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.
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.
When the same model is being reloaded rapidly with client connections
being canceled before the model finishes loading, the queued unload
event could cause a leak of runners by deleting a different runner from
the loaded list.
* fix mllama convert
- transform attn_gate and ffn_gate
- swap attention heads for vision models
* fix mllama
the mlp gate which was applied in the wrong place
Fall back to alternative quantization types when a tensor's dimensions aren't divisible by the block size required for the original desired quantization type. If retried quantization types fail, the system ultimately falls back to F16 (half-precision floating point) which has a block size of 1 and can handle any tensor dimension.
* remove support for multiple ggufs in a single file
this was an attempt to make it easier to import multimodal models into
ollama. this was rarely used and error prone so remove it
* fix: create fused model from blob
setting samebatch on the vision start token is problematic because it
will be shared with other inputs that also use images. this will cause
the input to be cached and the runner will not see SameBatch. SameBatch
will also be incorrect since it may be for a different image.
assigning samebatch to the input tokens resolves this by ensure it's
assigned correctly to inputs corresponding to the image.
not setting same batch correctly may cause panics during inference since
images are no longer guaranteed to be in the same batch.
This is a partial revert of 0478d44 "Fixed over vram allcation dure to
small initial layer sizes."
Previously we used the size of the first layer as an extra reserved
amount of space to buffer our memory estimates. The above commit
changed this to use the largest layer. However, this had performance
impacts on more models than the original commit was trying to fix.
There is just a heuristic without an ideal solution so this goes back
to the historic behavior.
Fixes: #10765, #10756, #10752, #10726
Currently, when the backend is created, the tensors are loaded at the
same time, which is a slow operation. This separates them to be two
steps:
- Create backend, including enumerating tensors and memory allocation
- Loading tensor data
This allows more flexibility in managing model loading.
The Llama engine always places vision projectors on the first GPU
if one exists. However, the Ollama engine groups it with the output
layer, which means the projector is only offloaded if all other layers
are offloaded. The memory estimation code always assumes the former
layout - this changes it to use the correct layout based on the engine.
This addresses two impacts of the current behavior:
- In multi-GPU setups, we can crash with OOM errors when we try to
allocate memory on a full GPU while another still has space.
- If the vision projector is large, it may prevent us from offloading
anything when we could have fit some of the text layers.
In some cases, if we fail to assign a piece of the model to a GPU then
we lose track of this data. Although it doesn't change the memory
allocation, it does affect the total size of the model reported by
tools such as ollama ps (and also the percent offloaded).
This makes it look like setting num_gpu isn't reflected in ollama ps,
which isn't true but the offloading percent may appear to not change.
Spreading the model across more GPUs will continue to impact the
reported total size of the model.
This PR adds Tiny Notepad, a lightweight, notepad-like interface to chat with local LLMs via Ollama.
- It’s designed as a simple, distraction-free alternative.
- The app supports basic note-taking, timestamped logs, and model parameter controls.
- Built with Tkinter, it runs entirely offline and available via PyPI.
Aims to provide a lightweight easy to run and install interface for ollama.
* get eos_token_id from generation_config.json
* refactor
* include both ids and strings in trace
* comments
* remove special case for gemma3 special vocab (#10743)
We currently preallocate compute graph memory for the worst case
batch of text tokens. This adds support for doing the same for
images.
Note that image models are more complicated than text models in
how they process their inputs so there may be cases where this
approach isn't completely generic for all models. It covers all
currently supported models though.
For some multimodal models (such as gemma3), we create a single
graph that generates the image embedding and then use this in the
text model. The embedding tensor is completely opaque to the runner.
However, this doesn't work if we need to use the embedding in multiple
batches. This can arise if the embedding is larger than the batch size.
In these cases (as with llama4), we would like to create views that
are more appropriately sized. However, if we do this then the original
source tensor is used in multiple graphs, which isn't allowed. To
avoid that problem, models with this pattern compute the embedding
tensor on first use and recreate the individual views. There is no
longer a single vision and text graph.
This codifies the pattern of separating vision and text graphs. The
logic of computing tensors on demand is moved to the runner, so models
no longer have to worry about this. It also gives the runner visibility
into the multimodal tensors, which is important for memory management.
When we restore a sequence from the cache, we split the prompt into
the already used tokens (stored in the cache) and new tokens that
need to be processed. Currently, the references to the used tokens
are coming from the stored previous sequence.
However, even though we know that the used tokens are semantically
equivalent to the prefix of the prompt, tokens can contain pointers
which are no longer valid. As a result, it is better to get the
used tokens from the prompt, which has currently valid pointers.
This doesn't currently have any impact because it isn't possible
to reuse the pointers (which are tensors) anyways. However, it
becomes an issue once we can.
Patrick Devine
Jesse Gross
Kyle Steere
Parth Sareen
RAPID ARCHITECT
Min Yoo
Daniel Hiltgen
frob
Michael Yang
Bruce MacDonald
DarkCaster
Ronald Wilson
tej