* Change to warn instead of debug, to explain reason for stopping.
* Update tools/main/main.cpp
Fix printing --2
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* metal : optmize FA vec for large heads and sequences
* metal : adjust small-batch mul mv kernels
ggml-ci
* batched-bench : fix total speed computation
ggml-ci
* cont : add comments
ggml-ci
* convert : fix tensor naming conflict for llama 4 vision
* convert ok
* support kimi vision model
* clean up
* fix style
* fix calc number of output tokens
* refactor resize_position_embeddings
* add test case
* rename build fn
* correct a small bug
- Use server_tokens in more places in server and util.cpp
- Convert most functions that used llama_tokens to server_tokens
- Modify input tokenizer to handle JSON objects as subprompts
- Break out MTMD prompt parsing into utility function
- Support JSON objects with multimodal_data arrays for MTMD prompts along with other existing types
- Add capability to model endpoint to indicate if client can send multimodal data
- Add tests.
* Fix -Werror=return-type so ci/run.sh can run
* Update tools/mtmd/clip.cpp
Co-authored-by: Diego Devesa <slarengh@gmail.com>
* Remove false now that we have abort
---------
Co-authored-by: Diego Devesa <slarengh@gmail.com>
* Update docker.yml
修改docker.yml文件中的内容使其停止周期性的运行该workflow，如果想要运行该workflow可以手动启动
* feat:Modify the header file include path
1. There's no llava directory in the tools directory.
2. Because the command `target_include_directories(mtmd PUBLIC .)` is used in the `mtmd` CMakeLists.txt file, other targets that link against `mtmd` automatically include the `mtmd` directory as a search path for header files. Therefore, you can remove `target_include_directories(${TARGET} PRIVATE ../llava`` or use `target_include_directories(${TARGET} PRIVATE ../mtmd`` to explicitly require the `llama-server` target to use header files from `mtmd`.
* Restore the docker.yml file
Add tracking for high watermark cache usage and make it available in /metrics endpoint.
Use-case: Tracking largest needed cache usage under realistic workload
to better understand memory requirements and be able to adjust
cache size/quantization for model/cache accordingly.
* wip lfm2 vision model
* Fix conv weight
* Implement dynamic resolution
* Fix cuda
* support LFM2-VL-450M
* happy CI
* Remove extra `ggml_conv` and put others into the right place
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
---------
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* model : add harmony parser for gpt-oss
* gpt-oss : fix grammar trigger from causing empty stack
* gpt-oss: tweak the grammar trigger again
* gpt-oss : add support for recipient in role header
* gpt-oss : fix ungrouped tool calls in grammar
* gpt-oss : loosen function name matching during parse
* gpt-oss : clean up workarounds
* gpt-oss : add template tests
* gpt-oss : simulate thinking and tool call tags
* gpt-oss : undo think tags when reasoning_format is none
* gpt-oss : set special tokens back to user defined
* gpt-oss : update openai-gpt-oss template
* server : filter out harmony thought messages
* gpt-oss : simplify parsing
* server : add SWA checkpoints
ggml-ci
* cont : server clean-up
* server : handle state restore fails
* llama : add extended llama_state_seq_ API
* server : do not make checkpoints if --swa-full
ggml-ci
* llama : remove flags value for NONE
* server : configure number of SWA checkpoints with CLI arg
ggml-ci
* args : fix scope of new argument
* perplexity: give more information about constraints on failure
This checks whether -np is insufficient vs context, and provides clues as to how much is needed for each.
* log formatting
* log error and return instead of storing max_seq_exceeded int
* check if s0 is zero for -np check
* Checkpoint from VS Code for coding agent session
* Initial plan
* Fix typo in --override-tensor-draft flag implementation
* Add null termination for speculative tensor buffer overrides
* Apply suggestions from code review
* Apply suggestions from code review
* Extract tensor override parsing logic to common function (addresses @slaren's feedback)
* Apply suggestions from code review
* Apply suggestions
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Co-authored-by: Diego Devesa <slarengh@gmail.com>
* Fix MinicpmV model converter and clip to avoid using hardcode.
* Code update for pr/14750
* Remove unused field, update script path in docs.
* Add version 5 for fallback code.
---------
Co-authored-by: lzhang <zhanglei@modelbest.cn>
This commit updates comments and error messages to use "decode" instead
of "eval" in perplexity.cpp.
The motivation for this is that `llama_eval` was renamed to
`llama_decode` a while ago, but the comments and error messages
still referred to "eval". This change ensures consistency and clarity.
This commit addresses an issue with the convert_hf_to_gguf script
which is currently failing with:
```console
AttributeError: module 'torch' has no attribute 'uint64'
```
This occurred because safetensors expects torch.uint64 to be available
in the public API, but PyTorch 2.2.x only provides limited support for
unsigned types beyond uint8 it seems. The torch.uint64 dtype exists but
is not exposed in the standard torch namespace
(see pytorch/pytorch#58734).
PyTorch 2.4.0 properly exposes torch.uint64 in the public API, resolving
the compatibility issue with safetensors. This also required torchvision
to updated to =0.19.0 for compatibility.
Refs: https://huggingface.co/spaces/ggml-org/gguf-my-repo/discussions/186#68938de803e47d990aa087fb
Refs: https://github.com/pytorch/pytorch/issues/58734
* llama-server : implement universal assisted decoding
* Erase prompt tail for kv-cache
* set vocab_dft_compatible in common_speculative
* rename ctx_main to ctx_tgt
* move vocab_dft_compatible to spec struct
* clear mem_dft, remove mem
* detokenize id_last for incompatible models
* update comment
* add --spec-replace flag
* accept special tokens when translating between draft/main models
* Escape spec-replace
* clamp draft result to size to params.n_draft
* fix comment
* clean up code
* restore old example
* log common_speculative_are_compatible in speculative example
* fix
* Update common/speculative.cpp
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Update common/speculative.cpp
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Update common/speculative.cpp
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
This commit adds support for the `embd_normalize` parameter in the
server code.
The motivation for this is that currently if the server is started with
a pooling type that is not `none`, then Euclidean/L2 normalization will
be the normalization method used for embeddings. However, this is not
always the desired behavior, and users may want to use other
normalization (or none) and this commit allows that.
Example usage:
```console
curl --request POST \
--url http://localhost:8080/embedding \
--header "Content-Type: application/json" \
--data '{"input": "Hello world today", "embd_normalize": -1}
```
Currently if RPC servers are specified with '--rpc' and there is a local
GPU available (e.g. CUDA), the benchmark will be performed only on the
RPC device(s) but the backend result column will say "CUDA,RPC" which is
incorrect. This patch is adding all local GPU devices and makes
llama-bench consistent with llama-cli.
