This change vendors in the bfloat16 package from
github.com/d4l3k/go-bfloat16/ and fixes a memory leak which
was being caused by using unsafe pointers instead of the
math package.
Models can specify that a group of inputs need to be handled a single
batch. However, context shifting didn't respect this and could trigger
a break anyways. In this case, we should instead trigger a context
shift earlier so that it occurs before the grouped batch.
Note that there still some corner cases:
- A long prompt that exceeds the context window can get truncated
in the middle of an image. With the current models, this will
result in the model not recognizing the image at all, which is
pretty much the expected result with truncation.
- The context window is set less than the minimum batch size. The
only solution to this is to refuse to load the model with these
settings. However, this can never occur with current models and
default settings.
Since users are unlikely to run into these scenarios, fixing them is
left as a follow up.
We do not need to bypass the prompt caching in the ollama runner yet, as
only embedding models needed to bypass the prompt caching. When embedding
models are implemented they can skip initializing this cache completely.
This fixes the case where a FROM line in previous modelfile points to a
file which may/may not be present in a different ollama instance. We
shouldn't be relying on the filename though and instead just check if
the FROM line was instead a valid model name and point to that instead.
This sets the agent header in DefaultRegistry to include the version of
the client, OS, and architecture in the previous format, with a minor
twist.
Note: The version is obtained from the build info, instead of the
version in version.Version, which should not longer be necessary, but we
can remove in a future commit. Using the build info is more accurate and
also provides extra build information if the build is not tagged, and if
it is "dirty". Previously, the version was just "0.0.0" with no other
helpful information. The ollama.com registry and others handle this
swimmingly.
Previously processing multiple images in a batch would trigger
segfaults so sending images together was disabled as a way to
mitigate this. The trigger was processing one image on the CPU
and one on the GPU.
This can no longer happen:
- The vision encoder is now on the GPU so both images would be
processed on the GPU.
- We require images to be fully contained in a batch and each
image including its special tokens is over half the batch size.
As a result, we will never get two images in the same batch.
Fixes#9731
Currently there is a single context per sequence, shared all by
all multimodal inputs. Since we build a vision encoder graph per
image, with a large number of inputs we can eventually hit the
maximum number of graph nodes per context.
This changes to use a separate context for each image, ensuring
that available resource limits are consistent.
Models may require that a set of inputs all be processed as part
of the same batch. For example, if an image has multiple patches
with fully connected attention between them, we should not split
the batch in the middle of an image.
Fixes#9697
This commit refactors the LLM subsystem by removing internal subprocess
request and response types. It consolidates duplicate type definitions
across the codebase, moving them to centralized locations. The change also
standardizes interfaces between components, simplifies the ServerStatusResp
struct, and moves the ParseDurationMs function to a common package. This
cleanup reduces code duplication between different runner implementations
(llamarunner and ollamarunner).
Replace large-chunk blob downloads with parallel small-chunk
verification to solve timeout and performance issues. Registry users
experienced progressively slowing download speeds as large-chunk
transfers aged, often timing out completely.
The previous approach downloaded blobs in a few large chunks but
required a separate, single-threaded pass to read the entire blob back
from disk for verification after download completion.
This change uses the new chunksums API to fetch many smaller
chunk+digest pairs, allowing concurrent downloads and immediate
verification as each chunk arrives. Chunks are written directly to their
final positions, eliminating the entire separate verification pass.
The result is more reliable downloads that maintain speed throughout the
transfer process and significantly faster overall completion, especially
over unstable connections or with large blobs.
Add metadata and tensor information to the show command to be able to
see more information about a model. This outputs the same data as
shown on the model details page on ollama.com
Softcap isn't in the whitepaper/implementation for the language model so we should remove it. There is no discernible difference in output with it removed.
