synthiumjp/sourcedrecall-notes-en

sourcedrecall notes models (English, int4 ONNX)

Two small models for one job: given one conversation, write the lasting facts the named person states about themselves, one short sentence per line, or NONE. sourcedrecall runs them inside its own process on onnxruntime-genai, on the CPU, with no server.

file	base	download	per conversation (4 CPU threads)	peak memory
sourcedrecall-notes-en-0.6b.tar.gz (small)	Qwen3-0.6B	0.51 GB	2-3 s	1.9 GB
sourcedrecall-notes-en-1.7b.tar.gz (standard)	Qwen3-1.7B	1.3 GB	about 4.5 s	3.3 GB

They are optional. sourcedrecall stores and finds the user's own words without them; with one, short notes are added, each kept only if its words are in what the user wrote, and shown labelled as written by a model.

pip install 'sourcedrecall[notes]'
sourcedrecall-setup --notes            # small
sourcedrecall-setup --notes standard   # standard

How they were made

• Full fine-tune of the base model with mlx-lm, then int4 ONNX with the onnxruntime-genai builder (k_quant_mixed).
• Teacher: Qwen3-14B wrote notes for 3,000 LongMemEval-S conversations (MIT) and 1,500 SODA dialogues (CC BY 4.0), each SODA dialogue from both speakers' sides; a note was kept only if most of its words are in the person's own lines.
• Every training conversation got a random owner name and date format, and its pronouns were made explicit (I/my -> the speaker, you/your -> the listener), as sourcedrecall does before the model reads a conversation.
• Scripts: tools/distil in the sourcedrecall repository.

Measured (local Qwen3-14B reader and judge for every system)

LoCoMo, conversations 2-9 (1,307 questions):

	overall	multi-hop	temporal	single-hop
sourcedrecall, no notes	64.7%	53.6%	56.2%	74.8%
with the small model's notes	68.2%	55.2%	58.9%	79.0%
with the standard model's notes	70.3%	59.0%	57.4%	81.6%
with Qwen3-14B's notes (a server)	73.1%	62.3%	57.4%	85.4%
Mem0 2.2.1	64.6%	61.9%	37.2%	77.9%

Notes on the LoCoMo development conversations, judged against their conversation by Qwen3-14B: said by the person 80% (small), 87% (standard), 93% (Qwen3-14B); gave the person the other speaker's facts 13%, 9%, 3%. Held-out false-memory sets: v3 0 of 30 for both; v4, an old state given as current, 3 of 44 (small) and 4 of 44 (standard), against 3 with Qwen3-14B's notes and 2 without notes.

These answer scores come from local models and are not comparable with published LoCoMo results, which use GPT-4-class models.

Limits

English only. They sometimes give the user something the other person said ("has kids" from the other person's children). Neither closes sourcedrecall's multi-hop gap to Mem0.

Attribution

SODA (Kim et al., 2023), CC BY 4.0. LongMemEval (Wu et al., 2024), MIT. Qwen3 (Qwen team), Apache-2.0.