* add option --tensor-type-file to llama-quantize, but it raises an error.
* add error message when file not found
* quantize: update help menu, fix CI
Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>
---------
Signed-off-by: Aaron Teo <aaron.teo1@ibm.com>
Co-authored-by: Your Name <you@example.com>
Co-authored-by: Aaron Teo <aaron.teo1@ibm.com>
* common : use two decimal places for float arg help messages
This commit updates the help messages for various command-line arguments
in arg.cpp to display floating-point default values with two decimal
places instead of one.
The motivation for this changes is that currently only having one decimal
place means that values generated using --help or llama-gen-docs will not
display the correct values.
For example, currently the value of top-p in tools/server/README.md is
`0.9`, but the default value is actually '0.95'. And running
llama-gen-docs does not update this value as it uses the output from the
help message, which shows only one decimal place, so the values look
like they are unchanged.
* docs : run llama-gen-docs to update docs
* Move `task_result_state::update_chat_msg` to match with header
* Move `server_task_result_cmpl_partial::to_json_anthropic()` to match with header
---------
Co-authored-by: openingnow <>
* 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 <>
* initial commit for branch
* simplify constants
* add params to `struct common_params_sampling`, add reference to PR
* explicitly clamp `min_target` and `max_target` to `[0.0, 1.0]`
* add args, rename `queue_size` -> `window_size`
* improved comments
* minor
* remove old unused code from algorithm
* minor
* add power law case to `common_sampler_init`, add sampler name mappings
* clarify behaviour when `window_size = 0`
* add missing enums
* remove `target_range` param, make `target == 1` no-op, cleanup code
* oops, straggler
* add missing parameters in `server-task.cpp`
* copy from author
ref:
https://gist.github.com/MrJackSpade/9be99c7efbba7b95a41377e123b7b069
* remove old debug log, style nit
* fix compiler warning, add commented-out logging per token
* re-write + change parameters + simplify
* oops forgot args.cpp
* fix leftover `window_size`
* add missing values to `common_params_sampling::print()`
* with logging
* does this fix it?
* no, but does this?
* update default decay
* optimize
* fix bad merge
my git skills are lacking
* silence `missing initializer for member`
* update default decay to 0.9
* fix logging
* format (double)
* add power law to the new `samplers` vector
* log sampler init values
* improve logging messages in llama_sampler_power_law
* remove extraneous logging
* simplify target computation
last commit with debug logging!
* remove debug logging, explicitly clamp params at init
* add `use_power_law` flag + logic, minor cleanup
* update `power-law` -> `adaptive-p`
* fix cold start EMA
- `ctx->weighted_sum` is now initialized and reset to `target / (1.0f -
clamped_decay)`
- `ctx->total_weight` is now initialized and reset to `1.0f / (1.0f -
clamped_decay)`
this fixes a "cold start" problem with the moving average
* update `SHARPNESS` constant to `10.0f`
* minor style fixes
no functional changes
* minor style fixes cont.
* update `llama_sampler_adaptive_p_i` for backend sampling (ref: #17004)
* separate into `apply` + `accept` functions
* `pending_token_idx`: switch from `llama_token` to `int32`
functionally identical (`llama.h` has `typedef int32_t llama_token;`),
but its more correct now
* don't transform logits <= -1e9f
* fix masking in backend top-p, min-p
* address review comments
* typo in comments `RND` -> `RNG`
* add docs
* add recommended values in completion docs
* address PR feedback
* remove trailing whitespace (for CI `editorconfig`)
* add to adaptive-p to `common_sampler_types_from_chars`
* 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 arg for disabling prompt caching
Disabling prompt caching is useful for clients who are restricted to
sending only OpenAI-compat requests and want deterministic
responses.
* address review comments
* address review comments
* Add Gemma3nVisionModel - MobileNetV5 vision encoder convertor to convert_hf_to_gguf.py. Add gemma3n to vision projectors in gguf-py/gguf/constants.py.
* Add mobilenetv5 impl
* Fix comments, remove unused vars
* Fix permute and remove transpose of projection weights
* Fix comments, remove debugging prints from hf_to_gguf
* 1. Hard-code image_mean = 0 and image_std = 1
2. Use available tensor mapping logic
3. Remove redundant chat template replacement of soft tokens placeholder with media placeholder
* 1. Move mobilenetv5 helpers declarations to `clip_graph_mobilenetv5` struct and definitions to mobilenetv5.cpp
2.Remove unused `clip_is_gemma3n` func declarations and definitions
3. Remove redundant `rescale_image_u8_to_f32` func and use `normalize_image_u8_to_f32` with zero mean and unit std
4. Calculate n_patches using image_size / patch_size
* Remove obsolete comments
* - convert_hf_to_gguf.py & constants.py & tensor_mapping.py: Use explicit mapping: Custom map for double indexed blocks and tensor_mapping.py for rest
- convert_hf_to_gguf.py: Unsqueeze Stem Bias and Layer scale tensors to correct shape while converting to gguf
- mobilenetv5.cpp: Remove explicit reshaping of Stem Bias and Layer scale which are now handled while converting to gguf, replace fprintf with LOG_*
- clip.cpp: Remove unused embedding and hard_emb_norm tensor loading
* - Rename tensors to v.conv..., v.blk..., v.msfa... to better align with already existing terminology
* Fix stem conv bias name
* Remove explicit handling of bias term for stem conv
* - Change order of addition in "project_per_layer_inputs" to support broadcasting of vision inp_per_layer
- Simplify the vision embeddings path of "get_per_layer_inputs" to output [n_embd_altup, n_layer, 1], broadcastable
* clean up conversion script
* fix code style
* also preserve audio tensors
* trailing space
* split arch A and V
* rm unused gemma3 func
* fix alignment
---------
Co-authored-by: Xuan Son Nguyen <son@huggingface.co>
Change is decoupled from https://github.com/ggml-org/llama.cpp/pull/18641.
[LFM2.5-Audio-1.5B](https://huggingface.co/LiquidAI/LFM2.5-Audio-1.5B)
needs streaming istft for generating output audio.
* add streaming ISTFT class (`mtmd_audio_streaming_istft`) with overlap-add for audio reconstruction
* replace global audio cache with per-instance cache, the model requires
two independent caches, for preprocessing (audio input) and for istft
(audio output).
* unified templated FFT/IFFT implementation supporting both forward and inverse transforms
* 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.
pockers21
EugeoSynthesisThirtyTwo
tc-mb
Georgi Gerganov
Andrew Marshall
Sascha Rogmann
liyang
Daniel Bevenius
Johannes Gäßler
Aldehir Rojas
Xuan-Son Nguyen
손희준
Hendrik Erz
Adrien Gallouët
Lennart Austenfeld
Tarek Dakhran
Julius Tischbein
ddh0
Piotr Wilkin (ilintar)
Ruben Ortlam
Radoslav Gerganov
Simranjeet Singh
Pascal
Asbjørn Olling
R