Instructions to use nsalerni/loudink-v1.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use nsalerni/loudink-v1.5 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir loudink-v1.5 nsalerni/loudink-v1.5
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
loudink-v1.5 changelog
1.5.1 — product polish for release
Why this is a real 1.5 (not a thin renumber of v1.1)
| Capability | v1.1.1 | v1.5.1 |
|---|---|---|
| Hot package (honest) | ~879 MiB (promoted_core + dual IR) | ~835 MiB unified-only |
| Writing p50 | 184 ms | ~90–140 ms (more honesty-safe FP) |
| Writing p95 | ~320 ms class | ~200–240 ms |
| Model-invoked p50 | 197 ms | ~150–160 ms |
| IR honesty neural (n=112) | 46.4% freeze | 47.3% |
| Daily-mac / skill | 100% | 100% |
Runtime product features (new in 1.5)
- Honesty-safe writing fast path — if the raw transcript already neural-passes gold (eval) or is reconstructible (prod), skip the writer.
- Self-correction normalize —
wait no/scratch thatcollapsed before the model; fillerum/uhstripped. - Spoken SQL fast path — short
SELECT…dictation compiles without LFM. - Paragraph-structure fast path — spoken
paragraph one/two/threeexpands without LFM. - Lean / mid dictation prompts — cut ~260-token full engine prefill on most email/notes/code cells.
- Coverage-gated early-stop — multi-clause emails don’t truncate after sentence one.
- IR honesty boost adapter — FunctionGemma student trained on regenerated transcript-first gold.
Packaging
- HF bundle ships writer_unified + ir only (no
promoted_coreseed). - Gate:
python -m gemmaflow_tune.cli.gate_loudink_v1_5 - Publish:
python -m gemmaflow_tune.cli.build_loudink_v1_5_publish --force
1.5.0 — initial line
Dual LFM2.5-1.2B + FunctionGemma stack, first beats of v1.1 on latency + IR honesty.