feat(llm): bump model registry to Qwen3.5 + Qwen3.6 family (4 tiers)

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>

crates/llm/tests/content_tags_smoke.rs

@@ -24,7 +24,7 @@ fn extract_content_tags_returns_valid_pair() {
 
     let engine = LlmEngine::new();
     engine
-        .load_model(LlmModelId::Qwen3_1_7B_Q4, &model_path, true)
+        .load_model(LlmModelId::Qwen3_5_2B_Q4, &model_path, true)
         .expect("load model");
 
     let transcript = "Tomorrow I need to run through the grant application one more time \

crates/llm/tests/smoke.rs

@@ -26,7 +26,7 @@ fn llama_cpp_2_smoke_generates_and_wraps() {
 
     let engine = LlmEngine::new();
     engine
-        .load_model(LlmModelId::Qwen3_1_7B_Q4, &model_path, true)
+        .load_model(LlmModelId::Qwen3_5_2B_Q4, &model_path, true)
         .expect("load model");
 
     let completion = engine