Instructions to use Luigi/voxsum-qwen3-0.6b-litertlm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LiteRT-LM
How to use Luigi/voxsum-qwen3-0.6b-litertlm with LiteRT-LM:
# LiteRT-LM runs on various platforms (Android, iOS, Windows, Linux, macOS, IoT, Web/WASM) # and supports many APIs (C++, Python, Kotlin, Swift, JavaScript, Flutter). # For platform-specific integration guides, please refer to the official developer website: # https://ai.google.dev/edge/litert-lm # To try LiteRT-LM, the easiest way is to use our CLI tool. # 1. Install the LiteRT-LM CLI tool: pip install -U litert-lm # 2. Download and run this model locally: # See: https://ai.google.dev/edge/litert-lm/cli litert-lm run \ --from-huggingface-repo=Luigi/voxsum-qwen3-0.6b-litertlm \ --prompt="Write me a poem"
- Notebooks
- Google Colab
- Kaggle
voxsum-qwen3-0.6b — on-device meeting summarizer (zh-TW / en)
Qwen3-0.6B fine-tuned for single-pass meeting summarization and insights over
VoxSumDroid transcripts, packaged as a
LiteRT-LM .litertlm bundle (dynamic int8, 32k context, mask-as-input, Qwen3 ChatML
template + stop tokens embedded).
Input format (transcript format v1)
One utterance per line, start-timestamp + speaker tag:
[0:00] S1: 我們今天要決定第三季的行銷預算。
[0:05] S2: 我建議把百分之四十放在社群廣告,效果最好。
[1:23:45] Alice: ...
Variants supported: S1…Sn tags, real names, or no speaker field (no diarization).
Tasks
Summaries (bullet / executive / narrative), title (≤8 words), action items + decisions, detailed summary, topics, open questions, risks/disagreements — in zh-TW and en, including cross-lingual output via the VoxSumDroid language clause ("Write the ENTIRE output in …"). Prompts must follow the VoxSumDroid templates the model was trained on (see the training repo).
Training
v2 (2026-07-30) adds the single-call structured NOTES task (TITLE/SUMMARY/DECISIONS/ ACTIONS/OPEN/TOPICS — see docs/OUTPUT-FORMAT.md).
Distilled from Qwen3.5-9B over 3,891 meetings (QMSum, MeetingBank, DialogSum,
VCSum→OpenCC s2twp; 60k filtered prompt/completion pairs), full-parameter SFT,
seq 32768 packed, 2 epochs. Details + eval: training repo eval/REPORT.md.
Eval vs base (held-out meetings, teacher-as-judge)
| metric | base | fine-tune |
|---|---|---|
| cross-lingual output-language compliance | 0.38 | 0.98 |
| action items coverage (1-5) | 1.90 | 3.94 |
| open questions faith / cover | 3.96 / 2.90 | 4.42 / 4.10 |
| structured NOTES format compliance (native / cross) | n/a | 1.00 / 0.94 |
| known limit: en→zh-TW hour-long faithfulness | 3.73* | ~2.5 |
* base scores "faithful" largely by not translating (wrong language 62% of the time).
Recommended sampler: temp 0.7, top_p 0.8, top_k 20, presence_penalty 1.0.
Files
voxsum-qwen3-0.6b_q8_ekv32768.litertlm— 664MB, kv 32768 (v2) sha256660436ac918f61f7fddffc419abf561b396585d115382efb8247814ac9382b9acheckpoint/— HF-format safetensors of the same fine-tune (for reuse / reconversion)
RAM note: full 32k KV ≈ 3-3.7GB fp16 on-device — cap nCtx lower on small devices.
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