---
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`](https://huggingface.co/naver/splade-v3)
for sparse (learned-lexical) retrieval with
[CrispEmbed](https://github.com/CrispStrobe/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

```bash
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`](https://huggingface.co/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.