The error:
./examples/sycl/test.sh: line 122: level_zero:${$GGML_SYCL_DEVICE}: bad
substitution
was thrown whenever the user used this command:
./examples/sycl/test.sh -mg 0
Fix is to get rid of a dollar sign.
* common: do not fit to unknown device memory
Signed-off-by: Florian Reinle <f.reinle@otec.de>
* common: preserve host fallback for non-GPU fit devices
Signed-off-by: Florian Reinle <f.reinle@otec.de>
* common: keep unknown GPU fit memory at zero
Signed-off-by: Florian Reinle <f.reinle@otec.de>
---------
Signed-off-by: Florian Reinle <f.reinle@otec.de>
* feat: migrate to PEP 621 and add uv support
* fix: remove upper bound on protobuf
* remove poetry.lock and uv.lock
* fix/add torch dependency version and markers
* fix dev-dependency deprecation warning
* gguf-py : update python version requirement to 3.10
---------
Co-authored-by: David Huggins-Daines <dhd@dhd.ecolingui.ca>
Co-authored-by: Daniel Bevenius <daniel.bevenius@gmail.com>
* convert : ignore non-language tensors for Gemma4Model
This commit adds a check to make sure only text language tensors are
handled in filter_tensors.
The motivation is that currently when trying to convert a Gemma4 model
the following error occurs:
```console
(venv) $ ./convert-gemma.sh
INFO:hf-to-gguf:Loading model: gemma-4-E2B-it
INFO:hf-to-gguf:Model architecture: Gemma4ForConditionalGeneration
INFO:hf-to-gguf:gguf: indexing model part 'model.safetensors'
INFO:gguf.gguf_writer:gguf: This GGUF file is for Little Endian only
INFO:hf-to-gguf:Exporting model...
INFO:hf-to-gguf:rope_freqs.weight, torch.float32 --> F32, shape = {256}
Traceback (most recent call last):
File "/home/danbev/work/llama.cpp/./convert_hf_to_gguf.py", line 13752, in <module>
main()
File "/home/danbev/work/llama.cpp/./convert_hf_to_gguf.py", line 13746, in main
model_instance.write()
File "/home/danbev/work/llama.cpp/./convert_hf_to_gguf.py", line 945, in write
self.prepare_tensors()
File "/home/danbev/work/llama.cpp/./convert_hf_to_gguf.py", line 805, in prepare_tensors
for new_name, data_torch in (self.modify_tensors(data_torch, name, bid)):
File "/home/danbev/work/llama.cpp/./convert_hf_to_gguf.py", line 7925, in modify_tensors
yield from super().modify_tensors(data_torch, name, bid)
File "/home/danbev/work/llama.cpp/./convert_hf_to_gguf.py", line 7290, in modify_tensors
yield from super().modify_tensors(data_torch, name, bid)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/danbev/work/llama.cpp/./convert_hf_to_gguf.py", line 579, in modify_tensors
new_name = self.map_tensor_name(name)
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/danbev/work/llama.cpp/./convert_hf_to_gguf.py", line 572, in map_tensor_name
raise ValueError(f"Can not map tensor {name!r}")
ValueError: Can not map tensor 'model.embed_vision.embedding_projection.weight'
```
* add forgotten embed_vision and embed_audio
* improve
---------
Co-authored-by: Sigbjørn Skjæret <sigbjorn.skjaeret@scala.com>
* hex-mm: process m-tail rows on HMX instead of HVX
* hmx-mm: unroll and optimize padded activation loop
---------
Co-authored-by: Max Krasnyansky <maxk@qti.qualcomm.com>
common/arg.cpp:3719:9: error: function 'operator()' could be declared with attribute 'noreturn' [-Werror,-Wmissing-noreturn]
3719 | [](common_params & /*params*/, int /*value*/) {
| ^
common/arg.cpp:3726:9: error: function 'operator()' could be declared with attribute 'noreturn' [-Werror,-Wmissing-noreturn]
3726 | [](common_params & /*params*/, int /*value*/) {
| ^
common/arg.cpp:3733:9: error: function 'operator()' could be declared with attribute 'noreturn' [-Werror,-Wmissing-noreturn]
3733 | [](common_params & /*params*/, int /*value*/) {
| ^
common/arg.cpp:3740:9: error: function 'operator()' could be declared with attribute 'noreturn' [-Werror,-Wmissing-noreturn]
3740 | [](common_params & /*params*/, int /*value*/) {
| ^
common/arg.cpp:3747:9: error: function 'operator()' could be declared with attribute 'noreturn' [-Werror,-Wmissing-noreturn]
3747 | [](common_params & /*params*/, int /*value*/) {
| ^
Signed-off-by: Adrien Gallouët <angt@huggingface.co>
Previously, unknown tool names passed via --tools were silently ignored.
Now the server validates each tool name at startup and exits with an
error if an unrecognized tool is specified, listing the available tools.
Assisted-by: llama.cpp:local pi
* chat/autoparser: the fixes
* Move optspace() to chat-peg-parser, comment out server tests invalidated due to content now allowed with forced tool calls.
* Trim whitespace on apply instead
* docs : update speculative decoding parameters after refactor (#22397)
Update docs/speculative.md to reflect the new parameter naming scheme
introduced in PR #22397:
- Replace --draft-max/--draft-min with --spec-draft-n-max/--spec-draft-n-min
- Replace --spec-ngram-size-n/m with per-implementation variants
- Add documentation for all new --spec-ngram-*- parameters
- Update all example commands
Assisted-by: llama.cpp:local pi
* pi : add rule to use gh CLI for GitHub resources
Assisted-by: llama.cpp:local pi
* docs : run llama-gen-docs
* arg : fix typo
* shader(norm): add layer norm ops
* shader(norm): stablize floating point computation with Kahan summation and handle mixed types
* shader(norm): remove the non-contiguous strides
* shader(norm): use the original implementation rather than the kahan summation
Llama-architecture q_proj/k_proj weights need an axis-0 row permutation
to match GGML's RoPE convention. The BF16 path applies this in
LlamaModel.modify_tensors via LlamaModel.permute, but the NVFP4 path
bypasses modify_tensors and writes weights directly through
ModelBase._repack_nvfp4. Without the permutation, attention heads end
up scrambled at inference and the model produces gibberish.
This change overrides _repack_nvfp4 on LlamaModel and applies the same
permutation to both the nibble-packed weight and the per-block scale
before delegating to ModelBase._repack_nvfp4 via super(). Reuses the
existing LlamaModel.permute static helper and respects the existing
undo_permute flag, so subclasses (Mistral, Granite, Llama4, etc.)
inherit the fix automatically.
Verified on TinyLlama-1.1B reproducer: perplexity drops from 4419
(gibberish) to 43.9, matching the BF16-dequantized baseline (44.0).
Also verified end-to-end on ALIA-40b-instruct-2601 (BSC, Llama
architecture) with multilingual generation in Spanish/Catalan/Basque/
Galician all coherent with the fix applied.
Co-authored-by: Chema <chema@montevive.ai>
