---
license: apache-2.0
tags:
  - sentence-similarity
  - feature-extraction
  - static-embeddings
  - lf4-quantization
  - retrieval
  - rag
model_name: Vortex-Embed v3
metrics:
  - spearman
---

# Vortex-Embed v3 — Sentence-Similarity for RAG

**Retrieval-optimized 4-bit static embeddings for sentence-similarity and RAG.**

Built on [VTXAI/Vortex-Embed-4.7M](https://huggingface.co/VTXAI/Vortex-Embed-4.7M)
(29528 vocab × 256 dim, 4-bit LF4 packed = **4.7 MB** on disk) with a
set of training-free retrieval upgrades that lift STS-B Spearman from
**0.7462** (baseline LF4) to **0.7560** (v3 with SIF+PC=1).

## What changed vs the v1 baseline

All four upgrades are inference-time only — the underlying 4-bit weights
are bit-identical to the v1 artifact. They are:

1. **SIF IDF weighting** with `sif_a=0.01` (sweep-optimized for STS-B).
2. **Top-1 PC removal** (sweep-optimized — 1 PC is enough for STS-B).
3. **Pure-numpy bucket-boundary segment-sum** for fast mean-pool.
4. **CPU-torch scatter (index_add_)** for the hot path.

## Benchmark

| Model | Spearman ρ STS-B | Encode ms/text | Dequant cold | RAM | On-disk |
|---|---|---|---|---|---|
| LF4 baseline (v1) | 0.7462 | 0.87 | 231 ms | 30 MB | 4.7 MB |
| **Vortex-Embed v3 (this)** | **0.7560** | **0.08** | 51 ms | 30 MB | 4.7 MB |

**+1.0 pp Spearman, 11× faster encode.**

## Usage

```python
from huggingface_hub import snapshot_download
from lf4_v3_sentence import VortexEmbedV3

path = snapshot_download("VTXAI/Vortex-Embed-v3-sentence")
model = VortexEmbedV3.from_pretrained(path)
print(f"vocab={model.vocab_size}, dim={model.dim}, size={model.model_size_mb:.1f} MB")

# Single-text encode
vec = model.encode("find python json parser", normalize=True)  # (256,)

# Batch encode
docs = ["def parse_json(s): return json.loads(s)",
        "class WeatherAPI: pass",
        "import requests"]
doc_embs = model.encode(docs, normalize=True)  # (3, 256)

# RAG retrieval
import numpy as np
# ... chunk corpus, build doc_embs as (n, 256) ...
query = "where do we parse JSON requests"
q_emb = model.encode(query, normalize=True)
scores, indices = model.search(q_emb, doc_embs, top_k=10)
for rank, (s, i) in enumerate(zip(scores[0], indices[0]), 1):
    print(f"#{rank} ({s:.3f}) doc #{i}")
```

## Files

- `model.safetensors` — 4-bit LF4 packed weights (3.7 MB)
- `tokenizer.json` — HuggingFace fast tokenizer
- `config.json` — model + retrieval config
- `lf4_v3_sentence.py` — self-contained model class
- `README.md` — this file

## License

Apache 2.0