---
license: apache-2.0
base_model: Qwen/Qwen3-Reranker-0.6B
tags:
  - coreai
  - text-ranking
  - reranker
  - apple-silicon
  - on-device
language:
  - multilingual
pipeline_tag: text-ranking
---

> **Mirror** of [`mlboydaisuke/Qwen3-Reranker-0.6B-CoreAI`](https://huggingface.co/mlboydaisuke/Qwen3-Reranker-0.6B-CoreAI) — the canonical repo ([CoreAI Model Zoo](https://github.com/john-rocky/coreai-model-zoo)). Updates land there first.


# Qwen3-Reranker-0.6B — Core AI export

[Qwen/Qwen3-Reranker-0.6B](https://huggingface.co/Qwen/Qwen3-Reranker-0.6B) as a single static
Core AI graph for macOS 27 / iOS 27. The **cross-encoder** that closes the on-device RAG loop —
embed (with [Qwen3-Embedding-0.6B-CoreAI](https://huggingface.co/mlboydaisuke/Qwen3-Embedding-0.6B-CoreAI))
→ **rerank** → generate, all local and private.

A cross-encoder reads one `query + document` sequence and asks the LM a yes/no question; the
relevance score is the softmax weight on **"yes"** vs **"no"** at the final token. So it keeps the
LM head (the embedder drops it), but it's still a plain `.aimodel` run via `AIModel.run` — one
forward, no generation. The scoring tail (gather last token → head on that one position → 2-way
softmax) is baked in-graph.

## Graph contract

| | name | shape | dtype |
|---|---|---|---|
| input | `input_ids` | [1, 512] | int32 (right-padded; pad id 151643) |
| input | `attention_mask` | [1, 512] | int32 (1 = real, 0 = padding) |
| output | `probs` | [1, 2] | fp16, `softmax([no, yes])` — **relevance = `probs[0,1]` = P(yes)** |

## Host recipe

Format the pair exactly like the upstream model card, then right-pad to 512:

```python
import coreai.runtime as rt, numpy as np
from transformers import AutoTokenizer

tok = AutoTokenizer.from_pretrained("tokenizer")
PREFIX = ("<|im_start|>system\nJudge whether the Document meets the requirements based on the "
          "Query and the Instruct provided. Note that the answer can only be \"yes\" or "
          "\"no\".<|im_end|>\n<|im_start|>user\n")
SUFFIX = "<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n"
INSTR  = "Given a web search query, retrieve relevant passages that answer the query"

m = await rt.AIModel.load("qwen3-reranker-0.6b_float16_s512_static.aimodel",
        rt.SpecializationOptions.from_preferred_compute_unit_kind(rt.ComputeUnitKind.gpu()))
fn = m.load_function("main")

def score(query, doc, S=512):
    body = f"<Instruct>: {INSTR}\n<Query>: {query}\n<Document>: {doc}"
    ids = (tok.encode(PREFIX, add_special_tokens=False)
           + tok.encode(body, add_special_tokens=False)
           + tok.encode(SUFFIX, add_special_tokens=False))
    n = len(ids); ids = ids + [151643] * (S - n)
    mask = [1] * n + [0] * (S - n)
    res = await fn({"input_ids": rt.NDArray(np.asarray([ids], np.int32)),
                    "attention_mask": rt.NDArray(np.asarray([mask], np.int32))})
    return float(res["probs"].numpy()[0, 1])   # P(yes) = relevance; sort candidates by this
```

The instruction is swappable per task (the model is instruction-aware). Right-pad is equivalent to
the upstream left-pad + `logits[:, -1]` (the graph reads the true last token from the mask).

### Swift — [CoreAIKit](https://github.com/john-rocky/coreai-kit)

Downloads this repo on first use and formats the pair in-process:

```swift
import CoreAIKitEmbeddings

let reranker = try await Reranker(model: .qwen3Reranker0_6B)
let ranked = try await reranker.rerank(
    query: "What is the capital of Japan?",
    documents: ["Tokyo is the capital of Japan.", "Python is a programming language."])
// ranked[0].document is most relevant; ranked[i].score is P(yes) in [0, 1]
```

## Bundle layout

```
qwen3-reranker-0.6b_float16_s512_static.aimodel   (~1.1 GB, fp16)
tokenizer/                                          (HF tokenizer files)
reference.json                                      (pairs, scores, prompt scaffolding)
```

## Parity

Precision **fp16**. Verified against the official `AutoModelForCausalLM` scoring (fp32): the
in-graph wrapper reproduces P(yes) **exactly** (|Δ| = 0.00000 over 6 relevant/irrelevant pairs),
relevant pairs 0.98–1.00 vs irrelevant ≈ 0.0000, ranking preserved. On the Core AI GPU delegate
the `.aimodel` matches the torch reference within **|Δ| < 0.0005** end-to-end. Measured **45.7 ms
per pair-score** on an M4 Max GPU (512 grid).

## License

Apache-2.0 (upstream model and code are Apache-2.0). Conversion script:
[`conversion/export_qwen3_reranker.py`](https://github.com/john-rocky/coreai-model-zoo/blob/main/conversion/export_qwen3_reranker.py)
in the coreai-model-zoo.