* tests: Harmonize the use of private ggml includes
* tests: In test-backend-ops, use quoted includes
As with all other tests. This is to ensure that the build uses shipped
headers over possibly system-installed ones.
* chat : fix reasoning leak with force-opened bare <think> templates
The reasoning start tag inferred from prior turns can carry trailing
whitespace (e.g. <think>\n) while a force-open template prefills a bare
<think>. Trim the tag used for the prefix split so the bare prefill is
matched instead of being swallowed into content.
* chat : fix Nemotron Nano v2 regression
---------
Co-authored-by: Alde Rojas <hello@alde.dev>
* fix: drop MCP recommendations auto-popup and silent preloads
* feat: Add consent-driven MCP recommendations inside Add New Server dialog
* refactor: Drop mcpDefaultServerOverrides for mcpServers[i].enabled
* feat: Center the empty state on the MCP settings page
* fix: keep existing MCP cards intact when adding a new server
* fix: keep MCP cards stable when a new server is added
* refactor: keep MCP server list in config insertion order
* feat: shrink the recommended-MCP cards to two tools each and fit them in one row
* feat: make recommended MCP cards click-to-fill and tighten copy
* feat: highlight the selected MCP recommendation and stop auto-focus on dialog open
* feat: derive MCP recommendation selection from the form URL
* fix: make recommendation MCP cards fully non-focusable
* fix: redirect focus from first card to the URL input on consent
* chore: Formatting
* refactor: Remove Recommended MCP Servers completely
* fix: Preserve legacy mcpDefaultServerOverrides key after merge migration for downgrade compatibility
* server: honour per-request reasoning_budget_tokens in chat completions
The reasoning-budget block in oaicompat_chat_params_parse read only the
server-level default (opt.reasoning_budget, typically -1) and the
Anthropic-style alias thinking_budget_tokens, but never the canonical
reasoning_budget_tokens field from the request body. Because the key
was then written into llama_params before the generic body-copy loop
ran, the copy loop found the key already present and silently skipped
the caller-supplied value. Any per-request override (e.g. 0 to
suppress thinking entirely) was therefore discarded.
Fix: read reasoning_budget_tokens from the request body first, so the
value that reaches the sampling layer is the one the caller intended.
Add a unit test in test-chat.cpp that exercises this path via
oaicompat_chat_params_parse with a Qwen3 template (which the autoparser
detects as a thinking-capable model) and asserts the returned
llama_params carries reasoning_budget_tokens == 0.
* server: honour per-request reasoning_budget_message in chat completions
The reasoning-budget block in oaicompat_chat_params_parse wrote
reasoning_budget_message into llama_params straight from the server-level
default (opt.reasoning_budget_message) and never read the canonical
reasoning_budget_message field from the request body. Because the key
was written before the generic body-copy loop ran, that loop found the
key already present and silently skipped the caller-supplied value. Any
per-request override of the message injected before the end tag when the
budget is exhausted was therefore discarded, even though server-task.cpp
already reads reasoning_budget_message from that data.
This mirrors the reasoning_budget_tokens bug fixed in the previous commit.
Fix: read reasoning_budget_message from the request body first, falling
back to the server default, so the value that reaches the sampling layer
is the one the caller intended.
While here, collapse the adjacent reasoning_budget_tokens override to a
single json_value() call; json_value already falls back to the default on
a missing/null/wrong-type key, so the explicit body.contains() guard was
redundant. No behavioral change.
Add a unit test in test-chat.cpp that exercises this path via
oaicompat_chat_params_parse with a Qwen3 template (which the autoparser
detects as a thinking-capable model) and asserts the returned
llama_params carries the per-request reasoning_budget_message rather than
the server default.
* cleanup
---------
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
If mmproj is explicitly disabled via the model preset or command-line
parameters then the model won't be able to handle image/audio inputs and
this shouldn't be declared as supported input modality on the /v1/models
endpoint.
* mtmd: fix silent prompt truncation on embedded NUL
mtmd_input_text carried the prompt as a bare const char* with no
length, so a NUL byte in message content cut the prompt at the
tokenizer boundary and dropped every later message plus the assistant
marker, with no log. Add an explicit text_len and thread it through,
matching llama_tokenize and the text only path.
* cleanup
---------
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
* server : fix image blocks in tool_result being dropped during Anthropic→OpenAI conversion
server_chat_convert_anthropic_to_oai() silently discarded image blocks
inside Anthropic tool_result content. This broke multimodal tool outputs
(e.g. a tool that returns an image) because the model never received the
image.
When tool_result contains image blocks, convert them to OpenAI
multimodal content parts (text + image_url array). Plain-text results
remain simple strings for backwards compatibility.
* server : add test for image blocks in Anthropic tool_result conversion
If a Cuda device has no or limited available memory, the actual call
to cudaMemGetInfo() itself can cause a fatal crash due to a cuda out
of memory error (there is not enough memory to actually query memory)
This causes an issue because we query memory for all devices at
startup even if the user isn't trying to use the device for inference.
Fix this by making the error non-fatal and assigning zero total/free
memory to the device. This will have the downstream effect of the fit
algorithm not trying to put any layers on it, which is desired outcome
vs hard crashing.
this also prevents crashes in cuda enabled builds when user explicitly
passes '-dev none'
* [Vulkan] Fixes llama-cli breaking over longer promts sizes
The llama-cli was breaking for longer promts sizes for q4_0 quantized networks. Causing due to insufficient shared memory.
* Removed the un-used Adreno device
* Updated matmul for small pipeline.
* server: accept null sampling params
Extend the schema validation to treat a null value as absent, so
clients can send null on nullable params (temperature, top_p, ...)
to request the server default. This matches the OpenAI spec and the
json_value convention used elsewhere.
Add has_field() to skip null in the field eval guards.
* has_field -> has_value
llama_meta_device_get_split_state() recompiled 29 std::regex on every call.
In -sm tensor mode the callback runs once per tensor per token, so this
dominated the decode thread in profiling. Mark them static const so they are
compiled once. Kept inside the function (local statics are thread-safe since
C++11). Patterns are literal and stateless, so behavior is unchanged.
* hex-sort: add efficient bitomic sort in hvx regs up to 1024 elements
* hex-sort: fix inverted vrors
* hex-sort: specialize sort functions for the common cases
* hex-sort: add tracing and local context
Concedo
Wagner Bruna
Christian Kastner
QuintinShaw
Frosty40
Todd Malsbary
Aleksander Grygier
Bernard Ladenthin
Alessandro de Oliveira Faria (A.K.A.CABELO)
Sebastian Dröge
Pascal
Aldehir Rojas
quei
kdkd
Rohit Mahesh
cphlipot
Aman Gupta
Xuan-Son Nguyen
fairydreaming
Raman Shinde
Hongqiang Wang
eduardopessin
Max Krasnyansky