voxsum-qwen35-0.8b — on-device meeting summarizer (zh-TW / en)
Qwen3.5-0.8B (text) fine-tuned for long meeting-transcript summarization and insight extraction in Traditional Chinese and English, for VoxSumDroid. Trained with quantization-aware training so it survives int4 weights on device.
Why this base model
Nine candidates were benchmarked on the three constraints that actually bind on a phone (prefill speed, Chinese token efficiency, RSS). Qwen3.5-0.8B won all three:
| Qwen3.5-0.8B | best rival | |
|---|---|---|
| prefill @16k, 8 CPU threads | 491 tok/s | 457 (granite-4.0-h-1b) |
| zh-TW tokenizer | 1.67 chars/token | 0.98 (Falcon-H1), 0.69 (Granite) |
| 3-h zh meeting, prefill (ARM-derated) | ~4 min | 9–12 min |
| KV cache @32k, q4 | 197 MB | — |
Its hybrid architecture (only 6 of 24 layers hold a KV cache; the other 18 are Gated-DeltaNet linear attention with constant state) is what keeps memory and prefill low at long context. Rejected: Falcon-H1-1.5B (1.84 GB @64k, slowest prefill), Granite-4.0-h-1b (0.69 chars/token on zh), Granite-3.1-1b-a400m MoE (−54% prefill decay by 16k), T5Gemma-2 enc-dec (no runtime support, 32k ceiling, degenerate at 270M), Gemma-3-1B (32k native), Qwen3.5-2B (1.68 GB, over budget).
Memory (measured components + measured runtime overhead)
| context | weights q4 | KV q4 | total RSS |
|---|---|---|---|
| 32k (≈3-h zh-TW meeting) | ~600 MB | 197 MB | ~1.2 GB |
| 64k | ~600 MB | 393 MB | ~1.4 GB |
Input format
VoxSumDroid transcript format v1 — one utterance per line:
[0:00] S1: 我們今天要決定第三季的行銷預算。
[1:23:45] Alice: ...
Output format
Single-call structured notes (docs/OUTPUT-FORMAT.md v2) — ASCII section keys, content in
the transcript's language:
TITLE: 第三季行銷預算決定
SUMMARY:
- 決定社群廣告佔百分之四十為核心預算。
DECISIONS:
- 社群廣告預算百分之四十。
ACTIONS:
- 小陳：下週五前提供細部計畫。
OPEN:
- 財務預算追補進度。
TOPICS:
- 第三季行銷預算分配。
The app's per-task prompts (summarize / title / action items / topics / open questions / risks, bullet-executive-narrative styles, en+zh with cross-lingual clause) are also supported — see the training repo.
Training
- Distillation from Qwen3.5-9B over 3,891 meetings (QMSum, MeetingBank, DialogSum, VCSum→OpenCC s2twp).
- Faithfulness filtering — every one of 39,731 targets judged for factual support and polarity inversion; 6,414 dropped (16.1% scored faith≤3; 300 were outright inversions). This was the single largest quality lever.
- QAT SFT — int4-block32 weight fake-quant (output head int8), 32k packed sequences.
- Rejection-sampling fine-tune on the model's own long-document outputs: 389 samples the judge scored faith 4.73 with zero inversions, upweighted 6×. Their rejected counterparts averaged faith 1.85 and contained 121 inversions.
Known limitations — please surface these honestly in UI
- Faithfulness degrades past ~12k tokens. Measured on the shipped checkpoint: faith 4.05 / 1.8% inversion at 4–12k tokens, versus 3.36 / 15.3% inversion above 12k, where 46% of outputs score faith≤2. Chunking at ~10–12k tokens with a hierarchical reduce keeps the model in its reliable regime; the map/reduce prompts are in the training mix for exactly this.
- Owner attribution can slip on long transcripts (assigning an action to the wrong speaker). Action items are a draft to correct, never an audit trail.
- Cross-lingual (en→zh-TW) faithfulness is the weakest axis; language routing is reliable (0.98) but details drift.
Sampler: temp 0.7, top_p 0.8, top_k 20, presence_penalty 1.0. Near-greedy (temp 0–0.3) measurably improves faithfulness for extraction tasks.