* ci : server windows set build type explicitly
* cont : try windows-2025
* ci : use llvm
* cont : use ninja
* cont : fix shell
* ci : set number of jobs correctly
* ci : fix windows with vulkan ccache by using llvm
* ci : server ccache only on master
* ocd : fix job names
[no release]
* ci : fix undefined sanitizer build to use Debug build type only
* ci : ccache the server builds
* cont : remove ui dependency + reuse ccache for both ubuntu jobs
* tmp : force ccache save
* Revert "tmp : force ccache save"
This reverts commit a857b03a10.
* cont : no need for node.js
* ci : move [no release] check to dedicated check_release job
Move the workflow-level \`if\` condition that skips builds when the commit
message contains \`[no release]\` into a lightweight \`check_release\` job.
All build jobs now depend on it via \`needs\` and check its output.
This ensures the skip logic is evaluated at the job level rather than at
the workflow level, which is the recommended approach for conditional jobs.
Assisted-by: llama.cpp:local pi
* cont : use `fast` runner
* ci : skip release workflow on master when commit message contains [no release]
Assisted-by: llama.cpp:local pi
* ci : restrict sanitizer builds to x86_64 + fix build type
the spark is apparently too slow for some reason
* tests : fix undefined warning
[no ci]
* ci : disable SYCL f16 builds
* ci : extract android and hip into separate workflows
* ci : move webgpu to separate workflow
* ci : move the rpc to a separate workflow
* ci : extract s309x and ppcl jobs
* ci : extract opencl job into a separate workflow
* ci : remove tag from build-self-hosted.yml
* ci : slim -> self-hosted
* ci : prevent heavy CPU jobs from running on fast runners
* ci : prevent cmake pkg to run on dedicated fast runners
* ci : try to bump 3.11 -> 3.13
* ci : move lint back to 3.11
* ci : back to 3.11
* ci : add comment about UI jobs
* ci : move python requirements check to CPU runners
this job is a bit slow for a dedicated "fast" runner
* ci : add self-hosted ui workflow
* ci : fix UI naming
* tmp to check if arm64 fast is compatible with all jobs
* revert last commit
* pi : update
* ci : fix ios build
* ci : fix andoroid
* ci : fix apple builds
* cmake : add install() for impl libraries
Add install(TARGETS <target> LIBRARY) for all -impl libraries that were
changed from STATIC to shared (controlled by BUILD_SHARED_LIBS) in
commit bb28c1fe2. Without this, cmake --install fails to copy the shared
libraries, causing runtime errors like:
llama-server: error while loading shared libraries: libllama-server-impl.so
Ref: https://github.com/ggml-org/llama.cpp/issues/23494#issuecomment-4512912515
Assisted-by: llama.cpp:local pi
* ci : fix xcframework build
* cmake : remove STATIC from impl libraries, allow BUILD_SHARED_LIBS control
Remove explicit STATIC from all -impl libraries (server, cli, completion, bench,
batched-bench, fit-params, quantize, perplexity) so BUILD_SHARED_LIBS controls
shared vs static linkage.
Add WINDOWS_EXPORT_ALL_SYMBOLS ON for proper DLL export on Windows.
Assisted-by: llama.cpp:local pi
* cmake : enable LLAMA_BUILD_APP by default
Assisted-by: llama.cpp:local pi
* ci : disable app in build-cmake-pkg.yml
* tests : move save-load-state from examples to tests
- Move examples/save-load-state/ to tests/test-save-load-state.cpp
- Remove subdirectory reference from examples/CMakeLists.txt
- Add test to tests/CMakeLists.txt as a model test
- Remove CODEOWNERS entry for removed example directory
Assisted-by: llama.cpp:local pi
* cont : update ci
* metal : fix GGML_OP_SET kernel threads
* tests : extend test_cpy to support different src/dst shapes
Extend test_cpy to support different source and destination tensor shapes
for CPY operations (reshaping), where the total number of elements must match.
- Renamed ne -> ne_src, added ne_dst parameter (default: use src shape)
- Added 50 new reshaping test cases covering 1D<->2D<->3D<->4D conversions
- Tests exercise 1024 boundary, small shapes, and large dimensionality changes
- Fixed dangling reference bug (storing & to temporary std::array)
- Updated all existing test calls with permute/transpose args for compatibility
Assisted-by: llama.cpp:local pi
* metal : optimize concat kernel with row batching for small widths
When ne0 < 256, batch multiple rows into a single threadgroup to improve
occupancy. This avoids underutilizing the GPU when processing narrow tensors.
- Dispatch nth = min(256, ne0) threads per group
- Calculate nrptg (rows per threadgroup) to fill up to 256 threads
- Update kernel index calculation to handle the row batching
- Add boundary check for i1 >= ne1
Assisted-by: llama.cpp:local pi
* tests : clean-up
* tests : refactor CPY shape tests to use dimension permutations
Replace 75 hardcoded test cases with a loop over permutations of
{3, 5, 7, 32} (total elements: 3360). Each src permutation is tested
against canonical sorted and reverse dst, skipping identical shapes.
Covers F32, F16, and Q4_0 (when both src and dst ne0 == 32).
Assisted-by: llama.cpp:local pi
ggml_backend_dev_by_name always appends a nullptr sentinel to the devices
vector. Skipping nullptr entries prevents assertion failure in
ggml_backend_dev_name.
Assisted-by: llama.cpp:local pi
Add graphs reused counter to the per-slot timing output, printed via
llama_perf_context().
Assisted-by: llama.cpp:local pi
Co-authored-by: ggerganov <ggerganov@users.noreply.github.com>
* save-load-state : refactor into separate phase functions
- Split monolithic main() into 4 self-contained phase functions, each
managing its own context/sampler/batch lifecycle
- Each function tokenizes internally using its local ctx instance
- main() is now a clean orchestrator: init -> run phases -> assert results
- Proper resource cleanup on every exit path (return {} on error)
Assisted-by: llama.cpp:local pi
* save-load-state : use params.out_file instead of separate state_file
- Remove state_file parameter from all phase functions
- Each function accesses params.out_file directly
- Initialize params.out_file in main alongside params.prompt
Assisted-by: llama.cpp:local pi
* save-load-state : use smart pointers for ctx and smpl
- Replace raw llama_context* with llama_context_ptr
- Replace raw llama_sampler* with llama_sampler_ptr
- Remove all manual llama_free() and llama_sampler_free() calls
- Keep llama_batch as raw (managed manually with llama_batch_free)
Assisted-by: llama.cpp:local pi
* save-load-state : add local llama_batch_ptr RAII wrapper
- Add llama_batch_ptr struct holding llama_batch by value
- Calls llama_batch_free() in destructor
- Eliminates all manual llama_batch_free() calls
Assisted-by: llama.cpp:local pi
* save-load-state : replace printf/fprintf with logging macros
- Add log.h include
- Replace fprintf(stderr, ...) errors with LOG_ERR
- Replace fprintf(stderr, ...) info with LOG_TRC
- Replace printf output with LOG
Assisted-by: llama.cpp:local pi
* save-load-state : refactor tests to check results inline
Each follow-up phase now accepts an expected result and performs
the comparison internally instead of collecting results in main().
Assisted-by: llama.cpp:local pi
* save-load-state : improve test output readability
Add phase labels, remove redundant run prefixes, and show
PASS after each test.
Assisted-by: llama.cpp:local pi
* pi : add rule about git signing
* save-load-state : simplify llama_batch_ptr
Change get() to return a reference and remove operator*().
Use batch.get() throughout for consistency.
Assisted-by: llama.cpp:local pi
* save-load-state : extract generate_tokens helper
Factor out the repeated token generation loop into a shared
helper function used by all phases.
Assisted-by: llama.cpp:local pi
* save-load-state : update comments to use test terminology
Replace "Phase" with "Test" and list each test's steps
as bullet points.
Assisted-by: llama.cpp:local pi
* save-load-state : rename test functions
Rename to test_baseline, test_state_load, test_seq_cp_host,
test_seq_cp_device. Update comments and logs accordingly.
Assisted-by: llama.cpp:local pi
* pi : add rule to never git push without confirmation
Assisted-by: llama.cpp:local pi
* common : add model_only option to common_init_from_params
Add bool model_only parameter to skip context creation,
sampler init, and context-dependent setup.
Use in save-load-state to initialize only the model,
with each test creating its own context.
Assisted-by: llama.cpp:local pi
---------
Co-authored-by: ggerganov <ggerganov@users.noreply.github.com>
* llama-eval : add per-problem summary table to HTML reports
- Add chunk_idx and problem_idx to TaskState and saved case dicts
- Group completed cases by problem_idx in dump_html()
- Render per-problem summary table before individual task table
- Columns: Problem (zero-padded), Runs, Correct (n/r),
Tokens (min/avg/max), T/s (min/avg/max), Gen s (min/avg/max)
- Sorted by problem index, monospace font, right-aligned numbers
- Colspan headers for grouped stats, auto width
- Simulator: add /v1/models endpoint, timings in response,
template-aware question matching, --dataset arg (aime/aime2025)
Assisted-by: llama.cpp:local pi
* llama-eval : add tabs for Detailed and Summary tables, apply monospace font globally
- Wrap Detailed and Summary tables in switchable tabs (Detailed active by default)
- Remove summary-section wrapper, use tab labels instead
- Apply monospace font to all tables and the top bar
Assisted-by: llama.cpp:local pi
* llama-eval : redesign top bar as CSS grid label/value pairs
- Replace flat span list with 4-column grid layout (2 pairs per row)
- Labels in muted color (#888), values in dark (#222)
- Bold dataset name and model name
- Removed media query, always uses 4 columns
Assisted-by: llama.cpp:local pi
* llama-eval : use realistic token counts and throughput in simulator
- comp_tokens: [30, 80] → [10000, 60000]
- tps_gen: derived → uniform [90.0, 110.0]
- t_gen_ms: now computed from tokens/tps
Assisted-by: llama.cpp:local pi
* llama-eval : color Answer column green/red based on correctness
Use the same .correct/.incorrect CSS classes on the Answer column
to make correct answers green and incorrect answers red.
Assisted-by: llama.cpp:local pi
* llama-eval : fix pyright errors from max(..., key=len) type inference
Use key=lambda x: len(x) instead of key=len so the type checker
infers the return type as str instead of Sized, fixing:
- unresolved-attribute: Object of type Sized has no attribute lower
- not-subscriptable: Cannot subscript object of type Sized
Assisted-by: llama.cpp:local pi
Add Aime2026Dataset class loading from MathArena/aime_2026 on
HuggingFace. 30 problems (two sets of 15), single config/split.
Usage: --dataset aime2026
Assisted-by: llama.cpp:local pi
* working llama-eval mc and math suite
* multi source llama-eval
* Add readme
* add checkpointing
* examples: add llama-server simulator for testing eval scripts
Add a standalone Python script that simulates a llama-server HTTP endpoint
for testing the eval script. The simulator:
- Implements /v1/chat/completions endpoint with OpenAI-compatible format
- Loads AIME dataset from HuggingFace with local caching
- Uses Levenshtein distance for intelligent question matching
- Supports configurable success rate for correct/wrong answer generation
- Provides debug logging for troubleshooting
Also includes test scripts and documentation for testing and understanding
the simulator functionality.
* examples: refactor test-simulator.sh for better readability
Extract repeating question string into TEST_QUESTION variable and
create make_request() helper function to reduce code duplication.
Add proper error handling for error responses.
* docs: update llama-eval-discussion.md with session work summary
Add summary of llama-server-simulator implementation work including
features, testing results, technical decisions, and refactoring.
* examples: add simplified llama-eval-new.py for AIME evaluation
- Create new simplified evaluation script focused only on AIME
- Implement EvalState and Processor dataclasses for structured state management
- Add real-time feedback showing correct/incorrect status per case
- Abstract grading interface for external grader support
- Use structured JSON output for eval state
- Apply HuggingFace dataset caching to avoid repeated downloads
- Remove Levenshtein matching - eval script only sends requests and validates answers
* docs: remove README.md from llama-eval
* examples: implement flexible grader system for answer validation
- Add Grader class supporting regex and CLI-based grading
- Implement built-in regex patterns for AIME, GSM8K, MMLU, HellaSwag, ARC, WinoGrande
- Add CLI grader interface: python script.py --answer <pred> --expected <gold>
- Add HF telemetry disable to avoid warnings
- Support exact match requirement for regex patterns
- Add 30-second timeout for CLI grader
- Handle both boxed and plain text formats for AIME answers
* examples: use HF_HUB_OFFLINE to avoid HF Hub warnings
* examples: remove HF_HUB_OFFLINE to allow dataset download
* examples: use cached dataset path to avoid HF Hub requests
* examples: use cached dataset path in simulator to avoid HF Hub requests
* docs: update llama-eval-discussion.md with session work summary
* examples: add threading support and model parameter to llama-eval-new.py
- Add ThreadPoolExecutor for parallel request processing controlled by --threads
- Add --model argument to specify model name in request data
- Refactor process() to use thread-safe _process_single_case() method
- Update progress tracking to work with concurrent execution
* docs: update llama-eval-discussion.md with threading and model parameter updates
- Add threading support implementation details
- Document ThreadPoolExecutor usage and thread safety
- Add model parameter implementation details
- Include testing results for both features
* examples: add task summary table to llama-eval-new.py
* eval : print progress
* eval : add prompts
* test : fix path
* sim : fix answer matching
* eval : support multiple dataset runs
* minor
* improve grader
* docs
* remove old files
* datasets : add gsm8k
* add gpqa + sampling + docs
* rename
* grader : improve example answers
* cont
* datasets : add aime2025
* grader : update prompt
* grade : improve regex + logs
* datasets : fix aime2025
* cleanup
* add AGENTS.md
* ignore errors
* resume eval
* cleanup
* fix counts
* simplify
* fix prompts
* add html
* store full response
* add tokens
* resoning and error handling
* refactor
* track total time
* remove junk
* eval : unify "judge" terminology to "grader"
Replace all occurrences of "judge" with "grader" for consistency
across the codebase (CLI args, Grader class fields, help text).
Assisted-by: llama.cpp:local pi
* eval : add Wilson score confidence interval to results
Compute 95% CI on-the-fly from completed cases. Displayed in
terminal output, HTML report, and JSON state.
* llama-eval : add per-task generation speed from server timings
Extract predicted_per_second from the server timings response and store
it as tps_gen per task. Display in console progress, print_all_tasks,
and HTML report.
Assisted-by: llama.cpp:local pi
* llama-eval : add per-task generation time from server timings
Extract predicted_ms from the server timings response and store it as
t_gen_ms per task. Display in seconds with one decimal digit in console
progress, print_all_tasks, and HTML report.
Assisted-by: llama.cpp:local pi
* llama-eval : rename display, escaped, and count variables to use prefix convention
- _display suffix → display_ prefix (answer, tokens, tps, t_gen)
- _escaped suffix → escaped_ prefix (response, prompt, reasoning)
- _count suffix → n_ prefix (correct, incorrect, pending)
Assisted-by: llama.cpp:local pi
* llama-eval : support multiple evaluation endpoints with dynamic task distribution
- Add ServerConfig dataclass (url, threads, name)
- Accept comma-separated --server, --threads, --server-name CLI args
- Dynamic shared-queue task distribution across servers (fast servers do more work)
- One ThreadPoolExecutor per server, workers pull from shared Queue
- Track which server processed each task (server_name in results)
- Thread-safe EvalState with threading.Lock for concurrent mutations
- Server column in HTML report and console output
- Backward compatible: single server works as before
Assisted-by: llama.cpp:local pi
* llama-server-simulator : replace Flask with stdlib http.server
- Use HTTPServer + BaseHTTPRequestHandler instead of Flask
- RequestHandler handles POST /v1/chat/completions
- Server runs in daemon thread with clean Ctrl+C shutdown
- Remove flask and unused asdict imports
Assisted-by: llama.cpp:local pi
* llama-eval : update README with PR link and quick-start examples
Assisted-by: llama.cpp:local pi
* llama-eval : track model name in eval state and verify on resume
- Store model_name in EvalState and JSON output
- Display model in HTML summary table
- Verify --model matches stored model when resuming
Assisted-by: llama.cpp:local pi
* llama-server-simulator : fix comment - Dice coefficient, not Levenshtein
Assisted-by: llama.cpp:local pi
* llama-eval : require --grader-model or --model when using --grader-type llm
Assisted-by: llama.cpp:local pi
* llama-eval : protect dump() with lock for thread safety
Assisted-by: llama.cpp:local pi
* llama-eval : compact HTML report output
- Replace verbose summary table with single inline bar
- Shorten status text: '✓'/'✗'/'–'/'!' instead of full words
- Flatten CSS: remove box-shadows, border-radius, reduce padding
- Use system-ui font, 13px table, 12px details
- Conditional reasoning section (only shown when present)
- Single toggle JS function instead of two
- Shorter column headers
Assisted-by: llama.cpp:local pi
* llama-eval : check server connectivity on startup
- Hit /v1/models for each server before evaluation
- Exit with error if any server is unreachable
- Print comma-separated model IDs per server in startup output
- Sequential checks, no retries, no timeout override
Assisted-by: llama.cpp:local pi
* llama-eval : use server1/server2 instead of gpu1/gpu2 in README
Assisted-by: llama.cpp:local pi
---------
Co-authored-by: gatbontonpc <gatbontonpc@gmail.com>
* spec : refactor
* spec : drop support for incompatible vocabs
* spec : update common_speculative_init()
* cont : pass seq_id
* cont : dedup ctx_seq_rm_type
* server : sketch the ctx_dft decode loop
* server : draft prompt cache and checkpoints
* server : improve ctx names
* server, spec : transition to unified spec context
* cont : sync main and drft contexts
* cont : async drft eval when possible
* cont : handle non-ckpt models
* cont : pass correct n_past for drafting
* cont : process images throught the draft context
* spec : handle draft running out of context
* server : fix mtmd draft processing
* server : fix URL for draft model
* server : add comment
* server : clean-up + dry
* speculative-simple : update
* spec : fix n_past type
* server : fix slot ctx_drft ptr
* tools : update readme
* naming : improve consistency
* spec : refactor for multi-sequence speculative context
* cont : prepare params
* cont : prepare params
* spec : support parallel drafts
* server : support parallel drafting
* llama : reuse device buffers when possible
* server, spec : clean-up
* cont : clean-up
* cont : minor
* spec : reset `drafting` flag at the end
* spec : introduce `common_speculative_process()`
* spec : allow for multiple spec types (chain of speculators)
* replace old type field of type common_speculative_type in the
common_params_speculative struct with a vector to allow multiple
types to be specified
* introduce common_get_enabled_speculative_impls(const std::vector<enum common_speculative_type>)
to figure out which implementations the user has enabled
* introduce common_speculative_type_from_names(const std::vector<std::string> & names)
to parse the already user provided spec types
* all speculators run sequentially, best one wins (we verify its drafted tokens)
* maximize expected accepted tokens for current round by calculating the
product between the probability of accepting current token (n_acc_tokens / n_gen_drafts)
and the draft's length
---------
Co-authored-by: Petros Sideris <petros.sideris@nokia.com>
Previously, unknown tool names passed via --tools were silently ignored.
Now the server validates each tool name at startup and exits with an
error if an unrecognized tool is specified, listing the available tools.
Assisted-by: llama.cpp:local pi
* docs : update speculative decoding parameters after refactor (#22397)
Update docs/speculative.md to reflect the new parameter naming scheme
introduced in PR #22397:
- Replace --draft-max/--draft-min with --spec-draft-n-max/--spec-draft-n-min
- Replace --spec-ngram-size-n/m with per-implementation variants
- Add documentation for all new --spec-ngram-*- parameters
- Update all example commands
Assisted-by: llama.cpp:local pi
* pi : add rule to use gh CLI for GitHub resources
Assisted-by: llama.cpp:local pi
* docs : run llama-gen-docs
* arg : fix typo
* scripts : add wc2wt.sh - create worktree from current HEAD
Add a script to create a git worktree on a new branch from the current
HEAD. Similar to pr2wt.sh but for local development branches instead of
PRs.
Usage:
./scripts/wc2wt.sh gg/new-feature
./scripts/wc2wt.sh gg/new-feature "bash -l"
Assisted-by: llama.cpp:local pi
* cont : no need to try to delete the branch
* gitignore: add AGENTS.local
Assisted-by: llama.cpp:local pi
Signed-off-by: Georgi Gerganov <ggerganov@gmail.com>
* gitignore: rename AGENTS.local to AGENTS.local.md
Assisted-by: llama.cpp:local pi
Signed-off-by: Georgi Gerganov <ggerganov@gmail.com>
---------
Signed-off-by: Georgi Gerganov <ggerganov@gmail.com>
Fixes#22237 — the find_library(MATH_LIBRARY m) result was being
discarded and the target linked against the literal 'm' string.
This prevents users from overriding the math library (e.g. for AMD AOCL)
via CMake variables. Now the discovered MATH_LIBRARY is used directly.
* gemma : reduce graph splits by keeping per-layer ops in the input layer
* gemma : put the per-layer proj in the first layer
* cont : move the projection before the layer loop
