; alias-template, manage-logs-link, manage-action-menu-position and model-editor-back-nav move off `.table` and the row kebab onto the rail and the pane. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-5 [Claude Code] * polish(ui): accessibility and consistency pass over the three split views Findings from a pass over what the previous four commits actually shipped, rather than what they were supposed to. The rail was not a listbox. ARIA lets a listbox contain options and groups, and nothing else, but each group's collapse control is a button that has to sit inside the scroller with the entries it folds. It is now a labelled group of buttons, which is the honest description; selection is announced with aria-current and the arrow keys are unaffected. Every entry was its own tab stop, so tabbing past a forty-entry rail to reach the pane took forty keystrokes. Roving tabindex makes the rail one stop, and arrowing now moves focus with the selection instead of leaving it behind on an entry Tab can no longer reach. The rail rounds its corners with overflow:hidden, which was clipping the focus ring off the first and last entries entirely. Inset outlines fix it. A 30px row is fine under a mouse and too small under a thumb, so coarse pointers get a 44px target without costing density on a desktop. One slot said three different things: "9 models loaded" on Discover, "12 loaded" on Backends, "3 of 9" on Host. All three lists are a page of a larger set, so all three now say so the same way. Also removed: an emptyLabel prop on EntityRail that nothing passed, its dead CSS rule, and MODELS_COLSPAN and ResourceRowDesc, which died with the tables. e2e: full suite 355 passing. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-5 [Claude Code] * fix(ui): correct three defects only a real gallery exposed Running the branch against a live instance with 1,595 models and 1,017 backends, rather than against mocked fixtures, surfaced three things the e2e suite could not. Grouping did nothing. The rails matched on the use-case keys the filter chips send (`chat`, `tts`, `transcript`), but those are a server-side vocabulary the handler maps onto entries. What entries actually carry is free-form and inconsistent: models come back tagged `llm`, `gguf`, `vision`, `coding`, and backends `LLM`, `text-to-text`, `audio-transcription`. Nothing matched, so every model landed in "Everything else" and the feature was decorative. Grouping now lives in utils/entityGroups.js, shared by both galleries, matching case-insensitively against the vocabulary the API really uses, with the entry's backend as a fallback signal - a backend named `whisper` is a speech backend whatever its tags say. Order is specific before general and that is load-bearing: a vision model is tagged `llm` too, so testing text first would swallow it. The zero state claimed GPU memory on a machine with no GPU. The resources endpoint reports system RAM in the same field when gpu_count is 0, so the hero read "84.4 GB of GPU memory" next to the recommendations panel correctly saying "No GPU detected". The number was never wrong, only its label; it now says system memory unless a GPU is actually present. The page title still said "Install Models" under a nav entry saying Discover. Also: the keyboard test named the model it expected to arrive at, which made it a hostage of the grouping table and broke the moment the buckets were fixed. It now asserts that the selection moves and returns. e2e: full suite 355 passing. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-5 [Claude Code] * fix(ui): the filters and the rail were fighting over the same job Four things you find odd on Discover, and they turn out to be one mistake seen from four sides. The rail grouped the current page. The listing is paginated at nine rows, so those bucket headers described nine entries out of 1,595, and turning a page reshuffled the sections under the reader. The structure was never stable because it was computed over the wrong set. The chips were redundant for the same reason, seen from the other side. They send tag= and filter all 1,595 server-side. The rail grouped nine of them client-side by the same axis. Two controls for one job, and the weaker one was the one this branch added, so it goes. Grouping stays only on Host, where the list is complete, local, and bucketed by state rather than capability. The search bar felt odd because it sat in a full-width band while the thing it narrowed was a 290px rail below and to the left. The whole band now lives in the rail column: search, backend, use cases, refinements, then the list it narrows. One column to say what you want, one to show what you got. Nineteen chips do not fit at that width, so they fold into a disclosure that states the selection. A disclosure and not a popover, deliberately: picking use cases is multi-select and interleaves with the backend select and the toggles below, and a popover dismisses itself the moment you touch either. The header held two counts and two buttons at arm's length from all of it. The counts were the third statement of the same number on one screen, after the rail's "9 of 1,247" and the pane's own headline, so they go. The buttons move into the pane's zero state, which is the surface that answers "what do I do here". Also: the two first-run empty states wore .loading-center, which is display:flex in the default row direction because it exists to centre one spinner. With four children that put the icon, the heading, the sentence and the buttons on a single line with no gap. They are now a proper full-height empty state. e2e: full suite 353 passing. Grouping tests are replaced by ones asserting the rail stays flat; chip tests open the disclosure first; two filter-layout tests that asserted the old three-band arrangement now assert the column. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-5 [Claude Code] * polish(ui): make Discover a full-height view, group the chips, name the refinements Four things, all of them the same complaint: the page read as a document with controls scattered on it rather than as one view. The header is fused. A title block with its own padding, a subtitle and two counts made the split view look like an attachment to a document that happened to sit below it. It is now a slim bar carrying the title, the count and the two page-level actions, and the split fills the rest of the window. Rail and pane scroll independently, so the filters and the pane's headline stay put while a long list moves under them. The chips group. Nineteen in a flat row is a lot to scan even behind a disclosure, and they already belong to the four families the rest of the UI speaks, so they are bucketed by those. "All" sits on its own above them without a heading, because it is a reset rather than a use case. The refinements stop looking dumped. When the band became a column they were three controls left where they landed; they now read as a named section with one control per row. The zero state suggests again. It had decayed into a "Browsing / 9 of 1,247 / select a model" line that restated the count for the third time on one screen. It now offers the four use cases as tiles that set the filter, which is the shelf idea from the mock without inventing curation or paying for a second fetch. Two bugs found by looking at it rather than at the tests: the disclosure was clamped to 190px, which cut it off partway through its third section so two of the five never appeared at all; and the creation actions rendered twice, once in the new bar and once in the pane hero a few pixels away. e2e: full suite 353 passing. The chip-row test now holds its contract across the per-family rows rather than a single one, and additionally asserts every family is present and non-empty. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-5 [Claude Code] * fix(ui): pin the split view's height so a long detail scrolls the pane Selecting a model with a long description grew the whole page and dragged the rail down with it, which is the opposite of what "full height" was supposed to buy. The flex chain was right and the ceiling was missing. .app-layout and .main-content are min-height:100dvh, which is a floor: flex distributes free space but nothing caps growth, so a pane taller than the viewport expanded the column, the document scrolled, and the rail stretched to match. height:100% on the pane then resolved against an auto-height parent and did nothing. The chat route already solves this by pinning .main-content to 100dvh. The same treatment now applies to any route containing a .page--app, selected with :has() so the shell does not have to learn which pages happen to be split views. Below the stacking breakpoint the pin is lifted, because two stacked halves in two short scrollers is worse than a page that scrolls. Measured on a live instance: document height stays at the viewport across selection (950px either side) and the pane overflows internally instead. Adds discover-height.spec.js, which asserts the page height and the rail height are unchanged by selection and that the pane is the thing that scrolls. The existing specs could not have caught this: they mock short descriptions, and the bug only appears when the pane has more content than the viewport holds. e2e: full suite 355 passing. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-5 [Claude Code] * feat(ui): give Backends and Host the full-height view, and fix the Update button Backends now matches Discover: the header fuses into a slim bar carrying the title, the count and the page-level actions, the filters move into the rail column where they narrow the rail and nothing else, and the split fills the window. Its seven chips fit at rail width, so unlike Discover's nineteen they need no disclosure. Host gets the bar and the height; its resource monitor, summary cards and tabs stay above the split, because those are read once while the rail and the pane are worked in. Two things the height change surfaced. The console layout is a flex row with align-items:flex-start, so its body sizes to content. Right for the pages it was built for, wrong for a split view, which needs a ceiling to scroll inside: without it the Backends rail ran past the viewport and over the footer. Pinned with :has() so only split-view routes are affected. The filters vanished when nothing matched. Both galleries swapped the whole shell for an empty state, which took the search box and the chips with it, so the page said "try adjusting your search or filters" while offering neither. The shell now stays and the empty state moves into the pane. Also fixes the Update control on Host, which had no className at all and rendered as bare text, next to a status span that had picked up btn classes and two copies of `fas` and so rendered as a button you cannot press. They have swapped appearances back. e2e: full suite 355 passing. The render-smoke selector learns .view-bar__title, since the pages it checks no longer all use PageHeader. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-5 [Claude Code] * fix(ui): keep the view mounted while searching, and bring rail grouping back Searching replaced the whole view with a loader. The search box lives in the rail column, so every debounced refetch unmounted the field being typed into and dropped its focus with it. The list, the filters and the pane went too. The shell now stays and the rail says it is busy: a sweep bar under its header and the stale list dimmed, so the eye knows the answer is being replaced without losing its place. A cold start still gets the skeleton, because there is nothing to keep. The condition for that is "nothing has loaded yet", not "the list is empty". Those differ exactly when someone is editing a query that matched nothing, and getting it wrong there would unmount the view on the keystroke after a no-results search - the worst possible moment. Grouping comes back on both galleries. It was removed because nine rows could not fill five buckets, so a page turn rebuilt the rail's whole structure. That was a symptom of the page size rather than of grouping: the rail now asks for 30 rows instead of 9 (Backends 60 instead of 21), which is enough for the sections to read as structure and turns five times fewer pages. The order of the sections is fixed, so what changes between pages is membership, not arrangement. Grouped while browsing, flat while searching, as before: once a term is typed the buckets stand between the reader and the answer. Also gives GalleryLoader a class and a testid instead of six inline style declarations on a bare div, which is why nothing could select it. e2e: full suite 359 passing, including a new spec asserting the search box keeps its focus and its value across a refetch, and that a cold start still shows the skeleton. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-5 [Claude Code] * perf(gallery): stop invalidating the VRAM estimate caches on every request Searching or turning a page felt slow. It was not the search and not the listing: /api/models answers in 3-9ms. It was the VRAM estimate, which the gallery asks for once per row, and which took ~2.3s every single time however often the same model was asked about. pkg/vram already caches what makes that expensive - the remote content-length probes, the GGUF metadata reads and the HF repo sizes. Those caches key on a gallery generation counter, and AvailableGalleryModelsCached triggered a background refresh on every call, with each refresh bumping the counter. One page view is one listing request plus thirty estimate requests, each of which re-read the gallery and started another refresh, so the generation moved constantly and every cache entry was stale before it could ever be read. The caches were dead in production. Three changes, each doing one thing: A refresh interval. The cached list is still served immediately; this only decides how often re-fetching from upstream is worth starting. Five minutes, as a package variable so tests can drive it without waiting. A generation bump only when the gallery actually changed. An unchanged gallery re-fetched on schedule must not throw away work that is still valid, which is the difference between an estimate costing nothing and costing a network round trip. A separate "loaded" flag. The cache engaged on `cached != nil`, so a gallery that legitimately holds nothing read as never-loaded and took the blocking path on every call, bumping the generation each time. Found by the test for the interval, which could not pass while this was true. Measured against a live instance with 1,595 models: one estimate, repeated 2.3s -> 2ms a page of 30, in parallel 10s -> 0.04s A first, genuinely unseen model still costs its remote probe. That is inherent; what changed is that it is now paid once per model per gallery version rather than once per request. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-5 [Claude Code] * perf(gallery): warm VRAM estimates at startup, and stop the UI waiting on them Two halves of the same complaint: the gallery stalls on VRAM estimation. Server side, the estimates are now warmed in the background at startup. Estimating an entry nobody has asked about costs a remote probe of its weight files, and the gallery needs one per row, so the first visitor was paying for the whole page. The warm-up walks the gallery in the order the UI lists it, so the first page is ready before anyone reaches it. It is bounded and it never blocks: 300 entries at 4 at a time by default, on its own goroutine, stopping with the server's context. Warming the whole gallery would be thousands of probes on every boot, which is rude to the upstream and slow to finish; warming nothing leaves the first page paying two seconds a row. Anything past the limit still warms itself on first view. LOCALAI_VRAM_WARM_LIMIT=0 turns it off for an air-gapped host, LOCALAI_VRAM_WARM_CONCURRENCY=1 slows it for a metered link. Client side, the page no longer waits on estimates it does not need yet. It fired one request per row at once; a browser allows about six connections per host, so thirty estimates took every slot and the request behind a click - the variant list, an install - queued behind work nobody asked for. That is the freeze: the list was already usable, and the UI was busy fetching sizes. Four at a time leaves room for the interactive request to overtake, and a row whose estimate is still in flight says "sizing…" rather than leaving a blank where a number will appear. buildEstimateInput moves to core/gallery as EstimateInput, since the handler and the warmer both need it. Measured against 1,595 models, from a cold boot: page 1, 30 estimates in parallel 10s -> 0.04s full warm-up (299 of 300 entries) 3m, in the background Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-5 [Claude Code] * chore: untrack data/.local_user_id and ignore the runtime data dir `local-ai run` writes its instance state under ./data when started from the repo root, which is exactly what a contributor testing a build does. The identity file ended up committed on this branch by a `git add -A` while verifying the gallery changes against a live instance. Anchored, so it matches the runtime directory at the repo root and not a `data` directory nested inside some package. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-5 [Claude Code] --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
||
|
Owen Adirah
|
1659365059 |
docs: add reverse proxy timeout guidance (#11195)
docs: clarify reverse proxy bulk job guidance Mention ingress controllers as another place to configure equivalent upstream response timeouts, and include an example private LocalAI URL for trusted bulk jobs. Assisted-by: Hephaestus:openai/gpt-5.5 [opencode] Signed-off-by: Owen Adirah <owenadira@gmail.com> |
||
|
localai-org-maint-botandlocalai-org-maint-bot
|
ecdb32193d |
docs(proxy): cover long inference timeouts (#11065)
Document the reverse-proxy settings needed for long-running and multimodal requests, and distinguish edge-generated 504 responses from the optional LocalAI busy watchdog. Assisted-by: Codex:gpt-5 [Codex] Co-authored-by: localai-org-maint-bot <306269227+localai-org-maint-bot@users.noreply.github.com> |
||
|
Richard Palethorpe
|
49ef40a187 |
feat(classifier/VAD): support voice control on low power devices (#10804)
* feat(llama-cpp): route Score through the slot loop Score previously bypassed the slot loop with a direct llama_decode: a conflict guard aborted the whole process if scoring raced generation, the config validator had to reject score alongside chat/completion/embeddings, and every candidate re-decoded the full shared prompt. Add SERVER_TASK_TYPE_SCORE to the (patched) upstream server so score tasks are scheduled like any other slot work: generation and scoring serialize naturally, the shared prompt is decoded once per call, and the slot's prompt cache carries the conversation prefix across calls. Context checkpoints at the score boundary and at the cache-divergence point keep SWA/hybrid/recurrent models (e.g. LFM2.5) from re-prefilling the whole prompt per candidate: warm-turn scoring on a 6-option set drops from ~8s to ~0.5s on a desktop CPU. The conflict guard and the validation split are removed; declaring score with generation usecases on one config is now supported and shares the slot cache. Assisted-by: Claude:claude-fable-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> * feat(realtime): classifier wire types and pipeline config Wire types and YAML config for realtime classifier mode: sessions carry a localai_classifier extension (options with canned replies/tool calls, softmax threshold, normalization, history trimming, fallback modes, and a deterministic wake-word address gate), mirrored by pipeline.classifier in the model YAML and surfaced in the config-meta registry. The localai.classifier.result server event reports the full score distribution per turn. Assisted-by: Claude:claude-fable-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> * feat(realtime): classifier response flow Classifier-mode responses: instead of autoregressive generation, each user turn is prefill-scored against the option list (router.ScoreClassifier prompt/candidate shapes over the Score primitive) and the winning option's canned reply and tool call are emitted through the existing response machinery. Below-threshold turns take the configured fallback (none / canned reply / generate); empty transcripts and unaddressed turns (wake word not mentioned) skip scoring entirely. The scoring probe defaults to the latest user message only — small scorers echo canned replies from prior turns back as the top option otherwise. Built for hardware that can afford prompt processing but not decode: with slot-based Score the option list stays KV-cached across turns, so a turn costs roughly one forward pass over the new words. session_update_error events now carry the validation cause instead of a generic message. Assisted-by: Claude:claude-fable-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> * fix(realtime): bound the VAD tick's scan window and buffer retention The VAD tick loop re-scanned the entire input buffer every 300ms and only trimmed it on zero-segment ticks or commits. Audio that keeps producing segments without a committing pause (steady noise a mic pipeline lets through, music, continuous speech) grew the buffer toward the 100MB cap with each tick rescanning all of it — O(n^2), measured at ~3.3ms of silero per buffered second: past ~90s retained, ticks run back to back and pin ~4 cores until the stream stops. Silero's recurrent state only carries a few hundred ms of context, so rescanning old audio buys nothing. Clip the slice handed to the VAD to the largest silence the commit test can need to measure (server_vad silence window or the semantic eagerness fallback) plus a warm-up margin, and rebase the returned segment times so every downstream consumer keeps whole-buffer coordinates. An open turn whose clipped window is all silence now commits (the silence outran the window) instead of being discarded as no-speech. Independently, retain at most 90s of raw buffer, rebasing the live-feed and EOU cursors on trim — this also bounds the previously unbounded VAD-error path. Turn boundaries are otherwise unchanged: no forced commits, no new coordinator states. pipeline.turn_detection.vad_window_sec can widen the scan window; values below the automatic floor are ignored. The tick body is extracted into vadTick so specs can drive turn detection synchronously (same shape as classifySoundWindow); the babble reproduction that pinned 4 cores now plateaus under 10% of one core. Assisted-by: Claude:claude-fable-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> * fix(backend): let per-model threads override the global default ModelOptions overrode a set per-model threads value with the app-level --threads whenever the latter was non-zero — and WithThreads defaults it to the physical core count, so it always was. The YAML threads: knob has been dead config: a tiny VAD model could never opt down from the global pool size. SetDefaults already fills an unset per-model value from the app config, which is the intended precedence; resolve threads through a helper that honors it (explicit threads: 0 still means unset). Assisted-by: Claude:claude-fable-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> * chore(gallery): single-thread the silero VAD Silero is a ~2MB recurrent model with no exploitable graph parallelism: measured per-call latency is identical at 1 and 10 ORT threads, while every extra pool thread just spin-waits between the realtime loop's frequent tiny inferences. Assisted-by: Claude:claude-fable-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> * docs(realtime): classifier mode, VAD scan window, threads precedence Document the realtime classifier mode (options, threshold guidance, wake-word address gate, empty-transcript handling), the VAD scan window and 90s buffer retention (pipeline.turn_detection.vad_window_sec), the per-model threads precedence, and the M3 classifier note in the realtime state-machine design doc. Assisted-by: Claude:claude-fable-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> * perf(llama-cpp): score all candidates in one batched decode One scoring call is now a single SERVER_TASK_TYPE_SCORE task: the slot decodes the shared prefix (prompt + longest common candidate token prefix) once, then forks one sequence per candidate off it (metadata-only for the unified KV cache, copy-on-write for recurrent state) and decodes every candidate's unique tail in one llama_decode. Previously each candidate was its own task that restored the boundary checkpoint and re-decoded its full tail sequentially, paying per-candidate task and decode overhead. The context reserves SERVER_SCORE_FORK_SEQS extra sequence ids (and recurrent-state cells) beyond the parallel slots via the new common_params::n_seq_score_forks. Forking requires the unified KV cache (already this backend's default) since per-sequence streams would shrink n_ctx_seq; an explicit kv_unified:false disables forking and Score calls that need it fail cleanly. Candidates beyond the fork/output budget decode in successive chunks. Wire contract and scores are unchanged: per-token logprobs are stitched from the shared region and the forked tails. Verified bitwise deterministic call-to-call and independent of candidate order (no cross-fork leakage via equal-length candidate swap); ranking matches the per-candidate implementation on the drone battery (winner softmax 0.99996 vs 0.99997), and >16-candidate chunking, prefix-of-another and empty candidates all pass. Measured on a desktop CPU: warm /api/score calls 0.52s -> 0.23s; warm realtime classifier turns 196-303ms. The 9-candidate drone turn decodes ~17 unique tail tokens in one batch instead of nine sequential ~220ms checkpoint-restore tasks. Assisted-by: Claude:claude-fable-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> * fix(realtime): gate scoring capacity by model usecase Reserve llama.cpp scoring slots only for models that explicitly declare the score usecase, while allowing score to coexist with chat and completion. Reject incompatible unified-KV settings and classifier activation on models without scoring capacity. Propagate application defaults when resolving realtime and preload pipeline stages so unset thread counts are resolved consistently without overriding explicit model settings. Assisted-by: Codex:gpt-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> * fix(ci): honor APT mirrors in the prebuilt llama-cpp compile step The builder-prebuilt path installs gcc-14 with apt directly and ignored the APT_MIRROR/APT_PORTS_MIRROR build args the from-source path already honors, so an ubuntu mirror outage broke every arm64 backend build. Pass the args into the stage and run apt-mirror.sh (already in the build context via COPY . /LocalAI) before the apt step. Assisted-by: Claude:claude-fable-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> * feat(realtime): classifier argument slots via constrained completion Hybrid classify-then-complete: a classifier option's canned tool call can declare typed argument slots (number | enum | string, with defaults and prompt hints) referenced as "{{name}}" in the arguments template. When the option wins, the slots are filled by a short grammar-constrained completion that continues the exact scoring prompt — rendered by the same cached ScoreClassifier, so the llama.cpp prompt cache is already warm — with the chosen route JSON re-opened at the first slot field. A GBNF grammar pins the field skeleton and frees only the values; temperature 0, a couple dozen tokens at most (~300ms on a desktop CPU for two slots). Slot declarations and hints ride the option descriptions in the shared system prompt, informing scoring and the fill alike at no per-turn token cost. The localai.classifier.result event carries the final arguments and a fill_latency_ms. On inference failure the slots' defaults apply; a slot without a default fails the response (or falls through with fallback.mode: generate). Slot filling requires completion alongside score in the scoring model's known_usecases. Verified end-to-end on the Pi drone demo: "fly forward three meters" in distance mode classifies forward and infers {"distance": 3, "units": "meters"} in ~310ms, and the drone flies exactly 3 units. Assisted-by: Claude:claude-fable-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> * feat(realtime): splice filled slot values into classifier replies A classifier option's spoken reply can now reference its tool's argument slots ("Going forward {{distance}} {{units}}."): the values inferred by the slot-fill completion — or the recovery defaults — are spliced into the reply as plain text before it is emitted, so what the assistant says confirms what it actually inferred. Placeholders without a value stay literal, and options without slots are untouched. FillToolArguments now returns the raw slot values alongside the spliced arguments JSON to make the reply templating possible. Assisted-by: Claude:claude-fable-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> * fix(realtime): harden classifier slot completion Reserve context for constrained slot filling, size completions from their encoded output, and encode enum grammar literals as valid JSON. Reject empty enum values and cover the failure modes with regression tests. Assisted-by: Codex:gpt-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> * feat(realtime): prewarm the classifier scoring prompt on registration Swapping a session's classifier option list (a voice-switched command mode, for instance) made the next turns pay a full re-prefill of the new option-list prompt — measured 2.4s vs 0.3s warm on a desktop CPU, and worse: on hybrid-memory models like LFM2.5, whose state cannot be partially rewound (llama.cpp can only restore checkpoints), *every* probe change re-prefilled from scratch whenever the last checkpoint missed the probe boundary, so even same-list turns intermittently cost full prefills. Registering an option list (pipeline seed or session.update) now fires a best-effort background prewarm: two throwaway scores with distinct probes. The first prefills the new option-list prompt; the second, diverging exactly where per-turn probe text starts, plants the backend's rewind point (KV checkpoint) at the stable-prefix boundary that every real turn reuses. The prewarm hides behind the canned mode-switch reply — by the time it finishes speaking, the cache is warm. Idempotent per option set, detached from the registering request's lifetime. Measured on the drone demo (LFM2.5-1.2B, desktop CPU): first turn after a mode switch 2374ms -> 340ms; intermittent same-list full prefills (1.3-2.1s) all -> under 0.5s. For clients that swap lists frequently, options: [parallel:2] on the scoring model additionally keeps one slot per list via prefix-similarity routing (+26MB RSS, unified KV). Assisted-by: Claude:claude-fable-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> * perf(llama-cpp): checkpoint scoring at the caller-declared stable prefix Hybrid-memory models (LFM2.5 shortconv, Qwen3.5 deltanet — where new small models are headed) cannot rewind their state, so any prompt-cache reuse that needs a rewind falls back to a full re-prefill. For classifier scoring that meant every probe change re-processed the whole option-list prompt: the server's checkpoints were placed reactively (at wherever the previous task happened to diverge), so a checkpoint past the next divergence was erased rather than restored — measured as intermittent 2-10s turns on prompts with a 95%+ common prefix. The classifier now computes the probe-invariant prompt prefix once (the byte-wise common prefix of two synthetic probe renders) and declares its length with every Score request; the server maps it to a token boundary and forces a KV checkpoint exactly there on each score prefill. That checkpoint sits at or before every future divergence under the same option list, so it always survives and always restores — repeat scoring costs probe+candidates regardless of how the probe changes. Also: - prewarm reruns on every option-list registration instead of memoizing per list: with boundary checkpoints a redundant rewarm costs two probe-sized decodes, while skipping one after a slot eviction (three lists sharing fewer slots evict in LRU cascades) silently moves a full re-prefill onto the user's next turn - new llama.cpp backend option rs_seq:N exposes bounded recurrent-state rollback outside speculative decoding; measured impractical for deltanet-scale states (65GB for 64 snapshots on Qwen3.5-4B) but cheap insurance for small-state models - docs: the multi-list recipe (parallel:N + sps:0.5 — the default slot similarity threshold funnels distinct lists onto one slot) Measured on the drone demo (LFM2.5-1.2B scorer, desktop CPU), steady state: every turn 285-421ms including mode switches, vs 2.4s post-switch and intermittent 1.3-2.9s re-prefills before. Assisted-by: Claude:claude-fable-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> * fix(realtime): align classifier cache guidance Document the single-score prewarm behavior and clean the vendored score patch formatting. Assisted-by: Codex:gpt-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> * fix(llama-cpp): guard score task for fork backends TurboQuant and Bonsai reuse the primary gRPC server against llama.cpp forks that do not carry LocalAI's slot-based Score patches. Compile the Score integration only for the patched primary backend and return UNIMPLEMENTED from fork builds instead of referencing absent task types and common_params fields. Assisted-by: Codex:gpt-5 [gh] Signed-off-by: Richard Palethorpe <io@richiejp.com> * fix(dev): generate gRPC code before commit lint The coverage phase regenerates ignored protobuf bindings, but lint runs first and can fail against missing or stale output. Generate the pinned bindings before lint so the gate always type-checks the current schema. Assisted-by: Codex:gpt-5 Signed-off-by: Richard Palethorpe <io@richiejp.com> --------- Signed-off-by: Richard Palethorpe <io@richiejp.com> |
||
|
Richard Palethorpe
|
9c43b2da8f |
fix(model): make backend shutdown model-scoped (#10865)
Avoid holding the global loader lock across backend lifecycle waits and propagate forced shutdown through distributed workers. Track parallel requests with in-flight counters and reserve worker ports until process termination. Add focused race tests and an authoritative FizzBee lifecycle model with a fail-closed conformance target. Assisted-by: Codex:GPT-5 [FizzBee] [Ginkgo] Signed-off-by: Richard Palethorpe <io@richiejp.com> |
||
|
mudler's LocalAI [bot]andEttore Di Giacinto
|
40d35c0385 |
docs: onboarding overhaul, dedup, and error docs (#7711) (#10895)
* docs: fix CPU image tag (latest, not latest-cpu) Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs: use canonical localai/localai registry in models guide Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs: replace dead llama-stable backend with llama-cpp Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs: correct mitm-proxy intercept config and redaction tier Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs: fix text-to-audio endpoint and broken notice block Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs: fix VAD example, stale FAQ, broken link, CLI list, whats-new dump Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs: render advanced/reference section indexes (consolidate _index) Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs: remove duplicate getting-started build/kubernetes pages Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs: fold container image reference into installation/containers Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs: remove stale advanced fine-tuning page (superseded by features/fine-tuning) Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs: fold distribution/longcat/sound pages into their parents Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs: make getting-started index accurate and complete Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs: carry one concrete model through the getting-started path Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs: add end-to-end 'build your first agent' walkthrough Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs: add runtime errors reference; consolidate troubleshooting from FAQ Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs: add agent actions catalog Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs: agent-scoped MCP, skills walkthrough, agentic disambiguation Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs: add concrete gallery install lines to media feature pages Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs: merge installation into getting-started (URLs preserved via aliases) Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs: add Operations section; move operator pages and P2P API reference Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs: journey-ordered top nav and grouped feature sections Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs: add docs-with-code process gate (PR template + agent instructions) Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs: remove em/en dashes from documentation prose Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
||
|
pos-ei-don
|
06b4a29387 |
docs(config): document grpc.attempts timing + tuning guidance (#10868)
The gRPC configuration table only listed the two fields with a one-line description each, without defaults, without explaining what the total load window looks like, and without hinting when a user should adjust them. In practice the default 20 attempts x 2 s = 40 s window is way too tight for large NVFP4 / FP8 models on slow storage or first-run CUDA-graph capture, and the resulting kill (exitCode=120, 'context canceled') looks like a backend crash even though the backend is still making legitimate forward progress. Extend the section with: - Defaults column (20 and 2) added to the table - Prose explaining that these govern the readiness handshake between LocalAI and a freshly spawned backend (Health polling loop) - Total-load-window formula - Concrete failure signature so users can recognize a timeout-kill vs. a real backend crash - Example configuration for a ~10 min cold-load window (grpc.attempts 140, attempts_sleep_time 5), with a note that inference-timeouts and the watchdog are unaffected. |
||
|
LocalAI [bot]andEttore Di Giacinto
|
8cec22c3b7 |
feat(vram): per-node VRAM allocation budget (LOCALAI_VRAM_BUDGET) (#10833)
* feat(vram): add vrambudget primitive for per-node VRAM caps Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(vram): apply default VRAM budget in xsysinfo aggregate getters Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(vram): wire LOCALAI_VRAM_BUDGET flag to xsysinfo default budget Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(vram): persist VRAM budget via runtime settings with live apply Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(vram): reset process-global VRAM budget after runtime-settings spec Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(vram): add VRAM budget field to Settings page Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(vram): store and enforce per-node VRAM budget in the node registry Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(vram): apply per-node VRAM budget in router hardware defaults Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(vram): report worker VRAM budget in node registration The distributed worker now reports its operator-set VRAM budget string (LOCALAI_VRAM_BUDGET) to the server on registration. The worker keeps reporting RAW total/available VRAM and never sets the xsysinfo process-global budget (that stays standalone-only); the server resolves and enforces the budget uniformly (Task 6). Also closes a Task 6 gap: on re-registration, a struct Updates zero-skips an empty budget, so a worker that dropped LOCALAI_VRAM_BUDGET left the stale cap in place. For non-admin-override nodes the budget columns are now force-written (map Updates) even when empty, so removing the env var clears the cap; admin overrides are preserved unchanged. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * style(vram): drop em dash from worker-clear comment Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(vram): add node VRAM budget admin endpoints Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(vram): add node VRAM budget control to the node UI Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(vram): expose set_node_vram_budget MCP admin tool Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs(vram): document LOCALAI_VRAM_BUDGET and node VRAM budget UI Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(vram): avoid double-applying VRAM budget in GetResourceAggregateInfo The GPU-branch aggregate returned by GetResourceInfo is sourced from GetGPUAggregateInfo, which already caps total/free/used against the process-wide VRAM budget. GetResourceAggregateInfo then applied the budget a second time. For an absolute budget this is idempotent, but for a percentage budget b.Apply resolves the ceiling as a fraction of its input total, so a second pass yields P*(P*T) instead of P*T and distorts UsagePercent (read by the memory reclaimer in pkg/model/watchdog.go). Remove the redundant second application so the budget is applied exactly once, against the raw physical totals, upstream in GetGPUAggregateInfo. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(vram): implement SetNodeVRAMBudget on mcp assistant test stub The LocalAIClient interface gained SetNodeVRAMBudget; the stubClient in core/http/endpoints/mcp used by the assistant tests is a separate implementer and needs the method too (broke golangci-lint typecheck and both test jobs). Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
||
|
LocalAI [bot]andEttore Di Giacinto
|
bcc41219f7 |
feat: materialize Hugging Face model artifacts (#10825)
* feat(config): add model artifact source contract Assisted-by: Codex:GPT-5 [Codex] * feat(downloader): add authenticated raw-byte progress Assisted-by: Codex:GPT-5 [Codex] * feat(huggingface): resolve immutable snapshot manifests Assisted-by: Codex:GPT-5 [Codex] * feat(models): add artifact storage primitives Assisted-by: Codex:GPT-5 [Codex] * feat(models): materialize pinned Hugging Face snapshots Assisted-by: Codex:GPT-5 [Codex] * feat(models): bind managed snapshots at runtime Assisted-by: Codex:GPT-5 [Codex] * feat(gallery): materialize model artifacts during install Assisted-by: Codex:GPT-5 [Codex] * feat(gallery): declare managed Hugging Face artifacts Assisted-by: Codex:GPT-5 [Codex] * feat(models): preload managed model artifacts Assisted-by: Codex:GPT-5 [Codex] * fix(gallery): retain shared artifact caches on delete Assisted-by: Codex:GPT-5 [Codex] * feat(models): report artifact acquisition progress Assisted-by: Codex:GPT-5 [Codex] * refactor(backends): load managed models from ModelFile Assisted-by: Codex:GPT-5 [Codex] * refactor(backends): load staged speech model snapshots Assisted-by: Codex:GPT-5 [Codex] * refactor(backends): use staged snapshots in engine backends Assisted-by: Codex:GPT-5 [Codex] * test(distributed): cover staged artifact snapshots Assisted-by: Codex:GPT-5 [Codex] * docs: explain managed model artifacts Assisted-by: Codex:GPT-5 [Codex] * docs: add product design context Assisted-by: Codex:GPT-5 [Codex] * feat(ui): show model artifact download progress Assisted-by: Codex:GPT-5 [Codex] * Eagerly materialize Hugging Face artifacts Materialize HF-backed model references as managed GGUF artifacts during load, with lazy download retained only as fallback. Assisted-by: Codex:GPT-5 [shell] * Refactor HF downloads through a shared executor Assisted-by: Codex:GPT-5 [shell] * drop Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
||
|
LocalAI [bot]andEttore Di Giacinto
|
4056283aa4 |
[voice] feat: add managed voice cloning profiles (#10799)
* feat(ui): add voice library workflow Give administrators a production-ready flow to record or upload consented reference audio, manage reusable profiles, inspect API usage, discover compatible models, and hand a saved voice directly to text-to-speech. Assisted-by: Codex:gpt-5 * feat(voice): add managed voice cloning profiles Make reusable reference voices manageable through the admin API instead of requiring model-directory and YAML edits. Discover compatible installed and gallery models from server-side backend capabilities, retain explicit model configuration controls, and stage saved references for supported backends. Expose profile management through REST and MCP, document backend-specific behavior, and cover the workflow from profile creation through real Qwen3-TTS synthesis. Harden the agent-job HTTP test against completion racing cancellation. Assisted-by: Codex:gpt-5 --------- Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
||
|
LocalAI [bot]andEttore Di Giacinto
|
b00422e45f |
feat(backends): add LongCat video and avatar generation (#10792)
* feat(backends): add LongCat video and avatar generation Assisted-by: Codex:GPT-5 [apply_patch] [exec_command] [web] * refactor(config): declare model I/O modalities Make model configs declare input and output modalities so capability discovery no longer branches on backend or checkpoint names. Complete the LongCat gallery and user documentation, make the SDPA patch apply to the pinned upstream revision, and stabilize the Agent Jobs race exposed by the required hook. Assisted-by: Codex:GPT-5 [web] --------- Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
||
|
LocalAI [bot]andEttore Di Giacinto
|
5569b2de56 |
feat(config): context_size: -1 to auto-use model's full trained context (#10752)
* feat(config): clamp negative context_size to default in EffectiveContextSize Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * feat(config): resolve context_size=-1 to model trained max with VRAM warn Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * fix(config): treat negative context_size as unset when GGUF is unparseable Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * docs(config): document context_size=-1 auto-max Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] * docs(backend): drop em dashes from EffectiveContextSize comment Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-4-8 [Claude Code] --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
||
|
LocalAI [bot]andEttore Di Giacinto
|
40dae953f4 |
feat: interleaved thinking with tool calls (reasoning_content alias + Anthropic thinking blocks) (#10744)
* feat(schema): accept reasoning_content as inbound alias for reasoning Interleaved-thinking clients (cogito, vLLM/DeepSeek-style) emit reasoning_content on assistant turns. Accept it as an inbound alias so reasoning survives the tool-result loop; canonical reasoning wins when both are present. Emission is unchanged (still reasoning). Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(schema): pin interleaved reasoning+tool_calls round-trip Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(openai): pin reachedTokenBudget truncation detection Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(anthropic): add thinking and signature fields to content blocks Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(anthropic): parse inbound thinking blocks into reasoning Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(anthropic): emit thinking blocks with synthetic signature on tool turns Extract buildAnthropicContentBlocks so non-streaming content assembly is unit-testable, and prepend a thinking block (with an opaque synthetic signature) before text/tool_use blocks when the request opts into thinking. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(anthropic): stream thinking_delta and signature_delta before tool_use Extract anthropicStreamSequence so the streaming block order is unit-testable, and emit content_block_start(thinking) -> thinking_delta -> signature_delta -> content_block_stop before the tool_use block sequence when thinking is enabled. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs: add interleaved thinking with tool calls guide Add a features guide describing interleaved thinking: an assistant turn carrying reasoning and tool_calls together, the reasoning-round-trip contract (including the reasoning_content inbound alias and Anthropic thinking blocks with a synthetic signature), per-backend enablement (reasoning_format for llama.cpp, reasoning_parser/tool_call_parser for vLLM/SGLang plus the vLLM auto-config hook), a worked request/response example, and known limitations. Cross-link from model-configuration, text-generation, and openai-functions. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
||
|
Roman Mazurenkoandrvmzes
|
a3fdfbc0d1 |
feat(llama-cpp): add device selection option (#10724)
Allow llama.cpp model configs to select the backend devices used for offload, matching upstream --device behavior so users can exclude a display or debug GPU. Signed-off-by: rvmzes <rvmzes@rvmzess-MacBook-Pro.local> Co-authored-by: rvmzes <rvmzes@rvmzess-MacBook-Pro.local> |
||
|
Richard Palethorpe
|
eb32cd9073 |
feat(realtime): eager blocking pipeline warm-up + /backend/load API (#10662)
Realtime sessions previously lazy-loaded each pipeline sub-model (VAD,
transcription, LLM, TTS) on first use, so every cold session paid a
per-request model-load stall and load errors only surfaced mid-stream.
Warm the whole pipeline eagerly and blockingly at session start
(including the voice-gate speaker-recognition model, which an enforced
gate blocks each utterance on; compaction's summary_model stays lazy
since it only runs off the response path):
- Add backend.PreloadModel / PreloadModelByName as the single load path
for every modality (no transcription special-case; backend-omitted
configs are deprecated).
- The realtime session blocks on Model.Warmup and returns a
model_load_error to the client if any stage fails to load;
updateSession warms in the background. Opt out per pipeline with
pipeline.disable_warmup, exposed as a UI toggle via the
config-metadata registry.
Add a LocalAI-native POST /backend/load (and /v1/backend/load) that
pre-loads a model -- expanding realtime pipelines into their sub-models
-- as the inverse of /backend/shutdown. There is one preload engine
(backend.PreloadStages): the realtime Warmup methods, /backend/load and
the --load-to-memory startup flag all use it, so --load-to-memory now
also expands pipeline models and records load-failure traces. Pipeline
sub-model alias resolution is likewise shared
(ModelConfigLoader.LoadResolvedModelConfig). Surface the endpoint
everywhere an admin manages models:
- MCP admin tool load_model (httpapi + inproc clients, safety/catalog
prompts, catalog/dispatch tests).
- "Load into memory" action in the React models UI.
- Swagger regenerated; docs moved to the general backend-monitor page
since it is not realtime-specific.
Fix a Traces UI crash ("json: unsupported value: -Inf"): audio-snippet
RMS/peak now floor at a finite dBFS, and backend-trace data is sanitized
to drop non-finite floats before marshaling. The sanitizer is
copy-on-write -- it runs on every RecordBackendTrace, so containers are
only re-allocated on the paths that actually changed.
Migrate core/http/openresponses_test.go onto the prebuilt mock-backend
the rest of the http suite already uses -- it was the last spec still
pointing at a real HuggingFace model, so it 404'd wherever no vision
backend was built -- and fix its item_reference specs to send the
spec's "id" field instead of "item_id", which the handler never
accepted.
Assisted-by: Claude:claude-opus-4-8 Claude Code
Signed-off-by: Richard Palethorpe <io@richiejp.com>
|
||
|
LocalAI [bot]andEttore Di Giacinto
|
fe4f425fb5 |
fix: correct scheme/host on self-referential URLs behind an HTTPS reverse proxy (#10482) (#10504)
* fix(http): harden BaseURL proxy scheme/host detection Split comma-separated X-Forwarded-Proto and honor the RFC 7239 Forwarded header so generated links use https behind common reverse-proxy setups. Refs #10482 Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(http): honor explicit external base URL in BaseURL When _external_base_url is set in the request context it dictates the origin (scheme+host+port); the proxy path prefix is still appended. Refs #10482 Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(config): generalize LOCALAI_BASE_URL to ExternalBaseURL LOCALAI_BASE_URL now sets a single instance-wide external base URL used for OAuth callbacks and all self-referential links. A Pre middleware stamps it into the request context for middleware.BaseURL. Refs #10482 Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs: document LOCALAI_BASE_URL and reverse-proxy headers Refs #10482 Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(http): cover parseForwarded edge cases; clarify base-url flag group Adds direct unit coverage for quoted/malformed/multi-element Forwarded headers and regroups the external base URL flag away from auth-only. Refs #10482 Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
||
|
LocalAI [bot]andEttore Di Giacinto
|
066abf82c0 |
feat(llama-cpp): cpu_moe/n_cpu_moe options + generic upstream-flag passthrough (#10490)
* feat(llama-cpp): add main-model cpu_moe/n_cpu_moe options Mirror the existing draft_cpu_moe/draft_n_cpu_moe siblings for the main model, matching upstream --cpu-moe / --n-cpu-moe (common/arg.cpp). Lets users keep MoE expert weights on CPU to manage VRAM on large MoE models. Closes part of #10483 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(llama-cpp): forward unknown '-' options to upstream arg parser Any options: entry starting with '-' is collected and passed verbatim to llama.cpp's own common_params_parse (LLAMA_EXAMPLE_SERVER) at the end of params_parse, so every upstream llama-server flag works without a new hand-wired branch. Passthrough runs last and wins on overlap; n_parallel is snapshotted to survive parser_init's SERVER reset, and help/usage/completion flags are skipped to avoid exiting the backend. Closes #10483 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs(llama-cpp): document cpu_moe/n_cpu_moe and option passthrough Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(llama-cpp): terminate tensor/kv override vectors after passthrough The tensor_buft_overrides padding and the kv/draft override terminators ran before the generic option passthrough, so a passthrough flag (--cpu-moe, --override-tensor, --override-kv, ...) appended a real entry after the null sentinel - tripping the model loader's back().pattern == nullptr assertion (crash) or being silently dropped. Move all three termination/padding blocks to the end of params_parse, after both the named-option loop and common_params_parse have pushed their real entries. Also widen the exit()-flag skip list so --version, --license, --list-devices and --cache-list cannot terminate the backend. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
||
|
Richard Palethorpe
|
3fa7b2955c |
feat(pii): NER tier engine — privacy-filter.cpp backend + NER-centric PII filter (#10360)
Squashed feat/pii-ner-tier-engine rebased onto master (was 45 commits; see backup/pii-ner-tier-engine-prerebase). Net change: - privacy-filter.cpp: standalone GGML engine for the openai-privacy-filter PII/NER token classifier, wired as a LocalAI gRPC backend (CPU/CUDA/Vulkan). TokenClassify moves off the patched llama.cpp path onto this backend. - PII filter reworked to be NER-centric (encoder/NER detection tier scanning whole conversations as one document), with a recreated bounded restricted- regex secret-matching pattern detector tier alongside it (per-model pii_detection.builtins / .patterns + core/services/routing/piipattern). - Detection labelled by source (ner vs pattern); backend trace / confidence / debug observability; analyze/redact exposed as a synchronous API. - Instance-wide default detector policy + per-usecase default-on; request filtering extended to completions, embeddings, edits & Ollama. - React UI: NER-centric PII editor, detector-models table, pattern/builtins editor, middleware default-policy UI. - Gallery: privacy-filter-multilingual token-classify model + NER install filter; token_classify known_usecase; batch sized to context for NER models. privacy-filter backend registered in the backend gallery (cpu/vulkan/cuda-13 meta + image entries with a capabilities map) matching its CI matrix jobs, and an /import-model auto-detect importer (PrivacyFilterImporter, narrow privacy-filter GGUF detection) replacing the prior pref-only registration. Reconciled against master's independent evolution: - Dropped master's PIIPatternOverrides feature (global-pattern runtime overrides + /api/pii/patterns API + runtime_settings.json persistence). The per-model NER + pattern-detector design supersedes it; it was built on the global redactor pattern set this branch replaced. - Reverted the llama.cpp Score carry-patch (0006-server-task-type-score): removed the patch and restored master's grpc-server.cpp Score RPC (direct llama_decode, slot-loop bypass) and LLAMA_VERSION pin, plus master's model_config validation forbidding score + chat/completion/embeddings on llama-cpp. token_classify is unaffected (it runs on the privacy-filter backend, not llama-cpp). Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Richard Palethorpe <io@richiejp.com> |
||
|
LocalAI [bot]andEttore Di Giacinto
|
1ab61a0875 |
feat: generic chat_template_kwargs (model config + per-request metadata) (#10359)
* feat(config): add chat_template_kwargs model field + resolver Adds the ChatTemplateKwargs model-config map and RequestMetadata carrier, plus ResolveChatTemplateKwargs which layers the config map under coerced request metadata. Foundation for generic jinja chat-template kwargs (issue #10329). Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(backend): forward resolved chat_template_kwargs blob to backends gRPCPredictOpts now merges per-request client metadata over the server-derived enable_thinking/reasoning_effort (reaching all backends via the standalone keys) and serialises the resolved chat_template_kwargs map into a JSON blob for llama.cpp, written last so a client cannot clobber it. Issue #10329. Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(http): wire request metadata to config.RequestMetadata The OpenAI request metadata field was parsed but unused; stamp it onto the per-request ModelConfig so gRPCPredictOpts forwards it as chat_template_kwargs overrides. Issue #10329. Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(llama-cpp): generic chat_template_kwargs merge (drop per-key blocks) Replace the per-key enable_thinking/reasoning_effort handling in both the streaming and non-streaming chat paths with a single block that parses the chat_template_kwargs JSON blob resolved by the Go layer and merges every key into body_json. New jinja template levers (e.g. preserve_thinking) now need no C++ change. Issue #10329. Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs: document custom chat_template_kwargs (model + per-request) Issue #10329. Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(backend): pin reasoning_effort as a string in the chat_template_kwargs blob Issue #10329. Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(http): e2e guard pinning chat_template_kwargs forwarded to gRPC Adds an ECHO_PREDICT_METADATA marker to the mock-backend that echoes the received PredictOptions.Metadata, and an app_test.go spec that drives a real /v1/chat/completions request (model chat_template_kwargs + per-request metadata override) and asserts the exact metadata + chat_template_kwargs blob the REST layer forwards to gRPC. Locks the REST->gRPC contract against regressions. Issue #10329. Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * test(config): grandfather chat_template_kwargs in registry coverage chat_template_kwargs is a free-form map[string]any (like engine_args, already on the list), not a scalar the config UI registry can surface, so it is exempt from the registry-entry requirement. Fixes the TestAllFieldsHaveRegistryEntries failure introduced by the new field. Issue #10329. Assisted-by: Claude:claude-opus-4-8 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
||
|
LocalAI [bot]andEttore Di Giacinto
|
f618636c71 |
docs: fix broken relref to realtime page (#10255)
Hugo fails the gh-pages build with REF_NOT_FOUND because the relref in model-configuration.md uses the 'docs/' prefix; refs are resolved relative to content/, so the page lives at 'features/openai-realtime' (as the other ref in the same file already uses). Assisted-by: Claude Code:claude-fable-5 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
||
|
LocalAI [bot]andEttore Di Giacinto
|
e837921c2c |
feat: forward reasoning_effort to the backend so jinja models honor it (#10184)
* feat: forward reasoning_effort to the backend so jinja models honor it reasoning_effort was only mapped to the binary enable_thinking toggle and otherwise reached Go-side templates — it was never sent to the backend. So jinja-templated models whose chat template keys on reasoning_effort (gpt-oss Harmony, LFM2.5) could not be driven by it: LFM2.5 ignores enable_thinking and kept emitting <think>. Forward the effective reasoning_effort to the backend as a chat_template_kwarg (mirroring enable_thinking) in grpc-server.cpp, and put it in PredictOptions metadata (gRPCPredictOpts). Add a config-level default: ModelConfig.reasoning_effort and Pipeline.reasoning_effort, resolved by ModelConfig.ApplyReasoningEffort (request value overrides config default, none->disable / level->enable, an operator's reasoning.disable wins). request.go now uses that helper. Assisted-by: Claude:claude-opus-4-8 go test, golangci-lint Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(realtime): set the pipeline LLM's reasoning_effort Apply Pipeline.ReasoningEffort to the pipeline's LLM config when the realtime model is built (per-session copy, overrides the LLM's own reasoning_effort), and surface the resolved effort on the template input so Go-templated models get it too. jinja models receive it via the backend metadata. This lets a realtime pipeline disable thinking on models that only honor reasoning_effort (e.g. LFM2.5), which enable_thinking can't. Assisted-by: Claude:claude-opus-4-8 go test, golangci-lint Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
||
|
LocalAI [bot]andEttore Di Giacinto
|
7e59a5c7c5 |
docs: architecture & feature diagrams (blueprint style) (#10137)
* docs: add 'how LocalAI works' architecture diagram Add a blueprint-style architecture diagram: clients -> small core (API, router, WebUI, agents) -> gRPC -> backend processes pulled on demand as OCI images. Place it on the overview page and replace the stale external architecture image on the reference page. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs: add blueprint diagrams across feature, distributed & getting-started docs Add 24 architecture/flow/comparison diagrams (PNG + HTML source) under docs/static/images/diagrams/, wired into their docs pages, from an impact-vs-effort audit of the docs. Broaden the API surface on the overview architecture diagram (OpenAI, Anthropic, ElevenLabs, Ollama, and LocalAI's own API) and move the gRPC boundary label clear of the arrows. Pages: distributed mode (architecture, scheduling, ds4 layer-split), distributed inferencing, MLX, realtime, quantization, MCP, agents, mitm & cloud proxy, middleware, reverse-proxy TLS, VRAM, voice & face recognition, reranker, function calling, fine-tuning (recipe + jobs), diarization, audio transform, quickstart, model resolution. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs: add composable-core diagram to README hero Commit the composable-core card (small core + on-demand backend tiles) alongside the other diagrams and reference it from the README hero via a repo-relative path, so it renders on GitHub. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * docs: fix composable-core connectors/badge and federated-vs-worker layout - composable-core: thicken the plug-in connectors so they read clearly, and widen the SEPARATE IMAGE badge so its text no longer overflows the box. - federated-vs-worker: shorten the WHOLE/SPLIT REQUEST pills to fit, and replace the tangled node-to-node activation arrows with a clean fan-out (request split across all sharded nodes), mirroring the federated panel. Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
||
|
Zhao73
|
c61838dba6 |
docs: fix documentation typos (#10125)
Correct clear spelling mistakes in documentation without changing behavior. Confidence: high Scope-risk: narrow Tested: git diff --check; uvx codespell on changed files Not-tested: Full docs build not run; text-only changes Assisted-by: Codex:gpt-5 codespell |
||
|
LocalAI [bot]andEttore Di Giacinto
|
4a2cc64d07 |
feat(reasoning): honor per-request reasoning_effort on chat completions (#10082)
The OpenAI `reasoning_effort` field only reached the prompt template; it never toggled the backend's thinking. Map it onto ReasoningConfig.DisableReasoning (which becomes the enable_thinking gRPC metadata) in the request merge, so reasoning_effort="none" disables reasoning per request: the use case from #10072 (run a single Qwen3-style model and turn reasoning off for low-latency tasks while keeping it on for others). Effort levels (minimal/low/medium/high) enable thinking unless the model config explicitly disabled it (reasoning.disable: true wins and is never re-enabled by a request); "none" always disables. Closes #10072 Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
||
|
LocalAI [bot]andEttore Di Giacinto
|
959de86761 |
feat(llama-cpp): make server-side prompt cache work by default (#9925)
Aligns LocalAI's llama-cpp gRPC backend with upstream's auto-on prompt cache path so repeated system prompts (agents, OpenAI/Anthropic-compatible CLIs, coding assistants) skip prefill on subsequent calls without any YAML changes. Reported in #9921. Upstream's server enables `kv_unified=true` (and bumps `n_parallel` to 4) when slot count is auto, which unlocks `cache_idle_slots`. LocalAI hardcodes `n_parallel=1` and so far also hardcoded `kv_unified=false`, which silently force-disables idle-slot saving at server init. The host prompt cache was allocated but never written across requests. Changes in backend/cpp/llama-cpp/grpc-server.cpp: - params.kv_unified: false -> true (single-slot path now benefits from the prompt cache; users can opt out with `kv_unified:false`) - params.n_ctx_checkpoints: 8 -> 32 (match upstream default) - params.cache_idle_slots = true initialized explicitly (upstream default) - params.checkpoint_every_nt = 8192 initialized explicitly (upstream default) - New option parsers: cache_idle_slots / idle_slots_cache, checkpoint_every_nt / checkpoint_every_n_tokens Docs: - features/text-generation.md: fix misleading `cache_ram` description (it's the host-side prompt cache, not the KV cache), document the kv_unified + cache_ram + cache_idle_slots interaction, add rows for the two newly-exposed options, and add a worked example for the agent/CLI workload from the issue. - advanced/model-configuration.md: mark the legacy `prompt_cache_path` / `prompt_cache_all` / `prompt_cache_ro` YAML fields as unused by the llama-cpp gRPC backend (they target upstream's CLI completion tool and are not consumed by grpc-server.cpp) and point readers at the new prompt-cache explainer. Closes #9921 Assisted-by: claude:opus-4.7 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
||
|
LocalAI [bot]andEttore Di Giacinto
|
d77a9137d8 |
feat(llama-cpp): bump to MTP-merge SHA and automatically set MTP defaults (#9852)
* feat(llama-cpp): bump to MTP-merge SHA and document draft-mtp spec type Update LLAMA_VERSION to 0253fb21 (post ggml-org/llama.cpp#22673 merge, 2026-05-16) to pick up Multi-Token Prediction support. No grpc-server.cpp changes are required: the existing `spec_type` option delegates to upstream's `common_speculative_types_from_names()`, which already accepts the new `draft-mtp` name. The `n_rs_seq` cparam needed by MTP is auto-derived inside `common_context_params_to_llama` from `params.speculative.need_n_rs_seq()`, and when no `draft_model` is set the upstream server builds the MTP context off the target model itself. Docs: extend the speculative-decoding section of the model-configuration guide with the new type, both load paths (MTP head embedded in the main GGUF vs. separate `mtp-*.gguf` sibling), the PR's recommended `spec_n_max:2-3`, and the chained `draft-mtp,ngram-mod` recipe. Also notes that the upstream `-hf` auto-discovery of `mtp-*.gguf` siblings is not wired through LocalAI's gRPC layer. Agent guide: short note explaining that new upstream spec types are picked up automatically and that MTP needs no gRPC plumbing. Assisted-by: Claude:claude-opus-4-7 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat(llama-cpp): auto-detect MTP heads and enable draft-mtp on import + load Detect upstream's `<arch>.nextn_predict_layers` GGUF metadata key (set by `convert_hf_to_gguf.py` for Qwen3.5/3.6 family models and similar) and, when present and the user has not configured a `spec_type` explicitly, auto-append the upstream-recommended speculative-decoding tuple: - spec_type:draft-mtp - spec_n_max:6 - spec_p_min:0.75 The 0.75 p_min is pinned defensively because upstream marks the current default with a "change to 0.0f" TODO; locking it here keeps acceptance thresholds stable across future llama.cpp bumps. Detection runs in two places: - The model importer (`POST /models/import-uri`, the `/import-model` UI) range-fetches the GGUF header for HuggingFace / direct-URL imports via `gguf.ParseGGUFFileRemote`, with a 30s timeout and non-fatal error handling. OCI/Ollama URIs are skipped because the artifact is not directly streamable; the load-time hook covers them once the file is on disk. - The llama-cpp load-time hook (`guessGGUFFromFile`) reads the local header on every model start and appends the same options if `spec_type` is not already set. Both paths share `ApplyMTPDefaults` and respect an explicit user-set `spec_type:` / `speculative_type:` so YAML overrides win. Ginkgo specs cover the append, preserve-user-choice, legacy alias, and nil safety paths. Assisted-by: Claude:claude-opus-4-7 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(importer): resolve huggingface:// URIs before MTP header probe `gguf.ParseGGUFFileRemote` only speaks HTTP(S), but the importer was handing it the raw `huggingface://...` URI directly (and similarly for any other custom downloader scheme). Live-test against `huggingface://ggml-org/Qwen3.6-27B-MTP-GGUF/Qwen3.6-27B-MTP-Q8_0.gguf` exposed this: the probe failed with `unsupported protocol scheme "huggingface"`, was caught by the non-fatal error path, and the MTP options were silently never applied to the generated YAML. Route every candidate URI through `downloader.URI.ResolveURL()` and require the resolved form to be HTTP(S). After the fix the probe successfully reads `<arch>.nextn_predict_layers=1` from the real HF GGUF and the emitted ConfigFile carries spec_type:draft-mtp, spec_n_max:6, spec_p_min:0.75 as intended. Assisted-by: Claude:claude-opus-4-7 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io> |
||
|
LocalAI [bot]andEttore Di Giacinto
|
6e1dbae256 |
feat(llama-cpp): expose 12 missing common_params via options[] (#9814)
The llama.cpp backend already accepts a free-form options: array in the
model config that maps to common_params fields, but a coverage audit
against upstream pin 7f3f843c flagged 12 user-visible knobs that were
neither set via the typed proto fields nor reachable via options:.
Wire them up under the existing if/else chain in params_parse, before
the speculative section. Each new option follows the file's prevailing
patterns (try/catch around numeric parses, the same true/1/yes/on bool
form used elsewhere, hardware_concurrency() fallback for thread counts,
mirror of draft_override_tensor for override_tensor).
Top-level / batching / IO:
- n_ubatch (alias ubatch) -- physical batch size; was previously
force-aliased to n_batch at line 482, blocking embedding/rerank
workloads that need independent control
- threads_batch (alias n_threads_batch) -- main-model batch threads;
mirrors the existing draft_threads_batch
- direct_io (alias use_direct_io) -- O_DIRECT model loads
- verbosity -- llama.cpp log threshold (line 479 had this commented
out)
- override_tensor (alias tensor_buft_overrides) -- per-tensor buffer
overrides for the main model; mirrors draft_override_tensor
Embedding / multimodal:
- pooling_type (alias pooling) -- mean/cls/last/rank/none; previously
only auto-flipped to RANK for rerankers
- embd_normalize (alias embedding_normalize) -- and the embedding
handler now reads params_base.embd_normalize instead of a hardcoded
2 at the previous embd_normalize literal in Embedding()
- mmproj_use_gpu (alias mmproj_offload) -- mmproj on CPU vs GPU
- image_min_tokens / image_max_tokens -- per-image vision token budget
Reasoning surface (the audit-focus three; LocalAI's existing
ReasoningConfig.DisableReasoning only feeds the per-request
chat_template_kwargs.enable_thinking and does not touch any of these):
- reasoning_format -- none/auto/deepseek/deepseek-legacy parser
- enable_reasoning (alias reasoning_budget) -- -1/0/>0 thinking budget
- prefill_assistant -- trailing-assistant-message prefill toggle
All 14 referenced fields exist on both the upstream pin and the
turboquant fork's common.h, so no LOCALAI_LEGACY_LLAMA_CPP_SPEC guard
is needed.
Docs: extend model-configuration.md with new "Reasoning Models",
"Multimodal Backend Options", "Embedding & Reranking Backend Options",
and "Other Backend Tuning Options" subsections; also refresh the
Speculative Type Values table to show the new dash-separated canonical
names alongside the underscore aliases LocalAI still accepts.
Assisted-by: claude-code:claude-opus-4-7
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
|
||
|
LocalAI [bot]andEttore Di Giacinto
|
bc4cd3dd85 |
feat(llama-cpp): bump to 1ec7ba0c, adapt grpc-server, expose new spec-decoding options (#9765)
* chore(llama.cpp): bump to 1ec7ba0c14f33f17e980daeeda5f35b225d41994
Picks up the upstream `spec : parallel drafting support` change
(ggml-org/llama.cpp#22838) which reshapes the speculative-decoding API
and `server_context_impl`.
Adapt the grpc-server wrapper accordingly:
* `common_params_speculative::type` (single enum) became `types`
(`std::vector<common_speculative_type>`). Update both the
"default to draft when a draft model is set" branch and the
`spec_type`/`speculative_type` option parser. The parser now also
tolerates comma-separated lists, mirroring the upstream
`common_speculative_types_from_names` semantics.
* `common_params_speculative_draft::n_ctx` is gone (draft now shares
the target context size). Keep the `draft_ctx_size` option name for
backward compatibility and ignore the value rather than failing.
* `server_context_impl::model` was renamed to `model_tgt`; update the
two reranker / model-metadata call sites.
Replaces #9763. Builds cleanly under the linux/amd64 cpu-llama-cpp
target locally.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* feat(llama-cpp): expose new speculative-decoding option keys
Upstream `spec : parallel drafting support` (ggml-org/llama.cpp#22838)
adds the `ngram_mod`, `ngram_map_k`, and `ngram_map_k4v` speculative
families and beefs up the draft-model knobs. The previous bump only
adapted the API; this exposes the new fields through the grpc-server
options dictionary so model configs can drive them.
New `options:` keys (all under `backend: llama-cpp`):
ngram_mod (`ngram_mod` type):
spec_ngram_mod_n_min / spec_ngram_mod_n_max / spec_ngram_mod_n_match
ngram_map_k (`ngram_map_k` type):
spec_ngram_map_k_size_n / spec_ngram_map_k_size_m / spec_ngram_map_k_min_hits
ngram_map_k4v (`ngram_map_k4v` type):
spec_ngram_map_k4v_size_n / spec_ngram_map_k4v_size_m /
spec_ngram_map_k4v_min_hits
ngram lookup caches (`ngram_cache` type):
spec_lookup_cache_static / lookup_cache_static
spec_lookup_cache_dynamic / lookup_cache_dynamic
Draft-model tuning (active when `spec_type` is `draft`):
draft_cache_type_k / spec_draft_cache_type_k
draft_cache_type_v / spec_draft_cache_type_v
draft_threads / spec_draft_threads
draft_threads_batch / spec_draft_threads_batch
draft_cpu_moe / spec_draft_cpu_moe (bool flag)
draft_n_cpu_moe / spec_draft_n_cpu_moe (first N MoE layers on CPU)
draft_override_tensor / spec_draft_override_tensor
(comma-separated <tensor regex>=<buffer type>; re-implements upstream's
static parse_tensor_buffer_overrides since it isn't exported)
`spec_type` already accepted comma-separated lists after the previous
commit, matching upstream's `common_speculative_types_from_names`.
Docs: refresh `docs/content/advanced/model-configuration.md` with
per-family tables and a note about multi-type chaining.
Builds locally with `make docker-build-llama-cpp` (linux/amd64
cpu-llama-cpp AVX variant).
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(turboquant): bridge new llama.cpp spec API to the legacy fork layout
The previous commits in this series adapted backend/cpp/llama-cpp/grpc-server.cpp
to the post-#22838 (parallel drafting) llama.cpp API. The turboquant build
reuses the same grpc-server.cpp through backend/cpp/turboquant/Makefile,
which copies it into turboquant-<flavor>-build/ and runs patch-grpc-server.sh
on the copy. The fork branched before the API refactor, so it errors out on:
* `ctx_server.impl->model_tgt` (fork still has `model`)
* `params.speculative.{ngram_mod,ngram_map_k,ngram_map_k4v,ngram_cache}.*`
(none of these sub-structs exist in the fork)
* `params.speculative.draft.{cache_type_k/v, cpuparams[, _batch].n_threads,
tensor_buft_overrides}` (fork uses the pre-#22397 flat layout)
* `params.speculative.types` vector / `common_speculative_types_from_names`
(fork has a scalar `type` and only the singular helper)
Approach:
1. backend/cpp/llama-cpp/grpc-server.cpp: introduce a single feature switch
`LOCALAI_LEGACY_LLAMA_CPP_SPEC`. When defined, the two `speculative.type[s]`
discriminations (the "default to draft when a draft model is set" branch
and the `spec_type` / `speculative_type` option parser) fall back to the
singular scalar form, and the entire new-option block (ngram_mod / map_k
/ map_k4v / ngram_cache / draft.{cache_type_*, cpuparams*,
tensor_buft_overrides}) is preprocessed out. The macro is *not* defined
in the source tree — stock llama-cpp builds get the full new API.
2. backend/cpp/turboquant/patch-grpc-server.sh: two new patch steps applied
to the per-flavor build copy at turboquant-<flavor>-build/grpc-server.cpp:
- substitute `ctx_server.impl->model_tgt` -> `ctx_server.impl->model`
- inject `#define LOCALAI_LEGACY_LLAMA_CPP_SPEC 1` before the first
`#include`, so the guarded blocks above drop out for the fork build.
Both patches are idempotent and follow the existing sed/awk pattern in
this script (KV cache types, `get_media_marker`, flat speculative
renames). Stock llama-cpp's `grpc-server.cpp` is never touched.
Drop both legacy patches once the turboquant fork rebases past
ggml-org/llama.cpp#22397 / #22838.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(turboquant): close draft_ctx_size brace inside legacy guard
The previous turboquant fix wrapped the new option-handler blocks in
`#ifndef LOCALAI_LEGACY_LLAMA_CPP_SPEC ... #endif` but placed the guard
in the middle of an `else if` chain — the `} else if` openings of the
new blocks were responsible for closing the previous block's brace.
With the macro defined the new blocks vanish, draft_ctx_size's `{`
loses its closer, the for-loop's `}` is consumed instead, and the
file ends with a stray opening brace — clang reports it as
`function-definition is not allowed here before '{'` on the next
top-level `int main(...)` and `expected '}' at end of input`.
Move the chain split inside the draft_ctx_size branch:
} else if (... "draft_ctx_size") {
// ...
#ifdef LOCALAI_LEGACY_LLAMA_CPP_SPEC
} // legacy: chain ends here
#else
} else if (... "spec_ngram_mod_n_min") { // modern: chain continues
...
} else if (... "draft_override_tensor") {
...
} // closes last branch
#endif
} // closes for-loop
Brace count is now balanced under both preprocessor branches (verified
with `tr -cd '{' | wc -c` against the patched and unpatched outputs).
Local `make docker-build-turboquant` builds the linux/amd64 cpu-llama-cpp
`turboquant-avx` variant cleanly.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
* fix(ci): forward AMDGPU_TARGETS into Dockerfile.turboquant builder-prebuilt
Dockerfile.turboquant's `builder-prebuilt` stage was missing the
`ARG AMDGPU_TARGETS` / `ENV AMDGPU_TARGETS=${AMDGPU_TARGETS}` pair that
`builder-fromsource` already has (and that `Dockerfile.llama-cpp`
mirrors across both stages). When CI uses the prebuilt base image
(quay.io/go-skynet/ci-cache:base-grpc-*, the common path) the build-arg
passed by the workflow never reaches the env inside the compile stage.
backend/cpp/llama-cpp/Makefile:38 (introduced by #9626) errors out on
hipblas builds when AMDGPU_TARGETS is empty, and the turboquant
Makefile reuses backend/cpp/llama-cpp via a sibling build dir, so the
same check fires from turboquant-fallback under BUILD_TYPE=hipblas:
Makefile:38: *** AMDGPU_TARGETS is empty — set it to a comma-separated
list of gfx targets e.g. gfx1100,gfx1101. Stop.
make: *** [Makefile:66: turboquant-fallback] Error 2
The bug is latent on master because the docker layer cache stays warm
across builds — the compile step rarely re-runs from scratch. The
llama.cpp bump in this PR invalidates the cache, so the missing env var
becomes load-bearing and the hipblas turboquant CI job fails.
Mirror the existing pattern from Dockerfile.llama-cpp.
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
---------
Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
|
||
|
Ettore Di Giacinto
|
bbcaebc1ef |
feat(concurrency-groups): per-model exclusive groups for backend loading (#9662)
* feat(concurrency-groups): per-model exclusive groups for backend loading Adds `concurrency_groups: [...]` to model YAML configs. Two models that share a group cannot be loaded concurrently on the same node — loading one evicts the others, reusing the existing pinned/busy/retry policy from LRU eviction. Layered design: - Watchdog (pkg/model): per-node correctness floor — on every Load(), evict any loaded model that shares a group with the requested one. Pinned skips surface NeedMore so the loader retries (and ultimately logs a clear warning), instead of silently allowing the rule to be violated. - Distributed scheduler (core/services/nodes): soft anti-affinity hint — scheduleNewModel prefers nodes that don't already host a same-group model, falling back to eviction only if every candidate has a conflict. Composes with NodeSelector at the same point in the candidate pipeline. Per-node, not cluster-wide: VRAM is a node-local resource, and two heavy models running on different nodes is fine. The ConfigLoader is wired into SmartRouter via a small ConcurrencyConflictResolver interface so the nodes package keeps a narrow surface on core/config. Refactors the inner LRU eviction body into a shared collectEvictionsLocked helper and the loader retry loop into retryEnforce(fn, maxRetries, interval), so both LRU and group enforcement share busy/pinned/retry semantics. Closes #9659. Assisted-by: Claude:claude-opus-4-7 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(watchdog): sync pinned + concurrency_groups at startup The startup-time watchdog setup lives in initializeWatchdog (startup.go), not in startWatchdog (watchdog.go). The latter is only invoked from the runtime-settings RestartWatchdog path. As a result, neither SyncPinnedModelsToWatchdog nor SyncModelGroupsToWatchdog ran at boot, so `pinned: true` and `concurrency_groups: [...]` only became effective after a settings-driven watchdog restart. Fix by adding both sync calls to initializeWatchdog. Confirmed end-to-end: loading model A in group "heavy", then C with no group (coexists), then B in group "heavy" now correctly evicts A and leaves [B, C]. Assisted-by: Claude:claude-opus-4-7 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fix(test): satisfy errcheck on new os.Remove in concurrency_groups spec CI lint runs new-from-merge-base, so the existing pre-existing `defer os.Remove(tmp.Name())` lines are baseline-grandfathered but the one introduced by the concurrency_groups YAML round-trip test is held to errcheck. Wrap the remove in a closure that discards the error. Assisted-by: Claude:claude-opus-4-7 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
||
|
Ettore Di Giacinto
|
95efb8a562 |
feat(backend): add turboquant llama.cpp-fork backend (#9355)
* feat(backend): add turboquant llama.cpp-fork backend
turboquant is a llama.cpp fork (TheTom/llama-cpp-turboquant, branch
feature/turboquant-kv-cache) that adds a TurboQuant KV-cache scheme.
It ships as a first-class backend reusing backend/cpp/llama-cpp sources
via a thin wrapper Makefile: each variant target copies ../llama-cpp
into a sibling build dir and invokes llama-cpp's build-llama-cpp-grpc-server
with LLAMA_REPO/LLAMA_VERSION overridden to point at the fork. No
duplication of grpc-server.cpp — upstream fixes flow through automatically.
Wires up the full matrix (CPU, CUDA 12/13, L4T, L4T-CUDA13, ROCm, SYCL
f32/f16, Vulkan) in backend.yml and the gallery entries in index.yaml,
adds a tests-turboquant-grpc e2e job driven by BACKEND_TEST_CACHE_TYPE_K/V=q8_0
to exercise the KV-cache config path (backend_test.go gains dedicated env
vars wired into ModelOptions.CacheTypeKey/Value — a generic improvement
usable by any llama.cpp-family backend), and registers a nightly auto-bump
PR in bump_deps.yaml tracking feature/turboquant-kv-cache.
scripts/changed-backends.js gets a special-case so edits to
backend/cpp/llama-cpp/ also retrigger the turboquant CI pipeline, since
the wrapper reuses those sources.
* feat(turboquant): carry upstream patches against fork API drift
turboquant branched from llama.cpp before upstream commit 66060008
("server: respect the ignore eos flag", #21203) which added the
`logit_bias_eog` field to `server_context_meta` and a matching
parameter to `server_task::params_from_json_cmpl`. The shared
backend/cpp/llama-cpp/grpc-server.cpp depends on that field, so
building it against the fork unmodified fails.
Cherry-pick that commit as a patch file under
backend/cpp/turboquant/patches/ and apply it to the cloned fork
sources via a new apply-patches.sh hook called from the wrapper
Makefile. Simplifies the build flow too: instead of hopping through
llama-cpp's build-llama-cpp-grpc-server indirection, the wrapper now
drives the copied Makefile directly (clone -> patch -> build).
Drop the corresponding patch whenever the fork catches up with
upstream — the build fails fast if a patch stops applying, which
is the signal to retire it.
* docs: add turboquant backend section + clarify cache_type_k/v
Document the new turboquant (llama.cpp fork with TurboQuant KV-cache)
backend alongside the existing llama-cpp / ik-llama-cpp sections in
features/text-generation.md: when to pick it, how to install it from
the gallery, and a YAML example showing backend: turboquant together
with cache_type_k / cache_type_v.
Also expand the cache_type_k / cache_type_v table rows in
advanced/model-configuration.md to spell out the accepted llama.cpp
quantization values and note that these fields apply to all
llama.cpp-family backends, not just vLLM.
* feat(turboquant): patch ggml-rpc GGML_OP_COUNT assertion
The fork adds new GGML ops bringing GGML_OP_COUNT to 97, but
ggml/include/ggml-rpc.h static-asserts it equals 96, breaking
the GGML_RPC=ON build paths (turboquant-grpc / turboquant-rpc-server).
Carry a one-line patch that updates the expected count so the
assertion holds. Drop this patch whenever the fork fixes it upstream.
* feat(turboquant): allow turbo* KV-cache types and exercise them in e2e
The shared backend/cpp/llama-cpp/grpc-server.cpp carries its own
allow-list of accepted KV-cache types (kv_cache_types[]) and rejects
anything outside it before the value reaches llama.cpp's parser. That
list only contains the standard llama.cpp types — turbo2/turbo3/turbo4
would throw "Unsupported cache type" at LoadModel time, meaning
nothing the LocalAI gRPC layer accepted was actually fork-specific.
Add a build-time augmentation step (patch-grpc-server.sh, called from
the turboquant wrapper Makefile) that inserts GGML_TYPE_TURBO2_0/3_0/4_0
into the allow-list of the *copied* grpc-server.cpp under
turboquant-<flavor>-build/. The original file under backend/cpp/llama-cpp/
is never touched, so the stock llama-cpp build keeps compiling against
vanilla upstream which has no notion of those enum values.
Switch test-extra-backend-turboquant to set
BACKEND_TEST_CACHE_TYPE_K=turbo3 / _V=turbo3 so the e2e gRPC suite
actually runs the fork's TurboQuant KV-cache code paths (turbo3 also
auto-enables flash_attention in the fork). Picking q8_0 here would
only re-test the standard llama.cpp path that the upstream llama-cpp
backend already covers.
Refresh the docs (text-generation.md + model-configuration.md) to
list turbo2/turbo3/turbo4 explicitly and call out that you only get
the TurboQuant code path with this backend + a turbo* cache type.
* fix(turboquant): rewrite patch-grpc-server.sh in awk, not python3
The builder image (ubuntu:24.04 stage-2 in Dockerfile.turboquant)
does not install python3, so the python-based augmentation step
errored with `python3: command not found` at make time. Switch to
awk, which ships in coreutils and is already available everywhere
the rest of the wrapper Makefile runs.
* Apply suggestion from @mudler
Signed-off-by: Ettore Di Giacinto <mudler@users.noreply.github.com>
---------
Signed-off-by: Ettore Di Giacinto <mudler@users.noreply.github.com>
|
||
|
Ettore Di Giacinto
|
06fbe48b3f |
feat(llama.cpp): wire speculative decoding settings (#9238)
Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
||
|
|
f73a158153 |
docs: Document GPU auto-fit mode limitations and trade-offs (closes #8562) (#8954)
* docs: Add documentation about GPU auto-fit mode limitations (closes #8562) - Document the default gpu_layers behavior (9999999) that disables auto-fit - Explain the trade-off between auto-fit and VRAM threshold unloading - Add recommendations for users who want to enable gpu_layers: -1 - Note known issues with tensor_buft_override buffer errors - Link to issue #8562 for future improvements Signed-off-by: team-coding-agent-1 <team-coding-agent-1@localai.dev> * Apply suggestion from @mudler Signed-off-by: Ettore Di Giacinto <mudler@users.noreply.github.com> --------- Signed-off-by: team-coding-agent-1 <team-coding-agent-1@localai.dev> Signed-off-by: Ettore Di Giacinto <mudler@users.noreply.github.com> Co-authored-by: team-coding-agent-1 <team-coding-agent-1@localai.dev> Co-authored-by: Ettore Di Giacinto <mudler@users.noreply.github.com> |
||
|
LocalAI [bot]andlocalai-bot
|
199fe89cfe |
feat: Expand section index pages with comprehensive navigation (M7) (#8929)
feat: expand section index pages with comprehensive navigation (M7) Co-authored-by: localai-bot <localai-bot@noreply.github.com> |
||
|
Ettore Di Giacinto
|
580517f9db |
feat: pass-by metadata to predict options (#8795)
Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
||
|
LocalAI [bot]andlocalai-bot
|
b260378694 |
docs: add TLS reverse proxy configuration guide (#8673)
* docs: add TLS reverse proxy configuration guide Add documentation explaining how to use LocalAI behind a TLS termination reverse proxy (HAProxy, Apache, Nginx). The documentation covers: - How LocalAI detects HTTPS via X-Forwarded-Proto header - Required headers that must be forwarded - Configuration examples for HAProxy, Apache, and Nginx - Sub-path serving configuration - Testing and troubleshooting guide Fixes: Issue #7176 - Web UI broken behind TLS reverse proxy Signed-off-by: localai-bot <localai-bot@users.noreply.github.com> * docs: remove non-existent --base-url option from sub-path section --------- Signed-off-by: localai-bot <localai-bot@users.noreply.github.com> Co-authored-by: localai-bot <localai-bot@users.noreply.github.com> |
||
|
Richard Palethorpe
|
dd8e74a486 |
feat(realtime): Add audio conversations (#6245)
* feat(realtime): Add audio conversations Signed-off-by: Richard Palethorpe <io@richiejp.com> * chore(realtime): Vendor the updated API and modify for server side Signed-off-by: Richard Palethorpe <io@richiejp.com> * feat(realtime): Update to the GA realtime API Signed-off-by: Richard Palethorpe <io@richiejp.com> * chore: Document realtime API and add docs to AGENTS.md Signed-off-by: Richard Palethorpe <io@richiejp.com> * feat: Filter reasoning from spoken output Signed-off-by: Richard Palethorpe <io@richiejp.com> * fix(realtime): Send delta and done events for tool calls and audio transcripts Ensure that content is sent in both deltas and done events for function call arguments and audio transcripts. This fixes compatibility with clients that rely on delta events for parsing. 💘 Generated with Crush Signed-off-by: Richard Palethorpe <io@richiejp.com> * fix(realtime): Improve tool call handling and error reporting - Refactor Model interface to accept []types.ToolUnion and *types.ToolChoiceUnion instead of JSON strings, eliminating unnecessary marshal/unmarshal cycles - Fix Parameters field handling: support both map[string]any and JSON string formats - Add PredictConfig() method to Model interface for accessing model configuration - Add comprehensive debug logging for tool call parsing and function config - Add missing return statement after prediction error (critical bug fix) - Add warning logs for NoAction function argument parsing failures - Improve error visibility throughout generateResponse function 💘 Generated with Crush Assisted-by: Claude Sonnet 4.5 via Crush <crush@charm.land> Signed-off-by: Richard Palethorpe <io@richiejp.com> --------- Signed-off-by: Richard Palethorpe <io@richiejp.com> |
||
|
Ettore Di Giacinto
|
c491c6ca90 |
feat(openresponses): Support reasoning blocks (#8133)
* feat(openresponses): support reasoning blocks Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * allow to disable reasoning, refactor common logic Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Add option to only strip reasoning Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Add configurations for custom reasoning tokens Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
||
|
Ettore Di Giacinto
|
c844b7ac58 |
feat: disable force eviction (#7725)
* feat: allow to set forcing backends eviction while requests are in flight Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * feat: try to make the request sit and retry if eviction couldn't be done Otherwise calls that in order to pass would need to shutdown other backends would just fail. In this way instead we make the request sit and retry eviction until it succeeds. The thresholds can be configured by the user. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * add tests Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * expose settings to CLI Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Update docs Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
||
|
Ettore Di Giacinto
|
fc5b9ebfcc |
feat(loader): enhance single active backend to support LRU eviction (#7535)
* feat(loader): refactor single active backend support to LRU This changeset introduces LRU management of loaded backends. Users can set now a maximum number of models to be loaded concurrently, and, when setting LocalAI in single active backend mode we set LRU to 1 for backward compatibility. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * chore: add tests Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Update docs Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Fixups Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
||
|
Ettore Di Giacinto
|
2dd42292dc |
feat(ui): runtime settings (#7320)
* feat(ui): add watchdog settings Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Do not re-read env Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Some refactor, move other settings to runtime (p2p) Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Add API Keys handling Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Allow to disable runtime settings Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Documentation Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Small fixups Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * show MCP toggle in index Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Drop context default Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
||
|
Ettore Di Giacinto
|
2cc4809b0d |
feat: docs revamp (#7313)
* docs Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Small enhancements Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * Enhancements * Default to zen-dark Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * fixups Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> |
||
|
Ettore Di Giacintoandlunamidori5
|
6ca4d38a01 |
docs/examples: enhancements (#1572)
* docs: re-order sections * fix references * Add mixtral-instruct, tinyllama-chat, dolphin-2.5-mixtral-8x7b * Fix link * Minor corrections * fix: models is a StringSlice, not a String Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * WIP: switch docs theme * content * Fix GH link * enhancements * enhancements * Fixed how to link Signed-off-by: lunamidori5 <118759930+lunamidori5@users.noreply.github.com> * fixups * logo fix * more fixups * final touches --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Signed-off-by: lunamidori5 <118759930+lunamidori5@users.noreply.github.com> Co-authored-by: lunamidori5 <118759930+lunamidori5@users.noreply.github.com> |
||
|
Ettore Di Giacinto
|
ce724a7e55 |
docs: improve getting started (#1553)
* docs: improve getting started Signed-off-by: Ettore Di Giacinto <mudler@users.noreply.github.com> * cleanups * Use dockerhub links * Shrink command to minimum --------- Signed-off-by: Ettore Di Giacinto <mudler@users.noreply.github.com> |
||
|
Ettore Di Giacinto
|
09e5d9007b |
feat: embedded model configurations, add popular model examples, refactoring (#1532)
* move downloader out * separate startup functions for preloading configuration files * docs: add popular model examples Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * shorteners * Add llava * Add mistral-openorca * Better link to build section * docs: update * fixup * Drop code dups * Minor fixups * Apply suggestions from code review Signed-off-by: Ettore Di Giacinto <mudler@users.noreply.github.com> * ci: try to cache gRPC build during tests Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * ci: do not build all images for tests, just necessary * ci: cache gRPC also in release pipeline * fixes * Update model_preload_test.go Signed-off-by: Ettore Di Giacinto <mudler@users.noreply.github.com> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Signed-off-by: Ettore Di Giacinto <mudler@users.noreply.github.com> |
||
|
Ettore Di Giacinto
|
db926896bd |
Revert "[Refactor]: Core/API Split" (#1550)
Revert "[Refactor]: Core/API Split (#1506)"
This reverts commit
|
||
|
Dave
|
ab7b4d5ee9 |
[Refactor]: Core/API Split (#1506)
Refactors api folder to core, creates firm split between backend code and api frontend. |