MLX
mlx-lm
flowcast
loudink
loudink-v1.1
voice-agent
apple-silicon
compact-ir
dictation
computer-use
skills
record-and-replay
Instructions to use nsalerni/loudink-v1.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use nsalerni/loudink-v1.1 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir loudink-v1.1 nsalerni/loudink-v1.1
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
| # loudink-v1.1 status — **PR-A→E complete** | |
| Last updated: 2026-07-08 | |
| ## Hugging Face | |
| | Line | Repo | Role | | |
| |------|------|------| | |
| | **Stable GA** | [nsalerni/loudink-v1](https://huggingface.co/nsalerni/loudink-v1) | **0.4.0** freeze | | |
| | **Canary** | [nsalerni/loudink-v1.1](https://huggingface.co/nsalerni/loudink-v1.1) | **1.1.0** skills + expanded daily-mac | | |
| Rebuild canary: | |
| ```bash | |
| python -m gemmaflow_tune.cli.build_loudink_v1_1_publish --force | |
| python -m gemmaflow_tune.cli.publish_sota --model loudink-v1.1 \ | |
| --commit-message "loudink-v1.1 1.1.0: skills PR-A–E canary" | |
| ``` | |
| ## PR board | |
| | PR | Deliverable | Status | | |
| |----|-------------|--------| | |
| | **PR-A** | Skill store + recorder schema + compiler + null-safe IR | **done** | | |
| | **PR-B** | Paraphrase invoke + skill bench + IR labels + runner wiring | **done** (19/19) | | |
| | **PR-C** | Slot fill (`for` / `about` / named) | **done** | | |
| | **PR-D** | Daily-mac expand (Focus/DND, Reminders, reply, tabs, Notion/Linear, writing) | **done** (69/69) | | |
| | **PR-E** | Vision fallback scaffold (disabled by default) | **done** | | |
| ## Headline metrics (canary) | |
| | Metric | Bar | Result | | |
| |--------|-----|--------| | |
| | Skill invoke (n=19) | ≥95% named / ≥85% paraphrase | **100%** shipped | | |
| | Daily-Mac overall (n=69) | no drop vs 0.4 | **100%** shipped | | |
| | Writing neural | hold ≥80% | **92%** | | |
| | Writing p50 | ≤250 ms | **171 ms** | | |
| | Computer / multi-step | hold 100% | **100%** | | |
| | IR honesty (n=112) | ≥45% neural | **46.4%** (v0.4 lineage) | | |
| ### Daily-Mac scorecard (v1.1 expanded) | |
| | Slice | Shipped | Neural | p50 | | |
| |-------|---------|--------|-----| | |
| | Writing (n=25) | **100%** | **92%** | **171 ms** | | |
| | Computer (n=44) | **100%** | — | ~0 ms FP | | |
| | Multi-step (n=25) | **100%** | — | ~0 ms FP | | |
| | **Overall (n=69)** | **100%** | — | mean ~60 ms | | |
| Scorecard: `artifacts/loudink_v1/product_scorecard.json` | |
| Skill bench: `artifacts/benchmarks/loudink_v1_1/skill_invoke/` | |
| ## Production stack (v1.1 canary) | |
| ``` | |
| writer: artifacts/sft_loudink_v1_rft_daily/adapters | |
| polish: structural (+ daily rules) | |
| ir: artifacts/sft_loudink_v1_1_skill_invoke_ir/adapters | |
| ir_fallback: artifacts/sft_loudink_v1_ir_honesty/adapters | |
| skills: SkillStore (example seed in publish bundle) | |
| runner: loudink_v1 + skill_store_path | |
| bench: configs/benchmark_loudink_v1_daily_mac.yaml | |
| configs/benchmark_loudink_v1_1_skill_invoke.yaml | |
| ``` | |
| ## Out of scope (later product) | |
| - Client recorder UI (Flowcast owns capture UX) | |
| - Optional real vision weights/resolver | |
| - AirDrop / Bluetooth / long-horizon 1:1 prep suites (roadmap backlog) | |
| - Training cloud models on private user recordings (forbidden by default) | |