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We now do a deeper probe of CUDA devices to verify the library version has
the correct compute capability coverage for the device.  Due to ROCm also
interpreting the CUDA env var to filter AMD devices, we try to avoid setting
it which leads to problems in mixed vendor systems.  However without setting
it for this deeper probe, each CUDA library subprocess discovers all CUDA GPUs
and on systems with lots of GPUs, this can lead to hitting timeouts.  The fix is
to turn on the CUDA visibility env var just for this deeper probe use-case.

Added Vulkan SDK installation instructions and environment variable setup for building with Vulkan support.

Avoid hitting test timeouts

Model eviction happens when we have at least one other model
loaded and are unable to load all layers into VRAM. However, on
CPU-only systems we can never load layers into VRAM, so this
constantly triggered eviction.

Fixes #13227

This change:

* fixes rope scaling in the mistral converter
* updates ministral to include llama4 scaling
* includes a new ministral parser for parsing reasoning and tool calling

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Co-authored-by: jmorganca <jmorganca@gmail.com>

If the user has somehow installed another GGML based app which places a
ggml-base lib somewhere in their PATH, we can experience runtime problems
due to incompatibilities.  This change adds a warning message if we detect
a ggml-base outside of our install location to aid in troubleshooting.

While processing the response stream during a chat or generation if an error is occurred it is parsed and returned to the user. The issue with the existing code is that this assumed the response would be valid JSON, which is not a safe assumption and caused cryptic error messages to be displayed due to parsing failures:
`invalid character 'i' looking for beginning of value`

This change updates the stream function to return the raw error string if it cant be parsed as JSON. This should help with debugging issues by making sure the actual error reaches the user.

The cuda_jetpack libs will enumerate discrete GPUs on SBSA systems
which leads to runtime failures of missing kernels.  This fix
requires an exact match to enable jetpacks instead of relying on
enumeration to filter out supported libraries.

There were a few Markdown typos in one FAQ answer. It now renders as a proper ascii table.

Recent refactoring introduced a regression for filtering cuda overlap to favor newest supported version.

the check for mla omits v3 and r1 which should not return unsupported.
instead check the tokenizer for compatibility

The causal cache can store data differently depending on what is
best for the backend. We should run tests both ways.

We currently copy data into the KV cache in contiguous buffers using
ggml_cpy(). ggml_set_rows() was introduced to allow scatter operation
so that contiguous buffers are no longer required. The direct primary
benefit of this is that we no longer need to perform defragmentation.

However, GGML recently removed an optimization for ggml_cpy() and
we picked it up in 544b6739 "ggml update to b6840 (#12791)". This
caused a roughly 40% drop in token generation performance on CUDA
due to CUDA graphs no longer being used. By switching to
ggml_set_rows(), the original optimization is no longer necessary
and CUDA performance is restored.

Fixes #13112

GGML requires tensors to be contiguous for reshape and if
this is not the case, it will assert fail. Contiguous is an
expensive operation, so it's best to do it lazily when it is
actually required rather than ahead of time when it may not
be needed.

Calling abort on windows triggers the C++ runtime to attempt a debugger
attach, which causes the crashed runners to hang instead of exit, leading
to a timeout instead of a fast failure during discovery.

cuda panics on batches larger than 1024 so skip those and fallback to
cpu

* migrate to golangci-lint v2
* copyloopvar

Void is an open source AI code editor and Cursor alternative that supports
Ollama. It's built on VS Code and allows users to connect directly to Ollama
for private LLM usage without going through a middleman backend.

Key features:
- Open source Cursor alternative
- Direct Ollama integration
- VS Code fork with full compatibility
- Agent mode and MCP support
- Works with any open source model

Fixes #12919
Signed-off-by: Samaresh Kumar Singh <ssam3003@gmail.com>

* Add mla for flash attention
* Revert to using chunks

* Add supports for cgroups cores and memory limitations

* fix compile error and add logs

* remove cpu info log

* build: optimize dockerfile context for iterating

This moves the copy of the source into the layer AFTER
doing software installs so we don't have to go through
the RPM install for cuda, etc. every time you touch a
source file.

* amd: implement linux sysfs based VRAM lookup

This adds a C++ implementation of sysfs DRM VRAM discovery
for more accurate free VRAM data on linux for AMD GPUs.