本地大模型工具发布 v0.34.4 版本更新。新版本支持在思考模型上单次生成结构化输出，提升了运行速度与可靠性；同时加速了 Apple Silicon 设备上 Qwen 3.8 的提示词处理，并改进了 Gemma 4 对高分辨率图像的处理机制。此外，更新修复了大型本地库偶发“模型未找到”及 macOS 界面卡顿等问题，并同步升级了 llama.cpp、MLX 和 XGrammar 依赖。
We pick up schema fixes for typed dictionary values and short arrays.
mlx: speed up Qwen 3.8 prompt processing Use MLX's gated-delta kernel for long scans and fold dense MLP global scales into SwiGLU. address comments
What's Changed GET /api/show now advertises each model's thinking controls and default: Available in the CLI with: ollama show gemma4 thinking levels false, true default true Available in the API with: curl http://localhost:11434/api/show -d ' {"model": "glm-5.3-flash:cloud"} ' { "thinking" : { "values" : [ " low " , " high " , " max " ], "default" : " max " } } Also available on ollama.com directly for cloud models. Nemotron H vision models are now supported on Apple Silicon with MLX Ollama's macOS app will now no longer reopen windows you've closed when activating it Fix for model pulls from HuggingFace Full Changelog : v0.34.2...v0.34.3
server: allow registry cross-host redirects among allowlisted hosts (…
api: expose model thinking levels and defaults ( #18473 )
What's Changed Added first-run setup when running ollama , with options to sign in or continue locally. Setup completion is shared with the desktop app on macOS and Windows. Added ollama://apps to open the desktop app’s Apps page directly on macOS and Windows. Fixed excessive memory growth during long generations with MLX speculative decoding. Updated llama.cpp. Full Changelog : v0.34.1...v0.34.2
cli: add first-run onboarding shared with the desktop app ( #18495 )
The decode loop releases MLX's pool of freed buffers every 256 generated tokens, which is also how often the KV cache grows and drops its previous, smaller buffers. The check fires only when the token count lands exactly on a multiple of 256. Speculative decoding emits several tokens per round, so most rounds step over the boundary and the pool is never released. Each growth at a long context leaves several GB of buffers that no later allocation can reuse, so the runner's footprint keeps climbing over a long generation until the system runs out of memory. We now release the pool whenever a round crosses a multiple of 256 tokens, which is what
model is one package with three jobs: the contract between the runner and the architectures, the opened checkpoint, and building nn layers from checkpoint tensors. Its files did not say which was which. base.go carried the folded package's name over the interfaces and the registry, root.go held the safetensors header scan next to Root, and quant.go mixed the nvfp4 global-scale helpers with quant parameter resolution. base.go becomes model.go, named for what it holds. root.go keeps Root and Open; TensorQuantInfo and the header scan join quant.go, so everything the checkpoint says about quantization is read and resolved in one file. The global-