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
Upload README.md with huggingface_hub
Browse files
README.md
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@@ -35,6 +35,9 @@ output in …"). Prompts must follow the VoxSumDroid templates the model was tra
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## Training
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Distilled from Qwen3.5-9B over 3,891 meetings (QMSum, MeetingBank, DialogSum,
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VCSum→OpenCC s2twp; 60k filtered prompt/completion pairs), full-parameter SFT,
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seq 32768 packed, 2 epochs. Details + eval: training repo `eval/REPORT.md`.
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| metric | base | fine-tune |
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| cross-lingual output-language compliance | 0.38 | **0.98** |
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| action items coverage (1-5) | 1.90 | **3.
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| open questions faith / cover | 3.96 / 2.90 | **4.
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\* base scores "faithful" largely by *not translating* (wrong language 62% of the time).
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## Files
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- `voxsum-qwen3-0.6b_q8_ekv32768.litertlm` — 664MB, kv 32768
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sha256 `
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RAM note: full 32k KV ≈ 3-3.7GB fp16 on-device — cap `nCtx` lower on small devices.
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## Training
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v2 (2026-07-30) adds the single-call structured NOTES task (TITLE/SUMMARY/DECISIONS/
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ACTIONS/OPEN/TOPICS — see docs/OUTPUT-FORMAT.md).
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Distilled from Qwen3.5-9B over 3,891 meetings (QMSum, MeetingBank, DialogSum,
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VCSum→OpenCC s2twp; 60k filtered prompt/completion pairs), full-parameter SFT,
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seq 32768 packed, 2 epochs. Details + eval: training repo `eval/REPORT.md`.
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| metric | base | fine-tune |
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|---|---|---|
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| cross-lingual output-language compliance | 0.38 | **0.98** |
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| action items coverage (1-5) | 1.90 | **3.94** |
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| open questions faith / cover | 3.96 / 2.90 | **4.42 / 4.10** |
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| structured NOTES format compliance (native / cross) | n/a | **1.00 / 0.94** |
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| known limit: en→zh-TW hour-long faithfulness | 3.73* | ~2.5 |
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\* base scores "faithful" largely by *not translating* (wrong language 62% of the time).
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## Files
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- `voxsum-qwen3-0.6b_q8_ekv32768.litertlm` — 664MB, kv 32768 (v2)
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sha256 `660436ac918f61f7fddffc419abf561b396585d115382efb8247814ac9382b9a`
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- `checkpoint/` — HF-format safetensors of the same fine-tune (for reuse / reconversion)
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RAM note: full 32k KV ≈ 3-3.7GB fp16 on-device — cap `nCtx` lower on small devices.
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