kanana-2-embedding

🤗 kanana-2-3b-embedding  |  🤗 kanana-2-1.3b-embedding  |  📕 Blog

kanana-2-1.3b-embedding

News 🔥

• 2026/10/29: 🤗 Released the kanana-2-3b-embedding and kanana-2-1.3b-embedding model weights.
• 2026/07/28: 📕 Published a blog post about the development of the Kanana-2 SLM series.

Introduction

Kanana-2-embedding is Kakao's text embedding model series for Korean and English retrieval. This release includes 3B and 1.3B models. The models are built on Kakao's Kanana-2 SLMs, converted to bidirectional attention with mean pooling. They produce text embeddings for semantic search, retrieval-augmented generation (RAG), and text similarity.

Highlights

• Korean retrieval: kanana-2-3b-embedding achieves the best average score on nano-BEIR (ko) and Ko-Retrieval among the compared models at every Matryoshka dimension.
• Matryoshka representation learning (MRL): Embeddings can be truncated to 1024 / 512 / 256 / 128 dimensions to reduce storage and search cost.
• Long input: The models accept inputs of up to 4,096 tokens.

Model Downloads

Embedding Performance

All models are evaluated with MTEB under the same conditions. Every evaluation runs without any task prompt (raw input text, with no query/document prefix or instruction template). Each cell is the average score of the benchmark, reported per Matryoshka dimension (full / 512 / 256 / 128).

Per-column best scores are bolded.

† Average over six Korean retrieval tasks: MIRACLRetrieval, Ko-StrategyQA, AutoRAGRetrieval, LawIRKo, SQuADKorV1Retrieval, and PublicHealthQA.

‡ Kanana-1 embedding models do not support Matryoshka truncation, so only the full-dim score is reported.

Library versions used for evaluation: transformers==5.8.0, sentence-transformers==5.5.1, vllm==0.20.2, torch==2.11.0+cu129.

Deployment

The model supports Matryoshka truncation to 1024 / 512 / 256 / 128 dimensions.

sentence-transformers

pip install torch sentence-transformers

from sentence_transformers import SentenceTransformer

model_id = "kakaocorp/kanana-2-1.3b-embedding"
model = SentenceTransformer(model_id, trust_remote_code=True)

queries = ["What is the capital of France?"]
documents = ["Paris is the capital of France."]

q = model.encode(queries)
d = model.encode(documents)
scores = model.similarity(q, d)
print(scores)

With MRL truncation:

DIM = 256  # one of: 1024, 512, 256, 128

model = SentenceTransformer(model_id, trust_remote_code=True, truncate_dim=DIM)
embeddings = model.encode(["query text", "document text"])

transformers

pip install torch transformers

import torch
import torch.nn.functional as F
from transformers import AutoModel, AutoTokenizer

model_id = "kakaocorp/kanana-2-1.3b-embedding"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModel.from_pretrained(
    model_id,
    trust_remote_code=True,
    dtype=torch.bfloat16,
).cuda().eval()

texts = ["query text", "document text"]
inputs = tokenizer(
    texts,
    padding=True,
    truncation=True,
    return_tensors="pt",
    max_length=4096,
).to("cuda")

with torch.no_grad():
    out = model(**inputs)

hidden = out.last_hidden_state
mask = inputs["attention_mask"].unsqueeze(-1).float()
pooled = (hidden * mask).sum(dim=1) / mask.sum(dim=1)
embeddings = F.normalize(pooled, dim=-1)

With MRL truncation:

DIM = 256  # one of: 1024, 512, 256, 128
embeddings = F.normalize(pooled[:, :DIM], dim=-1)

vLLM

pip install vllm

import numpy as np
from vllm import LLM

model_id = "kakaocorp/kanana-2-1.3b-embedding"

llm = LLM(
    model=model_id,
    runner="pooling",
    convert="embed",
    trust_remote_code=True,
    enable_prefix_caching=False,
    enable_chunked_prefill=False,
)

outputs = llm.embed(["query text", "document text"])
raw = np.asarray([o.outputs.embedding for o in outputs])
embeddings = raw / np.linalg.norm(raw, axis=-1, keepdims=True)

With MRL truncation:

DIM = 256  # one of: 1024, 512, 256, 128
trunc = raw[:, :DIM]
embeddings = trunc / np.linalg.norm(trunc, axis=-1, keepdims=True)

License

The model weights are released under the KananaOpenLicense.

Citation

@misc{kanana2embedding2026,
  title = {{kanana-2-embedding}},
  author = {Doohae Jung},
  year = {2026},
  url = {https://huggingface.co/collections/kakaocorp/kanana-2-slm}
}

Contact

• Kanana LLM Team Technical Support: kanana-llm@kakaocorp.com
• Business & Partnership Contact: alpha.k@kakaocorp.com