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Instructions to use baya1116/hypernet-sp-distill with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use baya1116/hypernet-sp-distill with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir hypernet-sp-distill baya1116/hypernet-sp-distill
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
| # Recall β recommended config (2026-06-14) | |
| Best end-to-end setup from the overnight campaign. Full write-up: see | |
| `cotscan_live/STATUS_2026-06-14_recall_improvements.md` in the git repo. | |
| ## Use these artifacts | |
| | component | path | why | | |
| |---|---|---| | |
| | GATE | `trigger_experiment/recall_contrastive_v3/artifacts/gate.npz` | best generation-distribution false-fire; cot/chat AUC ~0.90/0.93 preserved | | |
| | RETRIEVER (generation) | raw-QK `BlockArchive(mode="qk")` | raw-QK >= learned indexer in generation (88% vs 75%) | | |
| | RETRIEVER (held-out / offline) | `trigger_experiment/recall_mix_v1/artifacts/indexer.npz` | top-2 0.998 held-out | | |
| | pooler / base / package | `fft_out/pooler.pt`, `fft_hf`, `recall_kit/` | unchanged | | |
| ## Operating threshold (generation, raw-QK) | |
| - recall-favoring: **thresh β β1.5 to β2.0** β ~88% chat recall (SP-without-recall = 0%). | |
| - precision-favoring: **thresh β β1.2** β ~75% recall, casual false-fire ~38%. | |
| ## Measured (multi-fact chat needle, N=8, item-disjoint) | |
| - recall 88% / retrieval 100% (raw-QK) / SP-no-recall 0%. | |
| - detection AUC: cot ~0.90, chat ~0.93 (maintained vs Dolphin-only 0.888 chat). | |
| - casual false-fire: held-out per-position 3β5%; per-turn generation ~62% @88% recall β the | |
| remaining lever is a RUNTIME mitigation (fire-once-per-turn / hysteresis / relevance gate), | |
| not more gate data. | |
| ## Gate lineage | |
| dolphin-only (v4) -> recall_mix_v1 (diversified) -> recall_contrastive_v1/v2 (hard negatives) | |
| -> recall_contrastive_v3 (generation-distribution negatives, recommended). | |