magnitudedev/magnitude @magnitudedev/cli@0.2.11

on GitHub

0.2.11

Patch Changes

• 2e321c6 Thanks @thrgreenwald, @anerli! - Run custom models: any GGUF of a model family Magnitude supports, not just catalog models, including other quantizations, finetunes, merges and uncensored versions.

  • Search Hugging Face and add models from the desktop app, the web app or the CLI (magnitude catalog search, inspect and pull). Every version shows its size and whether it fits your computer, can't run (with the reason) or is gated, before you download it.
  • Sign in to Hugging Face once to download gated models; Magnitude says when a model needs sign-in, its terms accepted, or the author's approval.
  • Models already on your computer are found and used in place, without copying: the Hugging Face cache, LM Studio, the llama.cpp cache, Ollama, and folders you add. Found models can be hidden without touching their files, and a catalog model already on your computer is used instead of downloaded again.
  • Each model gets the best drafter known to work with it, when there is one: from its own repository or folder, the matching catalog model, or its built-in MTP heads. You can choose a different drafter or none.
  • Models that can't run are listed with the reason, such as an architecture without kernels yet or an unsupported weight type.

• 2e321c6 Thanks @thrgreenwald! - Discover and the model catalog are now one page.

  • Model cards show one clear status and an estimated download time, and catalog and custom models share the same details view.
  • Loading and optimizing show live progress.
  • Service status and model memory moved into Settings.

• 2e321c6 Thanks @thrgreenwald, @anerli! - Faster generation.

  • On x86 CPUs with AVX2 or VNNI, k-quant, Q8 and 4-bit codebook weights run faster.
  • On NVIDIA GPUs, IQ2_XXS weights read faster, and models that stream a token table decode faster: Nemotron 3.5 Bolt on a GB10 went from 36.0 to 41.9 tok/s with drafting off.
  • Drafting decides from the time recent steps actually took, so one slow step no longer turns it off, and it resumes once it pays off again.
  • AMD GPUs on Linux (Vulkan, RADV) prepare a model's programs in a fraction of the time and memory.
  • Chat templates that scan the whole conversation for every message no longer take seconds to render, and short follow-up turns resume from the cached conversation.
  • A catalog model's one-time optimization waits until the GPU is free, and loading a model you want takes priority over it.

• 2e321c6 Thanks @thrgreenwald! - Fix installation and platform issues.

  • On Windows 10, upgrades no longer fail to replace the installed app.
  • The desktop app no longer takes over a Magnitude server running as a Windows service, and says why.
  • Hermes connections on Windows are written where Hermes reads them.
  • Fix installer readiness checks, macOS onboarding, and verification error messages.
  • CLI commands piped into a reader that stops early end quietly instead of crashing.

• 2e321c6 Thanks @thrgreenwald, @anerli! - More accurate memory planning and more reliable loading.

  • Fit checks count each backend's own memory costs, so a model shown as fitting loads, and a long conversation's cached prompt no longer grows past what was planned.
  • Models too large for a small GPU show as not fitting instead of failing to load.
  • Magnitude serves on the discrete GPU when your computer also has an integrated one, and shows which device it's using.
  • Requested downloads, drafters and projectors included, resume after a restart, and a cancelled download stays cancelled.
  • A large image no longer holds a model running on the CPU for minutes.

• 2e321c6 Thanks @thrgreenwald, @anerli! - Fix incorrect output and load failures for several models.

  • Reasoning works with LFM2.5 Instruct and Thinking models, Gemma 4 thinks unless thinking is turned off, and a response that ends inside its reasoning keeps that reasoning instead of failing.
  • Default sampling is now temperature 1.0, top_p 0.95 and top_k 20 on the OpenAI and Anthropic APIs alike. The OpenAI APIs used 0.8 and 0.95, and the Anthropic API 1.0 and 1.0, with no top_k on either. The Anthropic API now accepts top_k.
  • Finetunes run with their base model's vision projector, and Qwen 3.5 models without built-in prediction heads run.
  • Fix Vulkan sampling probabilities, Vulkan weight formats failing to compile, and a Metal weight-packing bug.

• ac67c47 Thanks @thrgreenwald! - Add a native Chat tab with local model streaming, performance metrics, reasoning controls, generation settings, and SQLite conversation history.

  Render code blocks with language labels, syntax highlighting, and individual copy actions, and improve Markdown headings and nested spacing.

• 2e321c6 Thanks @anerli! - Add Nemotron 3.5 Bolt 74B-A9B, Nemotron 3.5 Super VL 120B-A12B with vision, and Qwen 3.8 Flash Next NVFP4 to the catalog.

  • Nemotron 3.5 models draft with LiLiCorr, a new drafting method ranked above DFlash2. On a GB10, Bolt decodes at 55.7 tok/s against llama.cpp's 51.6.
  • NVFP4 models that carry NVIDIA's activation scales, such as Qwen 3.8 Flash Next NVFP4 and Super VL's NVFP4 variant, run on FP4 tensor cores on GPUs that have them. On a GB10, Super VL NVFP4 prefills at 826 tok/s against llama.cpp's 552.
  • Nemotron 3 Super and Ultra can now run as custom models.

• 2e321c6 Thanks @anerli, @thrgreenwald! - Speed estimates in Discover are now based on measurements of your own hardware instead of a generic memory-bandwidth figure, so they track real performance more closely. The measurement runs once, in the background, and never while a model is in use.