extract_content_tags now generates with grammar=None and parses the
response via a manual brace-counting JSON envelope extractor that
handles Qwen <think>...</think> prefixes and trailing stop tokens.
Five new unit tests. Bumps llama-cpp-2 to 0.1.146. Explicit
features=[] on tauri dependency (no-op).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Replaces the three older Qwen3 variants with a four-tier ladder spanning
a wider hardware range:
- Qwen3_5_2B_Q4 (Minimal, 8 GB RAM, ~1.3 GB download)
- Qwen3_5_4B_Q4 (Standard, 16 GB RAM / 6 GB VRAM, ~2.7 GB) — DEFAULT
- Qwen3_5_9B_Q4 (High, 32 GB RAM / 12 GB VRAM, ~5.7 GB)
- Qwen3_6_27B_Q4 (Maximum, 64 GB RAM / 24 GB VRAM, ~17 GB)
All four GGUFs sourced from unsloth's HF org with pinned commit SHAs.
Sizes and SHA256 hashes verified against the live X-Linked-Etag /
X-Linked-Size headers on the LFS CDN. Q4_K_M quantisation throughout
(common sweet-spot for cleanup + task extraction).
recommend_tier rewritten to span four bands; default_tier moves from
the old 4B-Instruct-2507 to Qwen3.5 4B. The 27B Maximum tier honestly
needs 64 GB RAM to run without partial offload — surfaced in the
description string so the Settings UI can warn realistically.
In-tree smoke tests (smoke.rs, content_tags_smoke.rs) updated to
reference the new smallest tier so a developer's MAGNOTIA_LLM_TEST_MODEL
points at the cheapest GGUF to download. Crate description in
crates/llm/Cargo.toml refreshed to mention the new family.
NOTE (out of scope; not fixed): the size_bytes / sha256 / hf_url
methods could collapse into a single LlmModelMetadata table to remove
four parallel match arms. Layer 2 cleanup, separate session.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Replace all instances of the legacy product names "Kon" and "Corbie" with
"Magnotia" across user-facing copy, code identifiers, package names, bundle
ids, file paths, and documentation. Preserves the unrelated "konsole" (KDE
terminal) reference and the parent CORBEL company name.
- Renames 10 Rust crates (kon-* → magnotia-*) and the tauri binary
- Updates package.json, tauri.conf.json (productName + identifier)
- Renames CSS classes (kon-rh-* → magnotia-rh-*) and animations
- Renames brand and roadmap docs
- Regenerates Cargo.lock and package-lock.json
Verified: svelte-check passes; pure-rust crates compile under new names.
Both `kon-transcription` and `kon-llm` previously hardcoded their native
acceleration features in Cargo.toml — `whisper-rs` with `vulkan`,
`llama-cpp-2` with `openmp` + `vulkan`. That worked everywhere desktop
ships (Linux/macOS/Windows all have Vulkan via MoltenVK on Mac), but it
made an Android build structurally impossible: NDK builds against drivers
that vary wildly across SoCs (Adreno OK, Mali patchy, PowerVR worse), and
some older devices have no Vulkan at all.
Roadmap step 0 from the Android plan: make the GPU acceleration
opt-in so a CPU-only target compiles. Reuses the existing pattern that
README's "future Windows non-AVX2 build" comment hinted at.
- kon-transcription: new `whisper-vulkan` feature gates `whisper-rs/vulkan`
via the optional-syntax `whisper-rs?/vulkan`. Default features stay as
`["whisper", "whisper-vulkan"]` so desktop is unchanged.
- kon-llm: new `gpu-vulkan` and `openmp` features each gate the matching
`llama-cpp-2` feature. Default stays `["gpu-vulkan", "openmp"]`. They are
independent so an Android Vulkan build can opt into vulkan without
openmp (NDK OpenMP linking has known cross-version fragility).
CPU-only build invocations:
cargo build -p kon-transcription --no-default-features --features whisper
cargo build -p kon-llm --no-default-features
Verified: all 91 tests in the buildable-in-sandbox crates still pass.
The two crates whose Cargo.toml changed (kon-transcription, kon-llm)
can't be compiled in this sandbox (ort-sys CDN + cmake-built llama.cpp);
CI's Linux/macOS/Windows builders will exercise the default-feature path
exactly as before.
https://claude.ai/code/session_0189xUb6ie6t9qHkzatGZ9Rb
kon-llm now owns a real LlamaBackend + LlamaModel, with three Qwen3 tiers
(1.7B Q4, 4B-Instruct-2507 Q4, 14B Q5) selectable per hardware. Downloads
are resumable with SHA-256 verification and stored under ~/.kon/models/llm.
Engine exposes three high-level surfaces — all greedy/temp-0, GBNF-constrained
where output shape matters:
- cleanup_text (prompt-injection-hardened system prompt; profile terms
appended as "preserve these spellings" suffix)
- decompose_task (3–7 micro-steps, constrained JSON array)
- extract_tasks (optional-array; empty when no explicit commitments)
post_process_segments now takes an Option<&LlmEngine> and, when loaded and
format_mode != Raw, joins segments → cleanup → replaces segments with the
cleaned text (first segment span). Rule-based path still runs first; LLM
errors log and keep rule-based output.
Tauri commands: recommend_llm_tier, check_llm_model, download_llm_model,
load_llm_model, unload_llm_model, delete_llm_model, get_llm_status,
cleanup_transcript_text_cmd, extract_tasks_from_transcript_cmd,
decompose_and_store (LLM-backed subtasks).
Settings: AI tier toggle (off / cleanup / tasks), model picker with
downloaded/loaded status, download progress events via
kon:llm-download-progress.
Dictation: ensureLlmModelLoaded on mount, cleanupTranscriptIfEnabled after
stop when tier != off and format_mode != Raw, LLM task extraction when
tier=tasks (regex fallback on failure).
Interim: both llama-cpp-sys-2 and whisper-rs-sys statically link their own
ggml, so src-tauri/build.rs emits -Wl,--allow-multiple-definition on Linux.
Replace with a system-ggml shared-lib setup as a follow-up.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
