A LoRA fine-tune of Qwen/Qwen3-Embedding-0.6B, merged back into the base at α = 0.25 (WiSE-FT style interpolation), for one task: given a sentence a writer is drafting, retrieve the note card (saved highlight) it draws on.

model.safetensors is 1.1 GB, fp16, 1024-dim, Qwen3Model. Standard SentenceTransformers layout — SentenceTransformer("<repo-id>") just works.

n = 34 queries / 859 cards, one neuroscience paper labeled by its own author. Paired MDE ≈ 0.030, so the top four are a statistical tie — but a tie is the honest reading, not a win. A separate agent-labeled ruler scores this model resolvably above base (+0.026), but a cross-ruler agreement test showed that ruler disagrees with the human anchor on exactly this kind of close call, so we do not count it.

Do not use the custom Instruct: ... query prefix. A prompt ablation found it is net-negative for the base model; frozen base with no prompt is the best number in the table above. If you use Octen, keep its native prompt.

Retrieval in the product fires when the writer pauses mid-sentence, not on a finished sentence. Evaluated in that regime (clause truncated to 40/60% with the preceding ~30 words prepended), the ranking flips and this model leads:

This is the only claim we make for this model, and it is a weak one: on a second, cross-domain ruler (a physics/CS paper, 31 queries / 1101 cards) the advantage did not replicate — a025 and base both scored 0.1795 at 40%. Treat the mid-sentence edge as unconfirmed outside the domain it was measured in.

MarginMSE on citation-grounded pairs from unarXive, teacher = Qwen3-Reranker-4B log-odds margins, LoRA r16/α32, then merged at α=0.25. The interpolation is what made it survive out-of-domain; the pure-distill checkpoint (v2a, 0.303) is worse than the base it started from.

Scaling this recipe fails. A 60k field-balanced set of s2orc citation pairs (12 fields, hard negatives, same teacher, same loss) produced 0.2475 from base and 0.2410 continuing from this model — a statistically significant regression (per-query AUC 0.866 vs base 0.934, gap 0.068 > MDE 0.041). The citation-proxy signal is misaligned with human writing-utility judgments; more of it does not help.

For a fresh integration, prefer Octen/Octen-Embedding-0.6B frozen: tied-best or better on both rulers, Apache-2.0, 600 MB, same backbone and MLX path, and no LoRA-merge / prompt-calibration apparatus to maintain. Reach for this model only if you are specifically working the mid-sentence regime and want to reproduce the table above.

No MLX build exists. a025-mlx in the source repo is a broken stub (the safetensors entry is an 84-byte symlink). A Swift/MLX consumer needs a real conversion first; mlx-community/Qwen3-Embedding-0.6B-4bit-DWQ is the only ready-made MLX option today, and 4-bit was measured to cost nothing (device goldset: 4-bit base 0.733 vs bf16 a025 0.730).