* Fix music generation token stopping for quantized models
In Phase 1 lyrics mode, the FSM transitions to CODES state after
TOKEN_THINK_END and disables itself. The quantized Q4_K_M model was
not efficiently generating TOKEN_IM_END to stop the generation,
causing it to continue until hitting the 8192 token limit.
This fix forces TOKEN_IM_END to be generated immediately after
TOKEN_THINK_END in lyrics mode, ensuring clean completion of the
planning phase without excessive token generation.
Testing shows generation now completes in ~500ms instead of 80+
seconds with timeout errors.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
* Clarify comment - fix applies to all models, not just quantized
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
* Improve fix: only force TOKEN_IM_END at token limit
Instead of forcing TOKEN_IM_END immediately after TOKEN_THINK_END,
only force it when we've reached the token limit. This allows the model
to generate lyrics after the thinking block while still preventing KV
cache exhaustion.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
---------
Co-authored-by: Claude <noreply@anthropic.com>
* misc : prefer ggml-org models in docs and examples
Prefer referring to known-good quantizations under ggml-org rather than
3rd-party uploaders.
* remove accidentally committed file
* grammar: add test case for nullable symbol loop
Reproduce stack overflow (or OOM) with ( [x]* )* found while adding
GBNF support to ripgrep-edit.
llama-server reproducer:
curl \
-X POST \
-d '{
"messages": [{ "role": "user", "content": "write yes" }],
"grammar": "root ::= ( [x]* )*"
}' \
-H "Content-Type: application/json" \
http://localhost:8811/v1/chat/completions
* grammar: prevent stack overflow with nullable symbol loop
Fix a potential stack overflow in llama_grammar_advance_stack that
could occur when processing grammars with nullable symbols that lead
to infinite derivations of empty strings. The fix introduces cycle
detection by tracking visited stacks to prevent infinite recursion.
rg-edit regexp: llama_grammar_advance_stack
rg-edit extra-args: -A20
rg-edit directive: """Rewrite: fix the following segfault:
[..]
⚫ Testing segfault. Grammar:
root ::= ( [x]* )*
root ::= ( [x]* )*
Segmentation fault build/bin/test-grammar-integration"""
gptel-context:
(("~/llama.cpp/src/llama-grammar.cpp")
("~/llama.cpp/tests/test-grammar-integration.cpp")
("~/llama.cpp/grammars/./list.gbnf")
("~/llama.cpp/grammars/./json_arr.gbnf")
("~/llama.cpp/grammars/./json.gbnf")
("~/llama.cpp/grammars/./japanese.gbnf")
("~/llama.cpp/grammars/./english.gbnf")
("~/llama.cpp/grammars/./chess.gbnf")
("~/llama.cpp/grammars/./c.gbnf")
("~/llama.cpp/grammars/./arithmetic.gbnf")
("~/llama.cpp/grammars/./README.md"))
* grammar: convert recursive llama_grammar_advance_stack to iterative
This change converts the function to an iterative approach using
explicit stacks, which prevents deep recursion and eliminates the risk
of stack overflow.
rg-edit regexp: llama_grammar_advance_stack
rg-edit extra-args: -A30
rg-edit directive: """Rewrite: fix the following segfault:
[..]
⚫ Testing segfault. Grammar:
root ::= ( [x]* )*
root ::= ( [x]* )*
Segmentation fault build/bin/test-grammar-integration
convert from recursive to interactive"""
gptel-context:
(("~/llama.cpp/src/llama-grammar.cpp")
("~/llama.cpp/tests/test-grammar-integration.cpp")
("~/llama.cpp/grammars/./list.gbnf")
("~/llama.cpp/grammars/./json_arr.gbnf")
("~/llama.cpp/grammars/./json.gbnf")
("~/llama.cpp/grammars/./japanese.gbnf")
("~/llama.cpp/grammars/./english.gbnf")
("~/llama.cpp/grammars/./chess.gbnf")
("~/llama.cpp/grammars/./c.gbnf")
("~/llama.cpp/grammars/./arithmetic.gbnf")
("~/llama.cpp/grammars/./README.md"))
v2: Added a `std::set` to perform tree-based lookups with O(N log N)
complexity. Testing with a parallel run of `test-grammar-integration`
shows a double-digit percentage increase in runtime. An
`unordered_set` with O(1) hashing was also evaluated, but the overhead
of constructing hash keys from pointers made it significantly slower
than the rbtree implementation that only requires an ordering
operator. The performance regression in the test suite appears
justified by the overall reduction in algorithmic complexity.
Co-developed-by: Piotr Wilkin (ilintar) <piotr.wilkin@syndatis.com>
* grammar: add test case for hang in repetition grammar processing
This commit adds a new test case to the grammar integration tests that
specifically targets a hang scenario in the repetition grammar parser
found while adding GBNF support to ripgrep-edit.
llama-server reproducer:
curl \
-X POST \
-d '{
"messages": [{ "role": "user", "content": "write yes" }],
"grammar": "root ::= (([^x]*){0,99}){0,99}"
}' \
-H "Content-Type: application/json" \
http://localhost:8811/v1/chat/completions
* grammar: add repetition threshold check
The change introduces a maximum repetition threshold to avoid
excessive rule expansion during grammar parsing. When parsing
repetition patterns like {m,n}, the parser now calculates the
potential number of rules that would be generated and throws an error
if the product of previous rules and new rules exceeds the threshold.
A test case was added to verify the threshold is properly enforced for
deeply nested repetition patterns that would otherwise cause hangs.
The MEAN/CLS/LAST pooling paths in encode() and decode() used
n_embd_inp() (16384 for qwen3vl with deepstack) to read from the
pooled embedding tensor, which only has n_embd_out() (4096) floats
per sequence. This caused a tensor read out of bounds assertion.
Fixes embedding mode for Qwen3-VL-Embedding models.
rpc : prevent division by zero in deserialize_tensor
When receiving an RPC message with a deprecated tensor type (e.g., type 4 or 5 where `blck_size == 0`), `ggml_row_size()` will trigger a division by zero (SIGFPE) and crash the rpc-server.
This patch adds a simple validation check in `deserialize_tensor` to return `nullptr` if the requested tensor type has a block size of 0.
(Note: This was originally reported via Security Advisory and maintainer suggested dropping a patch here).
* style: remove trailing whitespace
Explicitly mark save_acc and add_save_Acc with always_inline
in tinyBLAS_PPC. This ensures the compiler keeps MMA accumulator
disassembly within kernel's register context, preventing un-necessary
stask spills.
Signed-off-by: Shalini Salomi Bodapati <Shalini.Salomi.Bodapati@ibm.com>
* Check granite hybriid expert count to set type as LLM_TYPE_7B_A1B or LLM_TYPE_1B
* Use feed fwd dim instead of num of experts
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* server: (doc) clarify in-scope and out-scope features
* Apply suggestions from code review
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* vulkan: change gated_delta_net to shard a column across a subgroup
This is based on https://github.com/ggml-org/llama.cpp/pull/20391, I used an
LLM to port the CUDA code to Vulkan, and guided to it to make various fixes to
work with Vulkan (e.g. handling different subgroup sizes, unknown mapping of
subgroup to invocation id, using subgroupAdd optionally, etc.).
This fixes a perf regression from the transposing of the values in memory
(!20443).
* vulkan: Spread columns across fewer lanes to reduce the number of workgroups
* context: zero output buffer on allocation
Address GHSA-wqq9-25mr-rw76.
The logits output buffer allocated in output_reserve() uses
posix_memalign(), which does not zero memory. The buffer is only
written during decode when needs_raw_logits() returns true. When
backend samplers cover all output sequences, needs_raw_logits()
returns false and the buffer is never written, but
llama_get_logits() still returns a pointer to it, exposing stale
heap content.
Zero the buffer after allocation to prevent information disclosure
through the public logits API.
Found-by: Pwno
* Update src/llama-context.cpp
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
