bekko-embedding-a

About

What bekko-embedding-a is

bekko-embedding-v1-a25m bekko-embedding-v1-a25m is an ultra-compact multilingual text embedding model. It has just 25M active parameters — light enough to run comfortably on modest CPUs — yet its retrieval quality is comparable to models with 3–10x more active parameters. For a smaller, faster model, see bekko-embedding-v1-a8m (8M active parameters). You can also try bekko right in your browser: the bekko-embedding-web demo runs the model fully client-side with Transformers.js — no server involved. [!NOTE] For a guided overview of the models, training recipe, and results, read Bekko Embedding: how small can a multilingual retrieval model be?. Highlights - Ultra-compact: just 25M active parameters, with retrieval quality on par with models 3–10x its active-parameter count - 100+ languages, context up to 8k tokens - 384-dim embeddings that truncate cleanly to 256 / 128 / 64 (Matryoshka) - Runs well on CPU — even a Raspberry Pi 5 — with ONNX and OpenVINO artifacts included - Fast on GPU too, with SDPA or Flash Attention 2 - MIT license a8m or a25m? Rule of thumb: a25m is the quality pick. Switch to a8m when CPU budget or latency is tight — it keeps most of the quality and gains about 2.7x CPU throughput. Quickstart We recommend Sentence Transformers 5.0+ and Transformers 5.12+: Queries and documents go through the same encode() call — no prefixes or task instructions needed. Pass normalizeembeddings=True when you plan to search with cosine similarity or dot product. On GPU, SDPA works out of the box with PyTorch and CUDA. Flash Attention 2 requires pip install flash-attn --no-build-isolation; on our RTX 5090 it was about 24% faster, and can be enabled by replacing "sdpa" below with "flashattention2". Sentence Transformers selects CUDA automatically, so device is normally unnecessary; to force it, use device="cuda", not "gpu". Output (exact scores vary slightly by backend): Queries and documents don't need to share a language. Continuing with the same model, a Japanese query finds the right English document in a mixed English / Spanish corpus: That's everything you need for basic use. For more speed — OpenVINO on CPU, Flash Attention on GPU, browser inference, small

Summarised from the published model card. Read the full card on the HuggingFace links below.

Specifications

What it is

How it works

How embedding models work

Variants

Sizes & precisions

Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys bekko-embedding-a for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (bekko-embedding-a below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/embeddings \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"bekko-embedding-a","input":"text to embed"}'

Details

Languages, data & research

Papers

Licence

Open weights

Sources

Weights & code

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