bekko-embedding

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+:

pip install -U "sentence-transformers>=5.0" "transformers>=5.12"

Queries and documents go through the same encode() call — no prefixes or task instructions needed. Pass normalize_embeddings=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 "flash_attention_2". Sentence Transformers selects CUDA automatically, so device is normally unnecessary; to force it, use device="cuda", not "gpu".

from sentence_transformers import SentenceTransformer, util

model = SentenceTransformer(
    "hotchpotch/bekko-embedding-v1-a25m",
    # model_kwargs={"attn_implementation": "sdpa"},  # Optional on GPU
)

query = "What are the characteristics of sushi?"
docs = [
    "A warm noodle soup served in broth with sliced toppings.",
    "天ぷらは魚や野菜に衣をつけて揚げた料理です。",  # "Tempura is battered, deep-fried fish and vegetables."
    "Une fine crepe garnie de sucre, de beurre ou de fruits.",
    "A Japanese dish made with vinegared rice, often shaped with seafood, vegetables, or egg.",
]

query_emb = model.encode(query, normalize_embeddings=True)
doc_emb = model.encode(docs, normalize_embeddings=True)
scores = util.cos_sim(query_emb, doc_emb)[0]

print(scores)
print("best doc:", docs[int(scores.argmax())])

Output (exact scores vary slightly by backend):

tensor([0.2953, 0.2785, 0.3209, 0.4378])
best doc: A Japanese dish made with vinegared rice, often shaped with seafood, vegetables, or egg.

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:

corpus = [
    "Sushi is a Japanese dish of vinegared rice topped with seafood.",
    "The Eiffel Tower is a wrought-iron lattice tower in Paris, France.",
    "Mount Fuji is the highest mountain in Japan, at 3,776 meters.",
    "Python is a programming language known for its readability.",
    "La Sagrada Família es una basílica de Barcelona diseñada por Antoni Gaudí.",
]
corpus_emb = model.encode(corpus, normalize_embeddings=True)

for query in [
    "日本で一番高い山は？",  # "What is the highest mountain in Japan?"
    "Who designed the famous basilica in Barcelona?",
]:
    query_emb = model.encode(query, normalize_embeddings=True)
    hits = util.semantic_search(query_emb, corpus_emb, top_k=2)[0]
    print(query)
    for hit in hits:
        print(f"  {hit['score']:.3f}  {corpus[hit['corpus_id']]}")

日本で一番高い山は？
  0.457  Mount Fuji is the highest mountain in Japan, at 3,776 meters.
  0.142  Sushi is a Japanese dish of vinegared rice topped with seafood.
Who designed the famous basilica in Barcelona?
  0.563  La Sagrada Família es una basílica de Barcelona diseñada por Antoni Gaudí.
  0.126  The Eiffel Tower is a wrought-iron lattice tower in Paris, France.

That's everything you need for basic use. For more speed — OpenVINO on CPU, Flash Attention on GPU, browser inference, smaller embeddings — see Optimized Inference below.

Benchmark results

In the chart above, up and to the left is better: more retrieval quality from fewer active parameters. The step line shows the best observed score within each active-parameter budget, and outlined markers identify Pareto-efficient models. Both bekko models sit in that upper-left region, scoring at or above many models several times their size — which is the whole point of the project.

On the 131-task MMTEB Multilingual v2 suite, a25m scores 57.5 Retrieval and 58.3 Mean(Task) with 24.9M active parameters. That edges out gte-multilingual-base on Retrieval and ties it on Mean with ~4.5x fewer active parameters, and beats multilingual-e5-large and BGE-M3 on Retrieval with ~12x fewer.

Scores are ×100. Retrieval is task-macro nDCG@10, and Mean is the mean across all 131 tasks. Competitor values use the official 2026-06-28 snapshot.