* quantize : imatrix-fail early + code cleanup
* fix manual override printing
it's in the preliminary loop now, so needs to be on its own line
* revert header changes per ggerganov
* remove old #includes
* clarify naming
rename `tensor_quantization` to `tensor_typo_option` to descirbe its
functionality
* fix per barto
* tests: add end-to-end tests per model architecture
* fixup for rebase
* fix use-after-free in llama-model-loader.cpp
* fix CI
* fix WebGPU
* fix CI
* disable CI for macOS-latest-cmake-arm64
* use expert_weights_scale only if != 0.0f
* comments
* models : add llm_build_delta_net_base
* cont : keep qwen35 and qwen35moe graphs intact
* cont : add comments [no ci]
* add kimi linear to delta-net-base
* removed unnecessary ggml_cont from g_exp_t
* removed ggml_cont from g_diff_exp_t. moved ggml_cont for o to kimi-linear.cpp
* removed unnecessary diag mask
* cont : simplify
* cont : avoid graph splits
* scale q after mul instead of beginning
* scale q after mul instead of beginning
* identical ppl
* cont : fix scale and decay mask
* minor : remove TODO
* block implementation for kda
* remove space at the end of line 101
* concat+pad
* pad+binary row concat
* chunk size 16 for kda
* removed minor differences to master
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* model : fix Qwen3.5 model type detection
* Update src/llama-model.cpp
whoops, my bad
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
Many models have vocabulary sizes, and thus tensor shapes, with more
than 5 digits (ex: Gemma 3's vocab size is 262,208).
I already fixed this for `llama_format_tensor_shape` but missed it for
`llama_format_tensor_shape` until now. Oops.
* WIP: Add EuroBERT support with autoformatting changes
This commit includes:
- EuroBERT model implementation for GGUF conversion
- C++ backend support for EuroBERT architecture
- Unintended autoformatting changes to Python files
Saving before reverting formatting-only changes.
* feat: add back eos assert when not last token pooling
* feat: removed duplicated code and cleanup
* feat: removed not working architectures and unnecessary check
* fix: typo
* fix: dynamic pooling config
* feat: added an example model for eurobert
* feat: proper llama-vocab implementation for jina-v5
* fix: removed unnecessary comments
* llama : remove write/read of output ids/logits/embeddings
This commit removes the write/read of output ids, logits and
embeddings from the llama context state.
Refs: https://github.com/ggml-org/llama.cpp/pull/18862#issuecomment-3756330941
* completion : add replying of session state
This commit updates the session handing in the completion tool to handle
the that logits are no longer stored in the session file. Instead, we
need to replay the last token to get the logits for sampling.
* common : add common_prompt_batch_decode function
This commit adds a new function which is responsible for decoding prompt
and optionally handle the saving for session data.
* update save-state.cpp to use llama_state_load_file
This commit updates the save-load-state example to utilize the new
llama_state_load_file function for loading the model state from a file.
And it also replays the last token after loading since this state is now
stored before the last token is processed.
* examples : set n_seq_max = 2 for ctx3
This commit updates the save-load-state example to set the n_seq_max
parameter to 2 when initializing the ctx3 context.
The motivation for this change is that using 1 as n_parallel/n_seq_max
the context only supports one sequence, but the test laster tries to
use a second sequence which results in the following error:
```console
main : loaded state with 4 tokens
main : seq 0 copied, 225760 bytes
main : kv cache cleared
find_slot: seq_id=1 >= n_seq_max=1 Try using a bigger --parallel value
state_read_meta: failed to find available cells in kv cache
```
This seems to only happen for recurrent/hybrid models.
* model: add JAIS-2 architecture support
Add support for the JAIS-2 family of Arabic-English bilingual models
from Inception AI (https://huggingface.co/inceptionai/Jais-2-8B-Chat).
Architecture characteristics:
- LayerNorm (not RMSNorm) with biases
- ReLU² (ReLU squared) activation function
- Separate Q/K/V projections with biases
- Simple MLP without gate projection (up -> act -> down)
- RoPE positional embeddings
- GPT-2 BPE tokenizer
Supported model sizes:
- Jais-2-8B (32 layers, 26 heads, 3328 hidden)
- Jais-2-70B (68 layers, 56 heads, 7168 hidden)
Tested with quantizations: BF16, Q8_0, Q6_K, Q5_K_M, Q5_0, Q4_K_M, Q4_0, Q3_K_M, Q2_K
Note: JAIS-2 requires F32 precision accumulators for numerical stability
and uses standard attention (not flash attention) on CUDA backends.
* fix: run convert_hf_to_gguf_update.py for jais-2 tokenizer hash
* fix: use NEOX RoPE type for JAIS2
* fix: remove Q/K permutation (NEOX RoPE doesn't need it)
* fix: enable flash attention for JAIS2 (fixed by #19115)
* fix: add dedicated JAIS2 pre-tokenizer type and control vector support
- Add LLAMA_VOCAB_PRE_TYPE_JAIS2 with cascading whitespace regex
- Include original regex from tokenizer.json as comment
- Add build_cvec call for control vector support
* no longer necessary to override set_vocab
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* model : Add tokenizer from LFM2.5-Audio-1.5B
[LFM2.5-Audio-1.5B](https://huggingface.co/LiquidAI/LFM2.5-Audio-1.5B) introduced lightweight audio tokenizer.
Tokenizer based on LFM2 architecture and acts as "embedding" model with
different input `n_embd` and output `n_embd_out`.
To be used in https://github.com/ggml-org/llama.cpp/pull/18641.
To convert use
```shell
python3 convert_hf_to_gguf.py /path/to/LFM2.5-Audio-1.5B/audio_detokenizer
```
* Update convert_hf_to_gguf.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Formatting
* Rework check for attention layers
* Add LFM2 SWA model support
* Address PR feedback
* Set vocab to none
* Move helper function definitions to cpp file
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
This commit updates get_logits_ith(), and get_embeddings_ith() to use
output_resolve_row() to resolve the batch index to output row index.
The motivation for this is to remove some code duplication between these
functions.
* full modern bert support
* added gelu op in rank pooling for modern bert
* still working on stuff, added mean calculation before classifier head
* Update convert_hf_to_gguf.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* first layer is dense, as per modern bert research paper
* Update src/llama-graph.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* fixed set input for mean pooling to check if pooling type is ranking since modern bert does mean & rank
* Update src/llama-graph.cpp
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Update convert_hf_to_gguf.py
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* models : add llm_build_delta_net_base
* cont : keep qwen35 and qwen35moe graphs intact
* cont : add comments [no ci]
* add kimi linear to delta-net-base
* removed unnecessary ggml_cont from g_exp_t
* removed ggml_cont from g_diff_exp_t. moved ggml_cont for o to kimi-linear.cpp
* removed unnecessary diag mask
* cont : simplify
* cont : avoid graph splits
* scale q after mul instead of beginning
* scale q after mul instead of beginning
* identical ppl
* cont : fix scale and decay mask
* minor : remove TODO
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* changes for tiny aya
* changes to hash
* changes to vocab
* fix some tokenizer regex edge cases
* update comment
* add some comments for regex
* Apply suggestion from @ngxson
---------
Co-authored-by: Xuan-Son Nguyen <thichthat@gmail.com>
* Refactoring to use new llama_put_adapter_loras
* cont : alternative lora API
---------
Co-authored-by: Jake Chavis <jakechavis6@gmail.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* ggml: added cleanups in ggml_quantize_free
Add missing cleanup calls for IQ2_S, IQ1_M quantization types and IQ3XS with 512 blocks during quantization cleanup.
* mmap: Fix Windows handle lifetime
Move hMapping from local variable to member variable so it stays alive for the entire lifetime of the mapping.
The file mapping handle must remain valid until UnmapViewOfFile is called.
Fixes cleanup order in destructor.
* Update llama-mmap.cpp
* Update llama-mmap.cpp
Remove trailing whitespace from line 567
Using the same conversion function ensures a consistent matching between
the regex pattern and the text.
Signed-off-by: Adrien Gallouët <angt@huggingface.co>
* llama : refactor sampling_info to use buffer_view template
This commit updates the sampling_info struct in llama-context to use a
buffer_view template for the logits, probs, sampled tokens, and
candidates buffers.
The motivation for this is to simplify the code, improve type safety
and readability.
* support qwen3.5 series
* remove deepstack for now, and some code clean
* code clean
* add FULL_ATTENTION_INTERVAL metadata
* code clean
* reorder v heads for linear attention to avoid expensive interleaved repeat
* Unified delta net handling
* Remove old methods.
* Refactor and optimize
* Adapt autoregressive version from @ymcki
* Change to decay mask approach
* Fix bad permute
* Qwen 3.5 support
* Apply suggestions from code review
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Further fixes
* Use inheritance, remove unneeded conts
* Not like this!
* Remove ggml.h explicit import
* Remove transformers, fix the views
* ACTUALLY fix views, make super calls explicit in conversion.
* Fix conversion again
* Remove extra ggml.h imports
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* Fix model loading regex error
* Change comments
* Use const_iterator and remove specializations
---------
Co-authored-by: Alde Rojas <hello@alde.dev>
* kimi linear model implementation
* kimi linear convert_hf_to_gguf
* kimi linear constants.py tensor_mapping.py
* Kimi Linear ggml.h
* kimi linear ggml-cpu
* Kimi Linear ggml-cuda
* Kimi Linear ggml.c
* kimi linear src/llama
* remove "const int64_t n_seq_tokens = q->ne[2];" to get rid of unused variable warning
* remove type mismatch warning
* read MoE params
* removed some hard coded code
* removed all hard code
* use DeepseekV2 tokenizer
* removed unnecessary internal methods called by the old set_vocab of KimiLinear
* rewrite get_vocab for KimiLinear. Removed all kda_scan code
* removed all traces of kda_scan
* reduce OP count by 1 due to removal of kda_scan
* Move KIMI_LINEAR to llm_arch_is_hybrid to enable KV cache
* set n_embd_head_k/v to ensure kv cache works
* don't quantize conv1d of Kimi Linear
* Kimi Linear backend agnostic
* removed LOG_INFO
* naive chunking form implemented
* fixed some comments
* add Kimi-K2 specific tokens to be recognized as EOG
* build_kda_autoregressive is implemented to replace build_kda_recurrent for faster inference. sync'd to b7682
* replaced Akk and Aqk with mul_mat and clamp
* no clamp version
* Moved Aqk computation out of the loop
* fixed typo and split wkv_b into wk_b and wv_b
* MLA KV cache support
* fix trailing spaces
* moved const llama_model & model; around to follow qwen3next format and see if it cna pass the -Wunused-private-field error
* fix trailing whitespace
* removed traling whitespaces in empty line + make sure indentation is multiple of 4
* try to make lint happy
* remove blank lines to make lint happy
* removed at least blank line containing white space
* fixed flake8 complaints locally
* return ggml_tensor * pair in kda_autoregressive and kda_chunking as in ngxson's Qwen3Next improvement
* removed Kimi-Linear specific change that causes failure at server-windows
* removed private: from kimi_linear to make build checks happy
* removed unnecessary ggml_cont before ggml_reshape
* created static function causal_conv1d to abtract similar code for q/k/v
* merged dt_bias to SSM_DT. Do -exp(log_A) in convert_hf_to_gguf.py.
* reverted to original
* fixed find_hparam calls. Fixed e_score_correction_bias to use bias instead of weight. Removed all ssm_conv bias terms.
* remove DT_B from constants.py. remove one comment line in llama-model.cpp
* new class llm_graph_input_mem_hybrid_k to get around the new MLA change. switch the concat order of ggml_concat calls in kimi-linear.cpp to accommodate MLA changes. Removed support for exp_probs_b.weight
* remove ssm_o_norm_b
* remove ssm_o_norm_b
* changed hparams.kda_head_dim to hparams.n_embd_head_kda. added TODO comment for class llama_graph_mem_hybrid_k
* removed all ggml_cont b4 ggml_reshape_4d
* Whitespace
* replaced all hparams.get with find_hparams
* added new names for n_experts, n_experts_used and score_func in TextModel and removed their code in KimiLinear in convert_hf_to_gguf.py. Removed unnecessary ggml_cont and GGML_ASSERT in kimi-linear.cpp
* use is_mla to switch between different mem_hybrid types
* fixed logical errors in convert_hf_to_gguf.py pointed out by CISC
* removed if else for required parameters kv_lora_rank and qk_rope_head_dim
* add back ggml_cont for Vcur
* minor changes
* removed extra line in llama-vocab.cpp. Added back the comment in llama-graph.cpp
* f16 gguf cannot run without context length
* made a mistake of adding back n_ctx parsing
---------
Co-authored-by: Piotr Wilkin (ilintar) <piotr.wilkin@syndatis.com>
This commit removes the unused tmp_buf variable from llama-kv-cache.cpp
and llama-memory-recurrent.cpp.
The tmp_buf variable was declared but never used but since it has a
non-trivial constructor/desctuctor we don't get an unused variable
warning about it.
* sampling : remove sampling branching in output_reserve
This commit updates output_reserve in llama-context.cpp to always
allocate sampling buffers regardless of whether sampling is needed for
the current batch.
The motivation for this is to avoid reallocations and branching based on
the sampling requirements of the batch.
* graph : avoid branches between embedding and token inputs
* models : make deepstack graphs (e.g. Qwen3 VL) have constant topology
* ci : enable -DGGML_SCHED_NO_REALLOC=ON for server CI
* cont : pad token embeddings to n_embd_inp
* mla : pass V as a view of K to the FA op
* cuda : adjust mla logic to new layout
* kv-cache : fix rope shift
* tests : remove comment
* cuda : fix reusable_cutoff
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
---------
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* 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`
* lora: make sure model keep track of associated adapters
* deprecate llama_adapter_lora_free
* minor : std::unordered_set over std::set
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Haiku OS does not support RLIMIT_MEMLOCK, similar to visionOS/tvOS.
Skip the resource limit check on Haiku to allow mlock functionality
to work without compile errors.
Tested on Haiku with NVIDIA RTX 3080 Ti using Vulkan backend.
* fix: Remove unnecessary `h` loops where `h` was only ever 0
Branch: CleanUpT5InputBuilders
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Remove unnecessary padding loop that is never hit anymore
The upper bound used to use GGML_PAD(n_tokens, GGML_KQ_MASK_PAD), but was
removed in https://github.com/ggml-org/llama.cpp/pull/17910 leaving the
loop dead.
Branch: CleanUpT5InputBuilders
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
---------
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* qwen3next: simplify qkvz projection
* use ggml_swiglu_split
* revert swiglu_split, but remove redundant repeat()
* fix missing reshape
* rm 2 redundant transposes
* move mul_mat(k,q) to outside of chunking
* rm redundant cont
* improve g_cs_chunk
* add comments about no cont
* use std::pair instead of ggml_concat
* vectorize key_gdiff calculation
* rm unused tensor
* avoid ggml_concat inside loop
* bring back ggml_concat as it may not work on other backend
* nits
* 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>
* Adding --direct-io flag for model loading
* Fixing read_raw() calls
* Fixing Windows read_raw_at
* Changing type off_t to size_t for windows and Renaming functions
* disable direct io when mmap is explicitly enabled
* Use read_raw_unsafe when upload_backend is available, not functional on some devices with Vulkan and SYCL
* Fallback to std::fread in case O_DIRECT fails due to bad address
* Windows: remove const keywords and unused functions
* Update src/llama-mmap.cpp
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
---------
Co-authored-by: jtischbein <jtischbein@gmail.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
ddh0
Xuan-Son Nguyen
Georgi Gerganov
ddh0
Sigbjørn Skjæret
Johannes Gäßler
Aman Gupta
Tim Neumann
ymcki
Eric Zhang
Marcel Petrick
Maximilian Werk
Tarek Dakhran
Daniel Bevenius
HelloKS
megemini
3 a l i
Ryan Mangeno
DAN™
Saurabh Dash
agent-enemy-2
George
Adrien Gallouët
JJJYmmm
Piotr Wilkin (ilintar)
forforever73
Lasse Lauwerys
Alexey Dubrov
Christian Kastner
Jakkala Mahesh
Tarek Dakhran
Julius Tischbein
Junwon Hwang
Daniel Benjaminsson
Ruben Ortlam
Gabe Goodhart
Simranjeet Singh
Aaron Teo
Julius Tischbein