BGE base en v1.5 embeddings (768-d)

models.skillsafe.ai/bge-base-en-v1.5@a5beb1e3/

Vetted New WebGPU

Feature extraction model for transformers.js (fp32). Runs on WASM or WebGPU — 416 MB downloaded once from models.skillsafe.ai, then cached for every SkillSafe app that uses it. Inference happens on your device; nothing you enter is uploaded to load it, and it never costs a credit. Weights from Hugging Face · skillsafe-ai/bge-base-en-v1.5, pinned at 2bf8cb2c03d3 — also loadable straight from Hugging Face outside SkillSafe (how).

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Details

Catalogue idbge-base-en-v1.5@a5beb1e3
Runtimetransformers.js ≥ 3.0.0
DeviceWASM, WebGPU
Variantfp32
Download416 MB · 1 file
LicenceMIT · notice
Pinned at2bf8cb2c03d39dccca1be63522b1f89257322a95
ApprovedSep 22, 2026
Statusactive — every file is a live vetted hash

Files

Each file is served at an immutable URL; the canonical form is the SHA-256 itself. Tokenizer and config JSON are not here by design — they ship in your app bundle.

PathFormatSizeSHA-256
onnx/model.onnx onnx 416 MB 9bc579acdba2…b91848

Signature

Graph inputs and outputs read from the ONNX bytes at vetting — the tensor names your session.run() call feeds and reads. Symbolic dimensions are shown by name.

Inputs

  • input_ids int64 [batch_size, sequence_length]
  • attention_mask int64 [batch_size, sequence_length]
  • token_type_ids int64 [batch_size, sequence_length]

Outputs

  • last_hidden_state float32 [batch_size, sequence_length, 768]
Use it in an app declaration · SDK loader · transformers.js · URLs · Hugging Face — generated from this entry

Add to the body of POST /v1/apps/{slug}/releases (or a release session). An unknown or withdrawn model is refused with a 400 naming it; the app page then shows "downloads 416 MB · runs on your device" and /models.txt carries the attribution.

{
  "models": [
    {
      "id": "bge-base-en-v1.5",
      "revision": "a5beb1e3"
    }
  ]
}

Evaluation

onnx.checker + onnxruntime CPU smoke run with zero-filled inputs at the declared shapes; per-file SHA-256 pinned to the source; ONNX output vs the PyTorch model from the same commit on real text (import.parity) — imported as published upstream, then checked. Evaluated Sep 22, 2026.

Parity against transformers BertModel from same snapshot, fp32 · last_hidden_state

fileprecisionmax absmean abscosine / PSNR
onnx/model.onnxfp326.4e-61.0e-61.000000

Runs under onnxruntime

Zero-filled inputs at the declared shapes, CPU execution provider on the converter host; the check is that the graph loads, runs, and emits the declared output shapes.

  • onnx/model.onnx: input_ids[1,8], attention_mask[1,8], token_type_ids[1,8] → last_hidden_state[1,8,768] 6 ms

Toolchain: python 3.12.13 · platform Darwin 25.6.0 arm64 · torch 2.10.0 · onnx 1.23.0 · onnxruntime 1.30.0. Recipe models/recipes/bge-base-en-v1.5.yaml (e18fa3565b09). Full manifest.json

Licence & attribution

MIT · licence text · notice

bge-base-en-v1.5: Beijing Academy of Artificial Intelligence (FlagEmbedding), MIT License. https://huggingface.co/BAAI/bge-base-en-v1.5 — the repo's own ONNX export.

Apps that declare this model get this text in their generated /models.txt, so a licence that requires a notice always carries one.

Every file here was approved by exact SHA-256 after a structural audit of the graph, fetched from a content-pinned source, and is served credential-free at an immutable URL. The SDK re-verifies the hash on your device before it caches or returns anything. Missing a variant? Request it — or read how the registry works.