In #11910 we started sanitizing function names, but we accidentally were
modifying built-ins like `browser.open` to `browser_open`. This was
removing the special prompt rendering for built-ins, but this wasn't
immediately apparent since the models seem to be reasonably good at
remembering the built-ins even when presented with these slightly
renamed version. This fix prevents built-ins from ever being renamed.
* auth: fix problems with the ollama keypairs
This change adds several fixes including:
- reading in the pubkey files correctly
- fixing the push unit test to create a keypair file in a temp directory
- not return 500 errors for normal status error
* tests: add single threaded history test
Also tidies up some existing tests to handle more model output variation
* test: add support for testing specific architectures
In <https://github.com/ollama/ollama/issues/12357> we that the model
will output tool calls such as
```
<function=shell>
<parameter=command>
pwd && ls -la
</parameter>
</function>
```
We parse this using the approach of transforming into valid xml and then
using an xml parser. While we do transform the function and parameter
names, we weren't escaping the parameter values (which in this example
are invalid since `pwd && ls -la` contains unescaped ampersands).
This has been fixed by first transforming the tags in the same way, and
then walking the transformed string and escaping the text in between the
tags. This also fixes a case where `<` in the middle of a parameter
value would cause an xml parse failure.
Fixes: #12357
with #12181, there's now support for embeddings in ollama engine.
this is done by mutating the architecture and adding _embed when it
detects an embedding model. however this introduced a bug where if
an embedding model was run based on an existing ollama engine model
without an embedding implementation, e.g. llama4, it will pass the
initial arch support check but fail when actually loaded.
there's currently two entrypoints to creating a model. previously this
second entrypoint was necessary because calling model.New would also
load the model. since #11818, this is no longer th case so merge them
to reduce complexity
With the addition of cuda v13, on a clean setup, the level of parallelism
was causing docker desktop to become overwhelmed and compilers
were crashing. This limits to 8 parallel per build stage, with the ability
to override if you have many more cores available.
Now that we have a built-in parser abstraction, which was introduced in
<https://github.com/ollama/ollama/pull/12248>, we can modify our harmony
parser to match this and then get rid of nearly all of the
harmony-specific logic in routes.go. We do have a small amount of
code that turns the parser on by default if the architecture matches and
no other built-in parser was provided.
The built-in parser interface was modified in order to handle harmony's
prefill and tool name translation requirements.
This change moves back to converting bf16 vision weights to fp16,
specifically if they start with the name "v." (such as v.blk.0.attn_k.weight).
This fixes a bug where converted images are failing because they are trying
to call `im2col` which doesn't have a bf16 kernel in ggml.
The format qwen3-coder uses is relatively unique, both in rendering and
in parsing. To implement parsing, I wrote a custom parser in similar
style to harmony. For the rendering, I found that the logic would be
much more difficult to follow in a template, so I introduced the concept
of a built-in renderer that uses go code, rather than a template to
generate prompts.
I set us up for future built-in parsers and renderers by making it so
they can be specified in a Modelfile like so:
```
RENDERER "qwen3-coder"
PARSER "qwen3-coder"
```
These need to be provided explicitly because the architecture alone is
not enough to understand what format the model expects to receive, and
what format we expect it to output (e.g., qwen3-coder is `qwen3moe`,
which includes other qwen3-family models as well)
I haven't converted harmony to be one of these "built-ins" yet, since
some of it is in flux with the changes @ParthSareen has been making to
move harmony to the runner. It is likely that many other built-ins will
need to move to the runner as well, but I'm able to slightly defer that
decision since qwen3-coder doesn't have thinking (and therefore doesn't
need to be in the runner to make structured outputs work). I expect to
unify harmony with this approach very soon.
Whether a particular model supports tools or thinking was previously
inferred from templates, but without a template we now also use the
parser itself to declare what it supports. If we have future models that
re-use the same parsing format, but have different capabilities, we'll
want to parameterize them and give them different names to be specified
as a `PARSER`.
Misc changes:
- I worked on the renderer by diffing outputs from the reference
implementation and ours. To make it easier to do this, I extended
<https://github.com/ollama/ollama/pull/11875> to also support
returning the prompt via the openai compat layer
Ollama's recent engine update, llama.cpp, caused all models requiring a slice schema to not display images. As a result, the value of numTokens isn't always the length of the sliced image embed, but rather the end length of the schema. This causes the image embed to not be correctly included during all slice processing.
If a model with a split vision projector is loaded in the Ollama
engine, the projector will be ignored and the model will hallucinate
a response. Instead, fallback and try to load the model in the llama
engine.
New memory estimates (see #11090 for more information) are now
enabled automatically for all models running on the Ollama engine,
improving both stability and performance through more accurate sizing
and allocation. Models running on the llama engine will continue to
use the original style of memory estimation.
Our new engine implementation of gemma2 doesn't support flash
attention, which means that it also doesn't support KV cache
quantization. Currently, it is possible to turn these two on,
which will result in a crash.
If flash attention is enabled without KV cache quanitization, we will
currently always get this warning:
level=WARN source=server.go:226 msg="kv cache type not supported by model" type=""
