Model reference · open weights

splade-code

Embeddings naver Embeddings 1 build Non-commercial 543 dl/mo

splade-code is an open-weight embedding model from naver. splade-code-06B (BF16) weighs 1.2 GB; the smallest configuration that runs it is RTX 3060 12 GB.

For self-hosting, compare this build's requirements below with the GPU you plan to rent.

  • SPLADE-Code-06B is a sparse retrieval model developed by Naver for code retrieval tasks.
  • It features 596 million parameters and a context length of 40,960 tokens.
  • The model is released under the cc-by-nc-sa-4.0 license.

Summary of the naver/splade-code-06B model card, 2026-10-01

What it is

Released bynaver
Released2026-02-24
Parameters596M
VRAM1.2 GB for the weights

What it runs on

Memory and cards for splade-code-06B (BF16)

1.2 GBweights, file size
1.1 GBruntime overhead
CardRunsMemory
RTX 3060 12 GBfits11.6 GB
RTX 4060 Ti 16 GBfits15.4 GB
RTX 3090 24 GBfits23.4 GB
RTX 4090 24 GBfits23.4 GB
RTX 5090 32 GBfits31.0 GB
L40S 48 GBfits44.0 GB
A100 80 GBfits78.2 GB
H100 80 GBfits78.1 GB
RTX PRO 6000 Blackwell 96 GBfits93.8 GB
DGX Spark (GB10) 128 GB unifiedfits107 GB
H200 141 GBfits138 GB
B200 180 GBfits176 GB

From the model card

What naver says about splade-code

Read the model card

SPLADE-Code-06B is a sparse retrieval model designed for code retrieval tasks. It is the top-performing models on MTEB for models below 1B (at time of writing, Feb 2026).

Usage

Using Sentence Transformers

Install Sentence Transformers:

pip install sentence_transformers
from sentence_transformers import SparseEncoder

model = SparseEncoder("naver/splade-code-06B", trust_remote_code=True)

queries = [
    "SELECT *\nFROM Student\nWHERE Age = (\nSELECT MAX(Age)\nFROM Student\nWHERE Group = 'specific_group'\n)\nAND Group = 'specific_group';"
]

query_embeddings = model.encode(queries)
print(query_embeddings.shape)
# torch.Size([1, 151936])

sparsity = model.sparsity(query_embeddings)
print(sparsity)
# {'active_dims': 1231.0, 'sparsity_ratio': 0.991897904380792}

decoded = model.decode(query_embeddings, top_k=10)
print(decoded)
# [[
#     ("Ġgroup", 2.34375),
#     ("Ġage", 2.34375),
#     ("ĠAge", 2.34375),
#     ("ĠStudent", 2.296875),
#     ("Ġspecific", 2.296875),
#     ("_group", 2.296875),
#     ("ĠMax", 2.21875),
#     ("Ġmax", 2.21875),
#     ("Ġstudent", 2.203125),
#     ("ĠGroup", 2.1875),
# ]]

Using Transformers

pip install transformers
from transformers import AutoModelForCausalLM, AutoModel
import os
import torch

splade = AutoModelForCausalLM.from_pretrained("naver/splade-code-06B", trust_remote_code=True)
device = (torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu"))
splade.to(device)
splade.eval()
queries = ["SELECT *\nFROM Student\nWHERE Age = (\nSELECT MAX(Age)\nFROM Student\nWHERE Group = 'specific_group'\n)\nAND Group = 'specific_group';"]
bow_dict = splade.encode(queries, prompt_type="query", top_k_q=10, return_dict=True, print_dict=True)
+--------------------------------------------------------------------+
|                        TOP ACTIVATED WORDS                         |
+--------------------------------------------------------------------+

* INPUT: SELECT *
FROM Student
WHERE Age = (
SELECT MAX(Age)
FROM Student
WHERE Group = 'specific_group'
)
AND Group = 'specific_group';

Ġgroup                    | ████████████████████ 2.34
Ġage                      | ███████████████████ 2.33
ĠAge                      | ███████████████████ 2.33
_group                    | ███████████████████ 2.30
ĠStudent                  | ███████████████████ 2.30
Ġspecific                 | ███████████████████ 2.28
Ġmax                      | ██████████████████ 2.22
ĠMax                      | ██████████████████ 2.22
Ġstudent                  | ██████████████████ 2.20
ĠGroup                    | ██████████████████ 2.19

Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.

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