MODEL PROFILE

octomind-embed

muvon · octomind-embed

Embedding model for octomind capability / skill auto-activation: `ibm-granite/granite-embedding-30m-english` (30M params, 6 layers, 384-dim, CLS-pooled, prefix-free, English) fine-tuned on trigger phrases from the octomind-tap capabilities + skills catalog and blended back into the base as a WiSE-FT model soup, which beats both the base and the raw fine-tune on the runtime gate (mean-of-top-3 cosine + threshold + margin). Training: rule-based + LLM paraphrase augmentation, one epoch of `CachedMultipleNegativesRankingLoss` (scale 10) on in-class pairs and positive-aware hard-negative triplets, `MatryoshkaLoss` over [384, 256, 192, 128, 96], then weight interpolation with the base. octomind loads `onnx:muvon/octomind-embed` via octolib's ONNX provider (`MODEL_NAME` in `octomind/src/embeddings/mod.rs`). Runtime thresholds are model-specific and calibrated against the int8 graph (`AUTO_ACTIVATE_THRESHOLD` / `_MARGIN` in `capability.rs`, `SEMANTIC_*` in `skill.rs`).

license:apache-2.0
Context windowNot disclosed
Input → Output待核验 → 待核验
Model weightsNot verified
Profile updated2026/10/29 22:33:43
OVERVIEW

Model overview

Embedding model for octomind capability / skill auto-activation: `ibm-granite/granite-embedding-30m-english` (30M params, 6 layers, 384-dim, CLS-pooled, prefix-free, English) fine-tuned on trigger phrases from the octomind-tap capabilities + skills catalog and blended back into the base as a WiSE-FT model soup, which beats both the base and the raw fine-tune on the runtime gate (mean-of-top-3 cosine + threshold + margin). Training: rule-based + LLM paraphrase augmentation, one epoch of `CachedMultipleNegativesRankingLoss` (scale 10) on in-class pairs and positive-aware hard-negative triplets, `MatryoshkaLoss` over [384, 256, 192, 128, 96], then weight interpolation with the base. octomind loads `onnx:muvon/octomind-embed` via octolib's ONNX provider (`MODEL_NAME` in `octomind/src/embeddings/mod.rs`). Runtime thresholds are model-specific and calibrated against the int8 graph (`AUTO_ACTIVATE_THRESHOLD` / `_MARGIN` in `capability.rs`, `SEMANTIC_*` in `skill.rs`).

Key features

特点摘要待依据来源补齐,不凭模型名称推断能力。

Limitations & considerations

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PRICING & COST

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INDEPENDENT BENCHMARKS

Public benchmarks & run configuration

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

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Frequently asked questions

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Sources & traceability

许可证:apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。