---
license: apache-2.0
language:
  - en
  - ar
tags:
  - retrieval
  - search
  - sparse-encoder
  - splade
  - bge-m3
  - lora
library_name: peft
---

# Search Stack — query towers

The **query-side** encoders for a two-stage product search over a multilingual
(EN + AR) fashion catalogue. These are the exact weights that produced the
vectors in the Qdrant collection they are used against — a cosine ANN is only
meaningful if query and documents share an encoder, so these are part of the
retrieval contract, not an interchangeable dependency.

| path | size | what it is |
|---|---|---|
| `adapter/` | 4.7 MB | LoRA (r=8, q/k/v) on `BAAI/bge-m3` — the dense query tower |
| `tokenizer/` | 17 MB | matching bge-m3 tokenizer |
| `splade_v6_v2/` | 545 MB | fine-tuned SPLADE sparse encoder (distilbert-base-multilingual-cased) |

The `BAAI/bge-m3` backbone is not included — it is unmodified upstream and
`transformers` fetches it on first run.

## Use

```python
from huggingface_hub import snapshot_download
snapshot_download("rdxtremity/jev-reranking",
                  allow_patterns=["adapter/*", "tokenizer/*", "splade_v6_v2/*"],
                  local_dir="models")
```

Dense query encoding is CLS-pool + L2-normalise, max 64 tokens, producing a
1024-d unit vector:

```python
from transformers import AutoModel, AutoTokenizer
from peft import PeftModel
import torch

tok = AutoTokenizer.from_pretrained("models/tokenizer")
model = PeftModel.from_pretrained(AutoModel.from_pretrained("BAAI/bge-m3"),
                                  "models/adapter").eval()
enc = tok(["red dress"], return_tensors="pt", truncation=True, max_length=64)
with torch.no_grad():
    out = model(**enc)
vec = torch.nn.functional.normalize(out.last_hidden_state[:, 0], p=2, dim=-1)
```

Sparse encoding uses `sentence_transformers.SparseEncoder` on `splade_v6_v2/`,
with `max_seq_length = 128`.

Only one short forward pass runs at query time, so CPU is sufficient.