A row click slides the pane in as a fixed-width drawer: the toolbar's
calls to action leave first, the pane is laid out before its first open,
and a model switch fades one out and the next in. The model information
dialog folds into the pane's information tab, and a chat started from
the pane closes the manager and focuses the composer.
Assisted-by: pi:llama.cpp/DeepSeek-V4.1-Flash
The pane shows a model's information, load and inference settings side by
side: what its provider reports, the Hub records it falls back to, the
load controls gated by what the serving backend can do, and the
per-model overrides the load form writes. The slider control arrives
with it.
Assisted-by: pi:llama.cpp/DeepSeek-V4.1-Flash
Adds a measured-px height transition to the tool category groups in the
Tools submenu, replacing the bits-ui Collapsible whose conditional
rendering kills the transition. The same technique as the reasoning panel:
the rows stay mounted, the height animates between 0 and the measured
scrollHeight, and visibility keeps collapsed rows out of the tab order.
Assisted-by: pi:GLM-5.3-Flash
Serve the recommended MCP server favicons and the Hugging Face badge from
the app's base path, not the domain root, so they resolve when the app is
mounted under a path. Also normalizes the safe HTML config indentation.
Assisted-by: pi:GLM-5.3-Flash
The reasoning level becomes its own control next to the selector, and
the add menu drops its reasoning submenu.
Assisted-by: pi:llama.cpp/DeepSeek-V4.1-Flash
The selector keeps working when a provider is down: the trigger shows the
provider's mark and the org avatar, the banner only appears when no
backend is enabled, and the model ids read through their settings. The
model mark becomes MODEL_ICON everywhere.
Assisted-by: pi:llama.cpp/DeepSeek-V4.1-Flash
The selector surfaces move under models/ModelsSelector with their own
barrel: the dropdown and sheet relocate, the list, option and trigger icon
split out, and the shared list helpers join the navigation utils. The
searchable dropdown gains a sticky footer and per-surface class hooks.
Assisted-by: pi:llama.cpp/DeepSeek-V4.1-Flash
The table grows a section per enabled provider, each with its own mark,
error and loading state, and a provider filter with live repo counts.
Rows gain their capability gates back, the draft column with its
use-as-draft action, and the quant badge names the provider on its rows.
Assisted-by: pi:llama.cpp/DeepSeek-V4.1-Flash
A provider is added from a preset card or by hand: the dialog probes the
endpoint, reads a refusal as a llama.cpp server that wants a key, and
shows the connection test as a status block. Presets carry their official
artwork and a saved backend keeps its branding through the favicon
fallback. The manager dialog gains its third view, and the settings save
stops clobbering keys that other surfaces write.
Assisted-by: pi:llama.cpp/DeepSeek-V4.1-Flash
Chat goes through protocol adapters, so an OpenAI-compatible endpoint
speaks its own wire format: per-backend paths and headers, the model on
the request, tools kept on the local server, and token counts synthesized
for endpoints that do not stream their own timings. The server store keeps
the local server's props while another provider is active, a conversation
resolves the provider its model belongs to before sending, and the chat
screen never blocks on the local probe when the install has none.
Assisted-by: pi:llama.cpp/DeepSeek-V4.1-Flash
A provider is a server entry with a base url, an optional key, a protocol
and the paths its API lives at; the local llama.cpp server is the built-in
one. Backends persist in settings and the active one is restored on load.
Requests resolve against the active backend, model ids become
backend-qualified, and every backend's model list is fetched and cached in
the background. The manager's helpers learn to read a model's drafts,
context and the provider that serves it.
Assisted-by: pi:llama.cpp/DeepSeek-V4.1-Flash
The manager dialog gains its second view: the Discover Models call to
action fades the table out, slides the title with its compass mark, and
fades the discover surface in; the arrow button returns to the table.
Assisted-by: pi:llama.cpp/DeepSeek-V4.1-Flash
The Discover surface: the curated-catalog list with its search and
skeletons, the details pane with its readme, metadata, Hub stats and
download options, and the standalone download progress bar that replaces
the plain one. The quant download button sizes its select with a new xs
trigger, so the select gains that size and the muted box look. The store
stays incomplete when a repo fails, so the next mount retries it.
Assisted-by: pi:llama.cpp/DeepSeek-V4.1-Flash
Add an allowHtml prop to MarkdownContent: raw HTML found in the markdown is
rendered after DOMPurify sanitization instead of being escaped as literal
text. Default stays escaped.
Assisted-by: pi:GLM-5.3-Flash
A manage models dialog for the local server: a table that folds each repo's
quants into one row, groups them into families, and sorts from its column
headers. Loaded models lead, then favorites, then the local block, then the
hidden one; sections keep their open state in local storage. The toolbar
filters by capability, modality and context, rows act on favorite, delete
and hide, and the manager opens focused on a model from the sidebar or a
download row. The models store gains the recents, hidden and group-open
state, and warms the Hub records for the context and capability columns.
Assisted-by: pi:llama.cpp/DeepSeek-V4.1-Flash
* ui : shared model display primitives
Extract ModelCapabilityIcons (canonical Tools/Reasoning/Vision/Video/Audio
order) out of ModelId and reuse it there, add the shared DialogConfirmDownload
for destructive download actions, the discover org avatar with dark-mode
inversion and the thin download progress bar, and rework ModelId badges to take
thinking/tool-use support directly.
Assisted-by: pi:GLM-5.3-Flash
* ui : remember hub avatars that failed to load
Assisted-by: pi:llama.cpp/DeepSeek-V4.1-Flash
* ui : render shared model row hints as native titles
Assisted-by: pi:llama.cpp/DeepSeek-V4.1-Flash
* ui : fix badge guard for draft sidecars, keep parameter precision
hasBadges now counts draft sidecar badges, so a sidecar-only model still
renders. Billions keep one decimal for hub counts and stay bare for whole
values. Avatar failures track the org instead of the instance, and the
download progress bar no longer pulses while determinate.
Assisted-by: pi:zai-org/GLM-5.3-Flash
* ui : model download pipeline
Track HuggingFace downloads end to end: the server download/cancel endpoints,
a status manager fed by the /models/sse download progress events, and a
models-discover store holding the catalog and detail state for the discover
view. Downloaded and in-flight entries are excluded from the loadable model
list.
Assisted-by: pi:GLM-5.3-Flash
* ui : route sidecar tag lookup through the sidecars util, validate the paused list
Assisted-by: pi:zai-org/GLM-5.3-Flash
* ui : model memory-fit estimation
Replace the raw runtime-memory estimate with the app's compatibility check:
the smallest real Mac memory tier that fits a model file, budgeted as
RAM x 0.75 minus fixed overhead with headroom on the file size. The constants
move to lib; the unused runtime-memory estimate is dropped. browser-info's
OS detection is exported for reuse.
Assisted-by: pi:GLM-5.3-Flash
* ui : cover the memory-fit and tool-use heuristics in tests
Assisted-by: pi:zai-org/GLM-5.3-Flash
* ui : Hugging Face Hub data layer
Add HuggingFaceService and its constants/enums/types: GGUF repo search, file
tree and model detail fetching, quant/sidecar filename analysis, shard-set
collapsing and the llama.app catalog feed, plus an orgOf() helper on the model
name utils.
Assisted-by: pi:GLM-5.3-Flash
* ui : strip provider tilde prefix from hub avatar urls
Assisted-by: pi:llama.cpp/DeepSeek-V4.1-Flash
* ui : trim redundant comments in the HF data layer service
Per review: drop JSDoc that restates the method name and inline comments
that restate the code; keep only comments carrying non-obvious context.
Assisted-by: pi:zai-org/GLM-5.3-Flash
* ui : harden the HF data layer error typing, cover the helpers in tests
Carries the HTTP status on retryable fetch errors instead of matching the
message text. Marks expand-dependent catalog fields optional and documents
the data/models index pairing. Adds table tests for the pure helpers.
Assisted-by: pi:zai-org/GLM-5.3-Flash
* ui : model id grammar for sidecars, quants and capability parsing
Extend the shared model id parser with sidecar tokens (draft variants and
auxiliary imatrix/mmproj files), weight-file and custom-quant regexes, and add
the tools capability to ModelCapabilities; the selector option row picks it up
from the model's declared capabilities.
Assisted-by: pi:GLM-5.3-Flash
* ui : escape sidecar tokens in the regex alternation
Assisted-by: pi:zai-org/GLM-5.3-Flash
* ui : type-safe API types, fetch helpers and download-ready models store plumbing
Assisted-by: pi:GLM-5.3-Flash
* ui : document the model list index pairing, fix an em-dash
Assisted-by: pi:zai-org/GLM-5.3-Flash
* Update tools/ui/src/lib/components/app/chat/index.ts
Co-authored-by: Pascal <admin@serveurperso.com>
---------
Co-authored-by: Pascal <admin@serveurperso.com>
* Rebase GLM-Next support onto master, and migrate to llama-memory-hybrid-idx
* Add initial MTP support
* Merge branch optimizations. Reduce allocated compute buffer size, speed up long context decode, fla, and slight MTP improvements.
* Review driven changes, remove env vars, protect tensors
* Strip MTP for initial PR
* Clean up after mtp strip
* Clean up after mtp strip
* Update speculative.cpp
* Update llama-context.h
* Clean up after mtp strip
* Fix tokenizer ignore merges
* Improve quantization protection selection
* Refactor mhc helpers, graph base
* Lint Fixes
* Apply suggestions from code review
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
* Skip glm5-next in model saver, fix CRLF
* Skip glm5-next in sweep
* Remove T4 fallback
* Review cleanup
* Review suggestions
* Defer separate MTP gguf handling to MTP PR, drop filter
* Repad n_head_kv
* kpool init apply
* Order by descending score
* Drop guard
* read kpool from hparams, clarify kpool cache flags, remove kpool_build_state(nullptr)
* Add glm5-next support to model saver and add arch test fixture
* Review cleanup
* Kpool pooled caching clarify
* Add multi stream support
* Finish Rebase
* Sparse FA fir DSA prefill
* Const
* Update llama-model.cpp to fix rebase error
* gguf-py : merge tensor map entries for HC tensors
* model : use build_gdn_l2_norm in GLM5_NEXT implementation
* chore : remove trailing whitespace
* model : use new OP precision setting API in GLM5_NEXT implementation
* mtmd : use ggml_swiglu_clamp in GLM5V and apply the image token limit
The two clamps around swiglu_split are what ggml_swiglu_clamp already does,
so the clamp bounds collapse back to one value. GLM5V also never called
set_limit_image_tokens(), so --image-max-tokens had no effect.
Assisted-by: Claude Opus 5
(cherry picked from commit 46d18e12d422be4cc04a70e4a9a9e0168bb3d5b7)
* llama : keep the GLM5-Next k-pool layout across ubatches
The layout was rebuilt from a full cell scan on every ubatch. Pools are fixed
by the positions relative to the sequence's first one, so the layout now lives
on the memory and a ubatch only appends to it.
A sequence edit no longer stales every pooled key either, only the ones at or
after the edited position, which makes a tail seq_rm free. The pooling subgraph
is built unconditionally so the graph shape no longer changes every kpool
tokens, and the pool axis is folded into rows before soft_max, which otherwise
exceeds the CUDA gridDim.y limit past n_kv 262144.
Assisted-by: Claude Opus 5
(cherry picked from commit 5d1c40b93e17fddbf73b785efe43e0d02ccb3977)
* model : write the GLM5-Next recurrent rollback checkpoints
The conv state and the delta net state were only written to the live row, so a
rollback restored whatever the checkpoint rows happened to hold. Take the same
route as kimi-k3: build_recurrent_attn for the state, and write all K_rs conv
groups. That also drops a state view that assumed contiguous rows.
Enroll the arch in test-recurrent-state-rollback, which catches this under its
garbage-filled cache pass.
Assisted-by: Claude Opus 5
(cherry picked from commit 5ace37e86d5d448e83ef5dde5632c748185b18cd)
* llama: fix PR #27773 test-save-load-state restore failure
Clear the attention and indexer cache data after a failed hybrid state restore so restored NaNs cannot affect a later sequence.
Assisted-by: Codex
* llama: fix PR #27773 gpu-rocm graph reallocation
Reserve the full GLM5-Next pool capacity and dirty pool count. The gpu-rocm Test step aborts when n_new grows while the graph node count stays fixed; CUDA, Vulkan, Metal, and WebGPU checks report the same error.
Assisted-by: Codex
* llama : fix GLM5-Next k-pool layout staleness after edits and shared teardown
Two defects in the cross-ubatch k-pool layout added by the k-pool commit:
1. Wrong results. An edited sequence only rebuilt its pool layout when its cell
count changed, so if the first ubatch after an edit added back exactly as many
cells as were removed, the stale position-to-cell list survived. With a unified
cache and more than one sequence, where another sequence takes the freed cells,
the reused layout points at the wrong cells (CPU: large logit drift, CUDA: NaN).
Rebuild whenever the sequence is stale, not only on a size mismatch.
2. Slowdown. "shared" mode was assumed to end only with an edit that forces a
rebuild, but sharing also ends when the other sequence is removed. The survivor
kept shared = true, pinning cache_safe off and re-pooling every pool on every
ubatch (server trigger: n>1 completions with -kvu, via the seq_cp in
copy_state_to). In seq_rm, if the layout has shared cells, stale every sequence
so one rebuild re-derives sharing and cache_safe returns to 1.
Assisted-by: Claude Opus 5
* llama : fix build_attn_mha stream stride for non-contiguous q
build_attn_mha split the batch into streams with a stream stride of
q->nb[3]/n_stream. That only equals one stream's span, (ne[2]/n_stream)*nb[2],
when q is contiguous. GLM5-Next is nope-only, so it does not concat a rope part
and passes the permuted q_absorbed straight in, where nb[3] != ne[2]*nb[2]; the
stride was then n_head times too large and every stream s >= 1 read another
head's queries. Split-KV (-np N without --kv-unified) multi-stream prefill was
wrong for every stream past the first. Unified KV and decode were unaffected
(n_stream == 1, and decode takes the gather path). Other MLA models concat rope
so q is contiguous and the computed value is unchanged for them.
Compute the stride from the token dimension, which is identical for a
contiguous q.
Assisted-by: Claude Opus 5
* llama : re-derive GLM5-Next k-pool sharing on state_read/state_drop
The shared-cell teardown added to seq_rm (stale every sequence when the layout
has shared cells, so a survivor does not keep shared = true and pin cache_safe
off) was missing from the other paths that can free shared cells: state_read
and state_drop staled only the one sequence. Apply the same re-derivation there
and correct the comment that claimed sharing ends only via an edit or seq_rm.
Assisted-by: Claude Opus 5
* quant : drop duplicate GLM5-Next hc_ filter
The hc_ name filter was listed twice in the GLM5_NEXT protection block.
Assisted-by: Claude Opus 5
* glm5-next: scope K-pool cache access to indexed operations
* glm5-next: keep K-pool access in hybrid index memory
* glm5-next: keep mHC graph builders model-local
* glm5-next: mark only touched pools per ubatch
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@huggingface.co>
Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>
Co-authored-by: Piotr Wilkin <ilintar@gmail.com>
With --path or --no-ui, /sw.js returned 404, and a 404 does not remove a service worker, so browsers kept showing the cached built-in UI. Serve a worker that unregisters itself, clears its caches and reloads open tabs. A sw.js in the --path folder is still served first.
Assisted-by: Claude Opus 5.5
* server : support multimodal input for /v1/embeddings (Qwen3-VL-Embedding)
Accept the OpenAI-style wrapped content array format for multimodal
embedding requests. Each {"content": [...]} object is one input that
produces one embedding; text parts are concatenated and image_url parts
are decoded via handle_media then spliced with process_mtmd_prompt.
The legacy formats (plain string, token arrays, mixed arrays, and the
{prompt_string, multimodal_data} object) continue to work unchanged via
tokenize_input_prompts. Bare content arrays (the unwrapped shape) are
rejected with a migration message.
Also disables KV prefix reuse for stateless embedding/rerank tasks so
that repeated inputs do not incorrectly share cached KV across requests.
Assisted-by: Opencode Qwen3.8 27B
* clean up comments and docs
* refactor
* add tests
* support video and audio inp
---------
Co-authored-by: timothywang21 <timothywang21@users.noreply.github.com>
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
* models: pad on the left with ggml_pad_ext
The Parakeet, LFM2-Audio, Granite Speech and Gemma 4 audio encoders
build a left padding as a right pad followed by a roll, and DFlash2
concatenates a zero filled block in front of the previous tokens.
ggml_pad_ext does both in one node now that every backend supports a
left padding. The Gemma 4 audio embeddings are bit identical.
* models: skip the DFlash2 taps that only read padding
A tap at or past block_size shifts every row out of the block, so its
term is zero. The loop runs min(kernel_size, block_size) taps.
* adapt common
* add common_batch
* wip
* wip: spec
* cont
* common_speculative_process
* server_batch to use common_batch
* rm some stale calls
Assisted-by: Claude Fable 5.1
* migrate mtmd
* handle imrope, handle return val of add()/add_embd()
* add spec zeros vector
* add warning on zero fill path
* server : allow splitting RANK pooling for causal LLM rerankers
Rerank models fall into two categories: bidirectional cross-encoders
(BERT, etc.) that require all tokens in a single physical batch, and
causal LLMs repurposed as rerankers (Qwen3, Qwen3-VL) that can use
chunked prefill like any other decoder.
Previously the server rejected all RANK-pooling inputs larger than
n_ubatch, and the graph builder hardcoded QWEN3/QWEN3VL arch checks to
determine last-token pooling. This broke long-document and multimodal
reranking for causal models.
Fix: expose llama_get_causal_attn(ctx) so the server can check the
effective runtime attention type (reflecting any --attention override
or set_causal_attn call). Also expose llama_model_is_causal(model)
for querying the static architectural property from GGUF metadata.
can_split() now permits chunked prefill for RANK pooling when the
context is causal. The graph builder's inline arch check is replaced
with the same cparams.causal_attn predicate, removing the duplication.
Assisted-by: Opencode/Qwen3.8-27B
* remove unused llama_model_is_causal, fix whitespace
Assisted-by: opencode
---------
Co-authored-by: timothywang21 <timothywang21@users.noreply.github.com>
- register --rpc unconditionally and call llama_supports_rpc() only from its handler
- print server "initialization ..." log after args are parsed
Assisted-by: pi:llama.cpp/MiMo-V2.6-Flash-RL
The original function was broken on Windows for some unicode paths
Paths without a trailing separator now create the last directory too,
matching the function name. All current callers already include a
trailing separator, so this change does not affect them.
Signed-off-by: Adrien Gallouët <angt@huggingface.co>
which resulted in different greedy transcripts for 4.5% of English and 6.5% of Japanese
test utterances. In Japanese, some differences changed entire words.
This change:
* uses `log(x + 2^-24)` instead of clamping to the log floor
* uses a symmetric Hann window, equivalent to `torch.hann_window(periodic=False)`
* adds the normalization epsilon to the standard deviation instead of inside the square root
Only the `lfm2a` preprocessor opts into these behaviors. Other audio preprocessors are unchanged.
Tested on top of 84e76d8 using `llama-server` with CUDA and `temperature=0`, compared against
http://github.com/Liquid4All/liquid-audio fp32.
Test set:
* 200 LibriSpeech `test-clean` utterances (EN)
* 200 Common Voice `ja` test utterances (JP)
* identical 16 kHz audio passed to both implementations
| Greedy transcript identical to `liquid-audio` | Without fix | With fix |
| --------------------------------------------- | ----------: | ----------: |
| EN F16 | 191/200 | 200/200 |
| JP F32 | 187/200 | 200/200 |
| JP F16 | 187/200 | 199/200 |
The remaining JP F16 difference is a comma and matches the reference implementation's own bf16
output.
Mel relative L2 error versus `liquid-audio`:
* EN: 3.2% -> ~2e-6 median
* JP: 3.9% -> ~2e-6 median
* model : fold Ling 3.0 VL into the BailingMoeV3 architecture
Assisted-by: Scout
* model : keep shared NORM rope list intact when gating bailingmoe3 on mrope sections
---------
Co-authored-by: aetherbird <aetherbird@users.noreply.github.com>
The OpenAI chat completions API specifies content part type "video_url"
with a {"url": ...} object, and clients typically send data: URIs
(e.g. data:video/mp4;base64,...). The llama-server only accepted the
non-standard "input_video" type and rejected data: URIs for video
(accept_base64_uri=false), so any OpenAI-conformant client failed with
"unsupported content[].type" or "Invalid uri format".
- accept "video_url" as an alias of "input_video"
- read the media object from whichever key was used
- allow data: URIs for video (data:video/*), as already done for images
Aleksander Grygier