* support interns1-mini

* fix comment

* update

ggml-ci

Signed-off-by: Weizhao Ouyang <[email protected]>

…5562)

* batched-bench : fix unified KV cache handling + pp timing

* cont : run dummy token only with split KV cache

…ml-org#15557)

* model-conversion: add model card template for embeddings [no ci]

This commit adds a separate model card template (model repository
README.md template) for embedding models.

The motivation for this is that there server command for the embedding
model is a little different and some addition information can be useful
in the model card for embedding models which might not be directly
relevant for causal models.

* squash! model-conversion: add model card template for embeddings [no ci]

Fix pyright lint error.

* remove --pooling override and clarify embd_normalize usage

…5564)

This commit explicitly sets the pooling type to 'none' in the logits.cpp
to support models that have a pooling type specified.

The motivation for this is that some models may have a pooling type set
in the model file (.gguf file) and for this specific case where we only
want to extract logits, we need to ensure that no pooling is used to
so that we are comparing raw logits and not pooled embeddings.

* CUDA: MoE helper in device code, better tile sizes

* reduce superfluous CUDA blocks

This avoids backend-dependent behavior for argmax that leads to intermittent failures.

…gml-org#15565)

* CUDA: optimize get_int_from_table_16

* CUDA: use v_perm_b32 to replace byte_perm on AMD GPUs

* revise documentation

---------

Co-authored-by: xix <[email protected]>
Co-authored-by: Johannes Gäßler <[email protected]>

row_ids only needs to hold the BN rows for the current tile.

* Add warning

* Print the devices names

* Add newlines

* Apply suggestions from code review

Co-authored-by: Johannes Gäßler <[email protected]>

* Fix vector names

---------

Co-authored-by: Johannes Gäßler <[email protected]>

* remove contiguous assertion for src0 in IM2COL

* add contiguous check in supports_op

* metal : mul_mm_id remove hdst

* metal : remove mul_mm_id hsrc1

* metal : mul_mm_id simplify + add test

* metal : opt mul_mm_id map0

* metal : optimize mul_mm_id id gathering

* metal : mul/div opt

* metal : optimize mul_mm_id_map0

ggml-ci

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

* 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

This commit adds two targets to the Makefile for quantizing of
Quantization Aware Trained (QAT) models to Q4_0 format.

The motivation for this is that this sets the token embedding and the
output tensors data types to Q8_0 instead of the default Q6_K. This is
someting that we wish to enforce for QAT Q4_0 models that are to be
uploaded to ggml-org on Huggingface to guarantee the best quality.

ggml-ci

This patch improves GEMM for FP32 Data Type on PowerPC

Implements GEMM on large blocks with configurable block size mc, nc, kc
(default: 256, 256, 256).
Packing Function optimized to access blocks as per memory layout.
GEMM Optimized to work on larger blocks.
Isolated Packing from GEMM Operations for better MMA utilization.

Verified functionality and correctness uing llama-cli and stand alone
test case (performs matmul and compares final mattrix C result with base).

Minor code refactoring changes:
Replace macro with inline function
Code Indent made consistent with 4 spaces

Performance Testing:

Observed 50% ~ 70% improvement in Prompt Processing Speed mesured using
llama-bench with Meta-Llama3-8B FP32 Model.  Similar gains observed with
Mistral-7b-Instruct-v0.3 Model.

model                   Size                Params     Backend       Threads   Test    Patch   Base
llama 8B all F32        29.92 GiB           8.03 B      CPU           20       pp512   98.58   60.3
llama 8B all F32        29.92 GiB           8.03 B      CPU           20       pp1024  95.88   57.36
llama 8B all F32        29.92 GiB           8.03 B      CPU           20       pp2048  85.46   53.26
llama 8B all F32        29.92 GiB           8.03 B      CPU           20       pp4096  68.66   45.78
llama 8B all F32        29.92 GiB           8.03 B      CPU           20       pp6144  57.35   40.44

25 ~ 30% improvement in llama-batched-bench with Metla-Llama3-8B in
Prompt Processing Speed for large prompts (256, 512, 1024, 2048, 4096)tokens with various batch
sizes ( 1, 2, 4, 8, 16)

Signed-off-by: Shalini Salomi Bodapati <[email protected]>

…gml-org#15592)

The original implementation unconditionally returned true for this operation, leading to a failure when the tensor's first dimension (ne[0]) was not a multiple of WARP_SIZE. This caused an GGML_ASSERT(ncols % WARP_SIZE == 0) failure in ggml-sycl/norm.cpp.

This change updates the ggml_backend_sycl_device_supports_op check to correctly return true for GGML_OP_RMS_NORM only when the first dimension of the tensor is a multiple of WARP_SIZE, ensuring the operation can be performed without error.