* from previous PR
* Make instruction(system) as first message
* Convert [input_message] (text/image/file)
* Rename convert_responses_to_chatcmpl(body) -> response_body
* Initial tool call support
* Erase instructions field from chatcmpl body
* Feed reasoning texts to chat template
* Use std::vector instead of opaque json array
* Make output_item.added events consistent
* Move `server_task_result_cmpl_partial::update` from header to source
* Match ID of output_item.added and .done events
* Add function_call only if there is no "fc_" prefix
* Add function call output at non-streaming API
* Test if ID is persistent
* Add doc
* Fix style - use trailing comma
* Rewrite state management
* catch up with upstream/master
* Fix style - "type" is the first item of SSE data
* Explicitly check "instructions" from response_body
* Make lambdas static
* Check if reasoning content exists
* Add `oai_resp_id` to task_result_state(also initialized at ctor), server_task_result_cmpl_partial, and server_task_result_cmpl_final
* Reject `input_file` since it is not supported by chatcmpl
* Add "fc_" prefix to non-straming function call id as coderabbit pointed out
---------
Co-authored-by: openingnow <>
* server : make sure children tasks are scheduled to launch with parent
* fix
* add comment pointing to this PR
* fix
* clean up
* more debug messages
* add pop_deferred_task with specific ID version
* improve the logic
* simple approach
* no double move
* correct return type of launch_slots_with_parent_task
* server : add thinking content blocks to Anthropic Messages API
Add support for returning reasoning/thinking content in Anthropic API
responses when using models with --reasoning-format deepseek and the
thinking parameter enabled.
- Non-streaming: adds thinking block before text in content array
- Streaming: emits thinking_delta events with correct block indices
- Partial streaming: tracks reasoning state across chunks via
anthropic_has_reasoning member variable
Tested with bartowski/DeepSeek-R1-Distill-Qwen-7B-GGUF model.
* server : fix Anthropic API streaming for thinking content blocks
Add signature field and fix duplicate content_block_start events in
Anthropic Messages API streaming responses for reasoning models.
* server: refactor Anthropic streaming state to avoid raw pointer
Replace raw pointer to task_result_state with direct field copies:
- Copy state fields in update() before processing chunk
- Use local copies in to_json_anthropic() instead of dereferencing
- Pre-compute state updates for next chunk in update()
This makes the data flow clearer and avoids unsafe pointer patterns.
* implement sleeping at queue level
* implement server-context suspend
* add test
* add docs
* optimization: add fast path
* make sure to free llama_init
* nits
* fix use-after-free
* allow /models to be accessed during sleeping, fix use-after-free
* don't allow accessing /models during sleep, it is not thread-safe
* fix data race on accessing props and model_meta
* small clean up
* trailing whitespace
* rm outdated comments
* backend support
* server: support multiple generations from one prompt (OAI "n" option)
* fix invalid batch
* format oai
* clean up
* disable ctx shift
* add test
* update comments
* fix style
* add n_cmpl to docs [no ci]
* allowing using both n_cmpl and n
* llama-server: add router multi-model tests (#17704)
Add 4 test cases for model router:
- test_router_unload_model: explicit model unloading
- test_router_models_max_evicts_lru: LRU eviction with --models-max
- test_router_no_models_autoload: --no-models-autoload flag behavior
- test_router_api_key_required: API key authentication
Tests use async model loading with polling and graceful skip when
insufficient models available for eviction testing.
utils.py changes:
- Add models_max, models_dir, no_models_autoload attributes to ServerProcess
- Handle JSONDecodeError for non-JSON error responses (fallback to text)
* llama-server: update test models to new HF repos
* add offline
* llama-server: fix router LRU eviction test and add preloading
Fix eviction test: load 2 models first, verify state, then load
3rd to trigger eviction. Previous logic loaded all 3 at once,
causing first model to be evicted before verification could occur.
Add module fixture to preload models via ServerPreset.load_all()
and mark test presets as offline to use cached models
* llama-server: fix split model download on Windows
---------
Co-authored-by: Xuan-Son Nguyen <thichthat@gmail.com>
* server : add Anthropic Messages API support
* remove -@pytest.mark.slow from tool calling/jinja tests
* server : remove unused code and slow/skip on test_anthropic_vision_base64_with_multimodal_model in test_anthropic_api.py
* server : removed redundant n field logic in anthropic_params_from_json
* server : use single error object instead of error_array in streaming response handler for /v1/chat/completions and use unordered_set instead of set in to_json_anthropic_stream()
* server : refactor Anthropic API to use OAI conversion
* make sure basic test always go first
* clean up
* clean up api key check, add test
---------
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
* kv-cache : pad the size of the small SWA cache for performance
* context : pad the total context to 256
* cont : future-proof the swa pad
* server : adjust test params to new logic
* server : support unified context across slots
* cont : fix speculative decoding initialization
* context : fix n_ctx_per_seq computation
* server : purge slots one by one
* tests : add unified cache server tests
* llama : update per-seq context computation
* test-thread-safety : handle tiny training context of the input model
* server : fix server_tokens clear()
* server : use 4 slots + unified KV by default
* llama : add note about context size queries
* cont : update todos [no ci]
* context : do not cap the size of the context
* tests : adjust parameters to be CI friendlier
* context : add warning
* server / ranking : add sorting and management of top_n
* Make the retro compatible if no top_n will return
all results
here is a script to make some test
```script
URL=${1:-http://127.0.0.1:8181}
curl "$URL/v1/rerank" -H "Content-Type: application/json" \
-d '{ "model": "M", "query": "What is the recipe to make bread ?",
"return_text" : true,
"texts" : true,
"top_n": 6,
"documents": [
"voici la recette pour faire du pain, il faut de la farine de l eau et du levain et du sel",
"it is a bear",
"bread recipe : floor, water, yest, salt",
"The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.",
"here is the ingedients to bake bread : 500g floor, 350g water, 120g fresh refresh yest, 15g salt",
"recipe to make cookies : floor, eggs, water, chocolat",
"here is the recipe to make bread : 500g floor, 350g water, 120g fresh refresh yest, 15g salt",
"il fait tres beau aujourd hui",
"je n ai pas faim, je ne veux pas manger",
"je suis a paris"
] }' | jq
```
* use resize() instead for(...)
* simplify top_n init since no need to return error
result to test :
./tests.sh unit/test_rerank.py -v -x
==================================================== test session starts =====================================================
platform linux -- Python 3.12.3, pytest-8.3.5, pluggy-1.6.0 -- /home/yann/dev/yann/llama.cpp/tools/server/tests/test/bin/python3
cachedir: .pytest_cache
rootdir: /home/yann/dev/yann/llama.cpp/tools/server/tests
configfile: pytest.ini
plugins: anyio-4.11.0
collected 8 items
unit/test_rerank.py::test_rerank PASSED [ 12%]
unit/test_rerank.py::test_rerank_tei_format PASSED [ 25%]
unit/test_rerank.py::test_invalid_rerank_req[documents0] PASSED [ 37%]
unit/test_rerank.py::test_invalid_rerank_req[None] PASSED [ 50%]
unit/test_rerank.py::test_invalid_rerank_req[123] PASSED [ 62%]
unit/test_rerank.py::test_invalid_rerank_req[documents3] PASSED [ 75%]
unit/test_rerank.py::test_rerank_usage[Machine learning is-A machine-Learning is-19] PASSED [ 87%]
unit/test_rerank.py::test_rerank_usage[Which city?-Machine learning is -Paris, capitale de la-26] PASSED [100%]
===================================================== 8 passed in 4.31s ======================================================
* add rerank top_n unit test
here is the result :
./tests.sh unit/test_rerank.py -v -x
=================================================================== test session starts ===================================================================
platform linux -- Python 3.12.3, pytest-8.3.5, pluggy-1.6.0 -- /home/yann/dev/yann/llama.cpp/tools/server/tests/test/bin/python3
cachedir: .pytest_cache
rootdir: /home/yann/dev/yann/llama.cpp/tools/server/tests
configfile: pytest.ini
plugins: anyio-4.11.0
collected 16 items
unit/test_rerank.py::test_rerank PASSED [ 6%]
unit/test_rerank.py::test_rerank_tei_format PASSED [ 12%]
unit/test_rerank.py::test_invalid_rerank_req[documents0] PASSED [ 18%]
unit/test_rerank.py::test_invalid_rerank_req[None] PASSED [ 25%]
unit/test_rerank.py::test_invalid_rerank_req[123] PASSED [ 31%]
unit/test_rerank.py::test_invalid_rerank_req[documents3] PASSED [ 37%]
unit/test_rerank.py::test_rerank_usage[Machine learning is-A machine-Learning is-19] PASSED [ 43%]
unit/test_rerank.py::test_rerank_usage[Which city?-Machine learning is -Paris, capitale de la-26] PASSED [ 50%]
unit/test_rerank.py::test_rerank_top_n[None-4] PASSED [ 56%]
unit/test_rerank.py::test_rerank_top_n[2-2] PASSED [ 62%]
unit/test_rerank.py::test_rerank_top_n[4-4] PASSED [ 68%]
unit/test_rerank.py::test_rerank_top_n[99-4] PASSED [ 75%]
unit/test_rerank.py::test_rerank_tei_top_n[None-4] PASSED [ 81%]
unit/test_rerank.py::test_rerank_tei_top_n[2-2] PASSED [ 87%]
unit/test_rerank.py::test_rerank_tei_top_n[4-4] PASSED [ 93%]
unit/test_rerank.py::test_rerank_tei_top_n[99-4] PASSED [100%]
=================================================================== 16 passed in 8.84s ===================================================================
* editor config check fix
In streaming mode when prompt exceeds context length, the server returns
HTTP 200 status code with a JSON error in the body. This is very
confusing and inconsistent with all other inference engines which return
HTTP 4xx error in this case.
This patch fixes this problem and makes the server return HTTP 400 in
such cases.
* minor : code style
* server : fix prompt similarity calculation
* server : initial host-memory prompt caching
* cont
* server : refactor
* cont
* cont : make the server task of the slot const
* cont : minor [no ci]
* server : cache prompts and checkpoints only for completion tasks
* server : improve prompt caching logic
* cont : fix check for number of cached prompts [no ci]
* server : improve caching logic, add -cram CLI arg
* server : print prompt mismatch info
* cont : better naming [no ci]
* server : improve prompt cache loading logic
* server : add option to debug the slot contents (#16482)
* server : add option to debug the slot contents
* Update tools/server/server.cpp
---------
Co-authored-by: Xuan-Son Nguyen <son@huggingface.co>
* server : add option to disable prompt cache
---------
Co-authored-by: Xuan-Son Nguyen <son@huggingface.co>
This commit adds support for using an externally started llama-server
instance for the server tests. This can be enabled by setting the
DEBUG_EXTERNAL environment variable.
The motivation for this is to allow debugging of the server itself
when investigating a test failure. Instructions for how to do this are
added to the README.md file in the tests directory.
* server : include usage statistics only when user request them
When serving the OpenAI compatible API, we should check if
{"stream_options": {"include_usage": true} is set in the request when
deciding whether we should send usage statistics
closes: #16048
* add unit test
