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* incremental gguf parser
* gguf: update test to not rely on gguf on disc
* re-use existing create gguf
* read capabilities from gguf kv
* kv exists
* update tests
* s/doneFunc/successFunc/g
* new buffered reader

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Co-authored-by: Bruce MacDonald <brucewmacdonald@gmail.com>

The current splitDim function only operates on tensors that are split evenly which isn't always the case, e.g. a QKV tensor. This change allows the function to be used for arbitrary splits

if tokenizer.json is already copied, skip tokenizer.model

while nn.Linear.Forward isn't applicable for sparse MLP, it's still
a nice container for the tensors

When a user elects to keep the existing app, the
new Ollama is named `Ollama 2.app`
This fixes the app startup flow to handle this naming pattern.

Give the desktop app a hint to start fast.

This reverts commit 09430011.

When starting the app in the background, start it hidden.

Fix an array out of bounds crash

move thinking logic into its own package

export ThinkingParser

This enables matching up devices and information reported by the backend
with system management libraries such as nvml to get accurate free
memory reporting.

"POST predict" basically means that the runner has crashed, which
can have many reasons. However, many people think this is a specific
error and either report only this message or group together unrelated
bugs. This replaces it with a more friendly and helpful message.

- Both `/api/generate` and `/api/chat` now accept a `"think"`
  option that allows specifying whether thinking mode should be on or
  not
- Templates get passed this new option so, e.g., qwen3's template can
  put `/think` or `/no_think` in the system prompt depending on the
  value of the setting
- Models' thinking support is inferred by inspecting model templates.
  The prefix and suffix the parser uses to identify thinking support is
  also automatically inferred from templates
- Thinking control & parsing is opt-in via the API to prevent breaking
  existing API consumers. If the `"think"` option is not specified, the
  behavior is unchanged from previous versions of ollama
- Add parsing for thinking blocks in both streaming/non-streaming mode
  in both `/generate` and `/chat`
- Update the CLI to make use of these changes. Users can pass `--think`
  or `--think=false` to control thinking, or during an interactive
  session they can use the commands `/set think` or `/set nothink`
- A `--hidethinking` option has also been added to the CLI. This makes
  it easy to use thinking in scripting scenarios like
  `ollama run qwen3 --think --hidethinking "my question here"` where you
  just want to see the answer but still want the benefits of thinking
  models

If OLLAMA_AUTH is set, sign each request w/ a timestamp and pass the signature in the token header

Computing an attention mask for a large context and max batch is
expensive - over 100ms. Models like Gemma3 that have multiple types
of caches and custom attention masks need to do this 4 times, so this
adds approximately 500ms to startup time when using 128k context

When we are reserving the worst case graph, we don't need the mask,
only its shape, so we can skip this.

Signed-off-by: Kyle Steere <kyle.steere@chainguard.dev>

This commit updates the README to include macLlama within the community integrations section.

macLlama is a native macOS application built for lightweight and efficient LLM interaction.  Key features include:

*   **Lightweight & Native:** Designed to be resource-friendly and perform optimally on macOS.
*   **Chat-like Interface:** Provides a user-friendly, conversational interface.
*   **Multiple Window Support:** Allows users to manage multiple conversations simultaneously.

The primary goal of macLlama is to offer a simple and easy-to-run LLM experience on macOS.

FromFloatSlice and FromIntSlice return an error if the shape doesn't
match the passed data or if memory can't be allocated. Since these
are inputs, the memory being allocated is system memory rather than VRAM.

In many cases, the caller can't really handle the error and panics.

Empty and Zeros directly panic if they can't allocate memory.

This makes things consistent by panicing for the first two cases,
removing a fair amount of error handling code. This is also consistent
with how Go typically handles these situations.

This provides granular information about the backend memory allocations
required by the runner:
 - Per backend
 - Per layer
 - Weights, cache and graph
 - Allocation status

This can be used for debugging and validating memory estimates.

GGML has a function to report the allocated size of a backend buffer.
However, this returns 0 if we tried to allocate a buffer and it failed.
For memory management purposes, it's important to know how much we were
trying to allocate. This extends the API to report attempted sizes for
all buffers and whether it succeeeded.