The Llama.cpp backend can emit the phrase “failed to allocate” when it runs out of memory.
Adding this check ensures such messages are correctly classified as out‑of‑memory errors,
providing more accurate error handling CPU backends.
- Removed the unused `getKVCachePerToken` helper and replaced it with a unified `estimateKVCache` that returns both total size and per‑token size.
- Fixed the KV cache size calculation to account for all layers, correcting previous under‑estimation.
- Added proper clamping of user‑requested context lengths to the model’s maximum.
- Refactored VRAM budgeting: introduced explicit reserves, fixed engine overhead, and separate multipliers for VRAM and system RAM based on memory mode.
- Implemented a more robust planning flow with clear GPU, Hybrid, and CPU pathways, including fallback configurations when resources are insufficient.
- Updated default context length handling and safety buffers to prevent OOM situations.
- Adjusted usable memory percentage to 90 % and refined logging for easier debugging.
* fix: correct context shift flag handling in LlamaCPP extension
The previous implementation added the `--no-context-shift` flag when `cfg.ctx_shift` was disabled, which conflicted with the llama.cpp CLI where the presence of `--context-shift` enables the feature.
The logic is updated to push `--context-shift` only when `cfg.ctx_shift` is true, ensuring the extension passes the correct argument and behaves as expected.
* feat: detect model out of context during generation
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Co-authored-by: Dinh Long Nguyen <dinhlongviolin1@gmail.com>
Implemented `isAbsolutePath` helper to correctly identify POSIX, Windows drive‑letter, and UNC absolute paths. Updated `planModelLoad` to automatically resolve relative model and mmproj paths against the Jan data folder, enhancing usability for users supplying non‑absolute paths. Also refined minor formatting for readability.
Expose the new `mmproj_offload` option in the model settings UI and include it in the `ModelPlan` type. The component now collects the offload flag (`result.offloadMmproj`) and queues it with other setting updates to ensure a single atomic change, preventing race conditions when toggling this feature. This enables users to control MMProj offloading directly from the app.
The flag `noOffloadMmproj` was misleading – it actually indicates when the mmproj file **is** offloaded to VRAM. Renaming it to `offloadMmproj` clarifies its purpose and aligns the naming with the surrounding code.
Additionally, the `planModelLoad` signature has been reordered to place `mmprojPath` before `requestedCtx`, improving readability and making the optional parameters more intuitive. All related logic, calculations, and log messages have been updated to use the new flag name.
The original calculation used only the `block_count` from the model metadata, which excludes the final LM head and the embedding layer. This caused an underestimation of the total number of layers and consequently an incorrect `layerSize` value. Adding `+2` accounts for these two missing layers, ensuring accurate model size metrics.
