---
license: apache-2.0
base_model: Qwen/Qwen3-Reranker-0.6B
library_name: peft
tags:
  - reranker
  - memory-retrieval
  - long-term-memory
  - dialog
  - lora
  - distillation
  - lycheemem
language:
  - en
pipeline_tag: text-classification
---

# LycheeMem Reranker v1

A LoRA adapter on **Qwen3-Reranker-0.6B** for **long-term memory dialog retrieval**. Built for [LycheeMem](https://github.com/LycheeMem/lycheemem).

## Evaluation

MAP (mean average precision), higher is better. Queries strictly held out from training.

| Model | LongMemEval-S<br/>(373 q, held-out) | MSC-MemFuse-MC10<br/>(27 q, held-out) | HotpotQA distractor<br/>(7,405 q, OOD) |
|---|---|---|---|
| **LycheeMem Reranker v1** | **0.9185** | **0.7457** | 0.7063 |
| BGE-Reranker-v2-m3 (560M) | 0.8647 | 0.5503 | **0.8002** |
| Δ | +5.4 pp | +19.5 pp | −9.4 pp |

Full metrics:

| Benchmark | hit@10 | R@5 | R@10 | MAP | NDCG@10 |
|---|---|---|---|---|---|
| LongMemEval-S held-out | 1.000 | 0.964 | 0.988 | 0.919 | 0.940 |
| MSC-MemFuse-MC10 held-out | 1.000 | 0.799 | 0.896 | 0.746 | 0.786 |
| HotpotQA distractor (OOD) | 0.987 | 0.793 | 0.890 | 0.706 | 0.769 |

**Use this model** for memory dialog reranking. **Use BGE-Reranker-v2-m3** for general retrieval.

## Training

| Source | Queries | Pairs |
|---|---|---|
| LongMemEval-S (cleaned-overlap) | 127 | 6,018 |
| MSC-MemFuse-MC10 (answer-turn) | 299 | 14,950 |
| Total | 426 | 20,968 |

90/10 query-stratified split: 18,947 train / 2,021 held-out.

Labels are 5-level continuous (`{0.0, 0.2, 0.4, 0.6, 0.8, 1.0}`) distilled from DeepSeek V4 Pro on the mid-tier candidates retrieved by the upstream retriever. Trained with LoRA r=16, BCE-with-logits against continuous targets, 3 epochs.

## Usage

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

BASE = "Qwen/Qwen3-Reranker-0.6B"
ADAPTER = "fuhao23/reranker_v1"

tok = AutoTokenizer.from_pretrained(BASE, trust_remote_code=True)
if tok.pad_token is None:
    tok.pad_token = tok.eos_token

base = AutoModelForSequenceClassification.from_pretrained(
    BASE, num_labels=1, torch_dtype=torch.bfloat16, trust_remote_code=True
)
base.config.pad_token_id = tok.pad_token_id
model = PeftModel.from_pretrained(base, ADAPTER).eval().to("cuda")

INSTRUCT = "Given a user query, retrieve memory snippets that answer the query"

def score(query: str, candidates: list[str], max_len: int = 512) -> list[float]:
    texts = [
        f"<Instruct>: {INSTRUCT}\n<Query>: {query}\n<Document>: {c}"
        for c in candidates
    ]
    enc = tok(texts, padding=True, truncation=True, max_length=max_len,
              return_tensors="pt").to(model.device)
    with torch.inference_mode():
        logits = model(**enc).logits.squeeze(-1).float().cpu().tolist()
    return logits
```

Apply `torch.sigmoid` for normalized probabilities.

## Limitations

- Specialized for dialog memory; trails BGE-Reranker-v2-m3 on out-of-domain retrieval.
- English-only training distribution.
- MSC-MemFuse-MC10 held-out is small (27 queries); LongMemEval-S held-out (373) is the primary in-domain reference.
- Continuous labels are LLM-distilled (DeepSeek V4 Pro), not human-annotated.
- Reports retrieval-stage metrics; end-to-end answer accuracy with this reranker integrated is not reported here.

## Citation

```bibtex
@misc{lycheemem_reranker_v1,
  title  = {LycheeMem Reranker v1: A Domain-Specialized Reranker for Long-Term Memory Dialog Retrieval},
  author = {LycheeMem Project},
  year   = {2026},
  url    = {https://huggingface.co/fuhao23/reranker_v1}
}
```

## License

Apache 2.0.