the stream accumulator exits as soon as it sees `api.ProgressResponse(status="success")` which isn't strictly correctly
since some requests may have multiple successes, e.g. `/api/create` when the source model needs to be pulled.
If a model is loading, and the request context is canceled during the load
by a client closing the connection, and another request is inbound for the
same model with a different configuration (context size, etc.) thus requiring
a reload, two unload events can be in flight. The first shuts down the
original model load, but the second one caused the loss of the new
reloading runner reference, thus triggering the leak.
The primary fix is detecting the duplicate unload and ignoring the second
instance. The load routine is also hardened to ensure we detect
clobbering an already present runner and unload it with a warning.
* Move quantization logic to GGML via new backend
This moves the model aware logic to Go code and calls GGMLs quantization code for model creation.
* Remove "add model quantizations"
This is no longer needed now that quantization is implemented in Go+GGML code directly.
This enhances our logging in the scheduler. The initial "waiting for server" log
no longer claims an initial error state (now "not responding" which better reflects
the actual state). Runners now have slog wiring to report more details about the
runner, including PID.
* strip out thinking tags in message history for qwen3 & r1
This is in advance of "proper" support where we'll make reasoning
configurable and we'll parse out thinking/reasoning tags and provide
them to the caller. These models expect there to be no thinking tags in
the message history, so this should improve quality
* parse model names instead of hacky prefix check
* Adjust initial scheduler refCount
Ensure we only set the refCount on success
* sched: fix lock order inversion deadlock
Under certain race conditions, there was a scenario where the scheduler would
get into a deadlock while trying to update free space information while a model
was trying to unload.
this is in part to "pay" for #10452, which doubled the default context length. The combination isn't fully neutral though, because even though the old 4x2k limit and the new 2x4k limit are memory equivalent, the 1x fallback is larger with 4k
the first call to http.ResponseWriter.Write implicitly calls WriteHeader
with http.StatusOK if it hasn't already been called. once WriteHeader
has been called, subsequent calls has no effect. Write is called when
JSON encoding progressUpdateJSON{}. calls to
http.ResponseWriter.WriteHeader after the first encode is useless and
produces a warning:
http: superfluous response.WriteHeader call from github.com/ollama/ollama/server/internal/registry.(*statusCodeRecorder).WriteHeader (server.go:77)
* increase default context length to 4096
We lower the default numParallel from 4 to 2 and use these "savings" to
double the default context length from 2048 to 4096.
We're memory neutral in cases when we previously would've used
numParallel == 4, but we add the following mitigation to handle some
cases where we would have previously fallen back to 1x2048 due to low
VRAM: we decide between 2048 and 4096 using a runtime check, choosing
2048 if we're on a one GPU system with total VRAM of <= 4 GB. We
purposefully don't check the available VRAM because we don't want the
context window size to change unexpectedly based on the available VRAM.
We plan on making the default even larger, but this is a relatively
low-risk change we can make to quickly double it.
* fix tests
add an explicit context length so they don't get truncated. The code
that converts -1 from being a signal for doing a runtime check isn't
running as part of these tests.
* tweak small gpu message
* clarify context length default
also make it actually show up in `ollama serve --help`
Previously, the pull handler would send an error message in the Status
field, this prevented the client from using the message as a signal to
stop. In the case of the "run" command, it would follow the pull with a
"show" which would print a nearly identical "not found" message for
unresolved models.
Fixes#10307
This removes the extra flushProgress() at the end of handlePull. It is
unnecessary because final progress updates are flushed in all cases of
the main select loop.
The completed and received counters must work in tandem and the code
should better reflect that. Previously, the act of updating them was 2-3
lines of code duplicated in multiple places. This consolidates them into
a single update closure for easy reading and maintenance.
This also simplifies error handling in places where we can use a return
parameter and defer to handle the error case for updates.
Also, remove the old Layer field from the trackingReader struct.
This commit adds retry/backoff to the registry client for pull requests.
Also, revert progress indication to match original client's until we can
"get it right."
Also, make WithTrace wrap existing traces instead of clobbering them.
This allows clients to compose traces.
No functional change. Many different done reasons can be set at the runner
level, so rather than obsuring them we should return them to the server
process and let it choose what to do with the done reason. This separates
the API concerns from the runner.
With support for multimodal models becoming more varied and common it is important for clients to be able to easily see what capabilities a model has. Retuning these from the show endpoint will allow clients to easily see what a model can do.
This change adds tracking of download chunks during the pull process so
that subsequent pulls can skip downloading already completed chunks.
This works across restarts of ollama.
Currently, download state will be lost if a prune is triggered during a
pull (e.g. restart or remove). This issue should be addressed in a
follow-up PR.
Gemma3 uses sliding windows for its context on 5/6 layers, significantly
reducing memory usage but leading to uneven usage across layers,
which makes allocation to the correct GPU difficult. We currently
estimate very conservatively by assuming all layers are consistent
at the max size.
Llama3.2-vision is also inconsistent between self attention and cross
attention layers - at moment, we calculate the correct total size
and then average this across layers. In some cases, this may lead
to crashes if a large layer is placed on a GPU sized by the average.
This allows memory estimation to calculate per-layer KV cache size
and take this account when placing layers onto GPUs. We already do
this for weights that vary per-tensor, so this is a logical extension.
Fixes#9730Fixes#9890
Close chunked writers as soon as downloads complete, rather than
deferring closure until Pull exits. This prevents exhausting file
descriptors when pulling many layers.
Instead of unbounded defers, use a WaitGroup and background goroutine
to close each chunked writer as soon as its downloads finish.
Also rename 'total' to 'received' for clarity.
If the chunksums response is missing a chunk, the client should fail
the download. This changes the client to check that all bytes are
accounted for in the chunksums response.
It is possible there are overlaps or gaps in the chunksums response and
so the size is not the only thing left to check, but this provides
enough coverage for now. We may want to check that chunks are contiguous
later.
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
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
This is useful for a few things:
- Work around bugs, such as having 2 images in one batch
- Keep the image in a single batch for fully connected attention
- Improve performance by not evaluating embeddings multiple times
This commit replaces the old pull implementation in the server package
with the new, faster, more robust pull implementation in the registry
package.
The new endpoint, and now the remove endpoint too, are behind the
feature gate "client2" enabled only by setting the OLLAMA_EXPERIMENT
environment variable include "client2".
Currently, the progress indication is wired to perform the same as the
previous implementation to avoid making changes to the CLI, and because
the status reports happen at the start of the download, and the end of
the write to disk, the progress indication is not as smooth as it could
be. This is a known issue and will be addressed in a future change.
This implementation may be ~0.5-1.0% slower in rare cases, depending on
network and disk speed, but is generally MUCH faster and more robust
than the its predecessor in all other cases.
* Include unified vision layers in memory prediction
For newer vision models with a single gguf, include
the projection estimates.
* Adjust CLI to handle both styles of vision model metadata
* Wire up new tokenizers for new engine
If we're loading the new engine, utilize the new model
text processor instead of calling into cgo wrappers for
llama.cpp. This also cleans up some tech debt from the
older tokenization flow for the C++ server which was
no longer used.
This also adjusts the grammar handling logic to pass
through to the new engine instead of utilizing the cgo
schema to grammar call.
* Lay foundation for auto selection of new engine
