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Cataloged from lightonai/LightOn-rerank-LW-2B
All models by lightonai →PW-0.8B | LW-0.8B | PW-2B | LW-2B | PW-4B | LW-4B
Production retrieval pipelines usually need two rerankers: one for text passages and one for visual documents (PDF pages, slides, scans). LightOn-rerank models are unified cross-encoder rerankers: a single model scores both text passages and document page images against a query, on top of any first-stage retriever (BM25, dense embeddings, or ColPali-family late-interaction models).
The models are built on Qwen3.5 backbone (hybrid linear + full attention) and jointly fine-tuned on text and visual reranking data with mixed-modality batches (LoRA, merged into the released weights). Training data is English-only; French performance transfers zero-shot from the multilingual backbone.
The family comes in two scoring flavours × three sizes (0.8B / 2B / 4B):
logit("Yes") − logit("No"). One forward pass per candidate and no generation.[2] > [4] > [1] > [3]). Larger candidate pools are ranked with a sliding window (window 4, stride 2, bottom-to-top). Cross-document attention makes LW markedly stronger on hard visual reranking, and unlike pointwise scoring it keeps improving with backbone size.LightOn-rerank-LW-2B is the best 2B-class model of the family: 62.66 nDCG@10 on ViDoRe V3, improving over the ColQwen2.5 first stage by 7.1 nDCG points and leading every open 2B-class multimodal reranker we measured. Putting 4 candidates in a single forward pass lets documents attend to each other, which fixes the discriminative weakness of independent per-candidate scoring: +2.8 nDCG@10 over the pointwise sibling with identical backbone and data, winning 13 of 16 splits.
ViDoRe V3 (visual document reranking, 8 domains × EN/FR queries), overall nDCG@10, ColQwen2.5-v0.2 first stage, retrieve 100 / rerank 100. All models, including baselines, were re-evaluated under this same two-stage protocol, so numbers are mutually comparable but not comparable to vendor-reported end-to-end results.
Overall nDCG@10: 62.66 (EN 64.01 / FR 61.31), the best 2B-class result under this protocol: +3.5 over the official Qwen3-VL-Reranker-2B (59.18) and +3.3 over jina-reranker-m0 (59.40). The training data is English-only, so the FR column is pure zero-shot cross-lingual transfer; FR even beats EN on three domains (finance_fr, energy, physics).
13 datasets, nDCG@10, BM25 first stage, retrieve 100 / rerank 100, all models re-evaluated under the same protocol (this model runs the same W=4 stride=2 sliding window it uses for document pages; baselines score through their native text paths). ⚠️ marks datasets in the text training mix (NQ, MSMARCO); the clean mean excludes them.
The ordering flips relative to ViDoRe V3: on vision this model leads the 2B class; on text jina-reranker-m0 leads decisively, and this model is roughly tied with Qwen3-VL-Reranker-2B. On text the pointwise sibling is slightly stronger (PW-2B: 49.13 clean mean) — the mirror image of vision, where listwise wins at every size.
[pos, neg_0, neg_1, neg_2] 4-list with hard negatives mined via the NV-Retriever approach with GTE-ModernBERT) + 106k vision groups (ColPali train set with negatives mined by Nomic). The gold permutation (pos > neg_0 > neg_1 > neg_2) is constructed directly from the mining metadata; training is cross-entropy on the permutation tokens only.transformers >= 5.4.0 (qwen3_5 architecture)The model ranks 4 candidates per prompt by generating a permutation string such as [2] > [1] > [4] > [3]. Candidate pools larger than 4 are ranked with a sliding window (window 4, stride 2) moving from the bottom of the list to the top, so the best candidates bubble up to the front. If a generation cannot be parsed, fall back to the input order (observed fallback rate in our evals: ≈0.01%).
For page images, build the window prompt with interleaved image placeholders instead:
If a window has fewer than 4 candidates, pad it by repeating the last candidate and drop the duplicates from the returned order. Our evaluations ran this model with HF generate().
Rule of thumb: LW models are stronger at every size (and the gap grows with size); PW models are cheaper to serve and score candidates independently. For the best quality pick LW-4B; for the best quality/cost trade-off pick LW-2B; for maximum throughput on text-heavy workloads pick a PW model.
import reimport torchfrom transformers import AutoModelForImageTextToText, AutoProcessor
model_id = "lightonai/LightOn-rerank-LW-2B"model = AutoModelForImageTextToText.from_pretrained( model_id, dtype=torch.bfloat16, attn_implementation="flash_attention_2", # optional, remove if flash-attn is not installed device_map="cuda",).eval()processor = AutoProcessor.from_pretrained(model_id)
PROMPT = "<|im_start|>user\n{user}<|im_end|>\n<|im_start|>assistant\n"PERM_RE = re.compile(r"\[(\d)\]\s*>\s*\[(\d)\]\s*>\s*\[(\d)\]\s*>\s*\[(\d)\]")
def rank_window(query: str, docs: list[str]) -> list[int]: """Rank exactly 4 text passages; returns window indices, most relevant first.""" body = "\n".join(f"[{i + 1}]: {d}" for i, d in enumerate(docs)) user = f"Query: {query}\n\nRank these passages from most to least relevant:\n{body}\n\nRanking:" inputs = processor(text=[PROMPT.format(user=user)], return_tensors="pt").to(model.device) out = model.generate( **inputs, max_new_tokens=30, do_sample=False, pad_token_id=processor.tokenizer.eos_token_id, ) completion = processor.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True) m = PERM_RE.search(completion) return [int(g) - 1 for g in m.groups()] if m else list(range(4))
def rerank(query: str, docs: list, window: int = 4, stride: int = 2) -> list[int]: """Sliding-window listwise rerank; returns document indices, most relevant first.""" order = list(range(len(docs))) positions = list(range(max(0, len(docs) - window), -1, -stride)) if positions and positions[-1] != 0: positions.append(0) for pos in positions: end = min(pos + window, len(docs)) p = max(0, end - window) if end - p < 2: continue perm = rank_window(query, [docs[i] for i in order[p:end]]) order[p:end] = [order[p + j] for j in perm] return order
query = "What is late interaction in neural information retrieval?"documents = ["passage 1 ...", "passage 2 ...", "passage 3 ...", "passage 4 ...", "passage 5 ..."]print(rerank(query, documents))def rank_window_images(query: str, images: list) -> list[int]: # 4 PIL images content = [{"type": "text", "text": f"Query: {query}\n\nRank these documents from most to least relevant:\n"}] for i, img in enumerate(images): content += [ {"type": "text", "text": f"[{i + 1}]: "}, {"type": "image", "image": img}, {"type": "text", "text": "\n"}, ] content.append({"type": "text", "text": "\nRanking:"}) text = processor.apply_chat_template( [{"role": "user", "content": content}], tokenize=False, add_generation_prompt=True ) inputs = processor(text=[text], images=list(images), return_tensors="pt").to(model.device) out = model.generate( **inputs, max_new_tokens=30, do_sample=False, pad_token_id=processor.tokenizer.eos_token_id, ) completion = processor.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True) m = PERM_RE.search(completion) return [int(g) - 1 for g in m.groups()] if m else list(range(4))@misc{ananya2026lightonrerank, title={One Adapter, Both Modalities: Field Notes from Building and Serving a Multimodal Reranker}, author={Ananya, Ishrat Jahan and Chatelain, Amelie}, year={2026}, howpublished={\url{https://huggingface.co/blog/lightonai/lighton-rerank}},}