---

license: cc-by-nc-sa-4.0

base_model: naver/splade-v3

tags:

• splade
• sparse-retrieval
• gguf
• crispembed

library_name: crispembed

---

SPLADE-v3 — GGUF (CrispEmbed)

GGUF conversions of naver/splade-v3

for sparse (learned-lexical) retrieval with

CrispEmbed — a dependency-free

C/C++ embedding runtime (--sparse mode / crispembed_encode_sparse).

SPLADE produces a sparse vector of weighted vocabulary term expansions

(log(1 + ReLU(MLM_logits)), max-pooled over tokens), not a dense embedding.

Files

| File | Quant | Size | Sparse-cos vs HF fp32 |

|------|-------|------|-----------------------|

| splade-v3-iq4_xs.gguf | IQ4_XS + imatrix | 68 MB | 0.9971 (compact default) |

| splade-v3-q8_0.gguf | Q8_0 | 111 MB | 1.0000 |

| splade-v3-f16.gguf | F16 | 256 MB | 1.0000 (precision control) |

Sparse-cos = cosine over the full 30522-dim vocabulary term-weight vector vs

the original PyTorch naver/splade-v3 (BertForMaskedLM + SPLADE pooling),

averaged over a probe set. The IQ4_XS build uses a CrispEmbed importance matrix

collected over a calibration corpus. The MLM/SPLADE head is preserved and

verified present in every quant.

Usage

crispembed -m splade-v3-iq4_xs.gguf --sparse "your query text"
# → token_id  weight   (one per expansion term)

License & attribution

Derived from naver/splade-v3,

licensed CC-BY-NC-SA-4.0 (non-commercial, share-alike). These GGUF

conversions inherit the same license: non-commercial use only, attribution

to Naver required, and derivatives must be shared alike. See the base model

card and the SPLADE papers for citation.

Provenance and EU AI Act Art. 53 note

• Upstream model: naver/splade-v3 — published by naver.
• Upstream licence: cc-by-nc-sa-4.0. This repository redistributes under the same terms; it grants no rights the upstream licence does not.
• What was done here: format conversion and/or quantisation only (GGUF). No training, no fine-tuning, no merging, no distillation, no change to architecture, vocabulary or capability. Only the numeric representation of the upstream weights differs.
• Training data: documented — where it is documented at all — by the upstream provider; see the upstream model card. No training data was used, added or selected by this repository. No training-content summary was found on the upstream model card at the time of writing; that documentation gap is upstream's and is not filled here.
• Provider status: under Regulation (EU) 2024/1689 the upstream authors remain the provider of this model. Converting the serialisation format does not make this repository the provider of a new general-purpose AI model, and no such claim is made. Questions about training content, copyright policy or model capability belong upstream.