README.md

---
license: apache-2.0
base_model: Qwen/Qwen3-Embedding-0.6B
tags:
  - coreai
  - sentence-similarity
  - feature-extraction
  - apple-silicon
  - on-device
language:
  - multilingual
pipeline_tag: sentence-similarity
---

# Qwen3-Embedding-0.6B — Core AI export

[Qwen/Qwen3-Embedding-0.6B](https://huggingface.co/Qwen/Qwen3-Embedding-0.6B) as a single static
Core AI graph for macOS 27 / iOS 27: the full sentence-transformers pipeline (Qwen3-0.6B backbone
→ **last-token pooling** → **L2 normalize**) runs in-graph, so one call returns a normalized,
**MRL-truncatable 1024-d** embedding. Multilingual (incl. Japanese), instruction-aware
on-device semantic search / RAG.

**This is an encoder** — one forward over the (right-padded) input → one pooled vector. No
autoregressive loop, no KV cache, no LM head. It runs as a plain `.aimodel` via `AIModel.run`
(like the vision encoders), not the pipelined generate engine.

## Graph contract

| | name | shape | dtype |
|---|---|---|---|
| input | `input_ids` | [1, 512] | int32 (right-padded; pad id 151643) |
| input | `attention_mask` | [1, 512] | int32 (1 = real token, 0 = padding) |
| output | `embedding` | [1, 1024] | fp16, L2-normalized |

The grid (512) is an export-time choice — a smaller grid is proportionally faster for short
queries. Last-token pooling under the causal mask is right-pad safe (real tokens never attend to
trailing pads), so the host just right-pads to the grid.

## Host recipe (everything else is in-graph)

- **Query** → prepend the instruction prefix:
  `Instruct: Given a web search query, retrieve relevant passages that answer the query\nQuery:`
  **Document** → no prefix.
- Tokenize, **right-pad** to 512 (truncate longer text). Run → 1024-d unit vector.
- **Similarity** = cosine = dot product (vectors are unit-norm).
- **Matryoshka (MRL)**: to shrink, take the first D dims (32 ≤ D ≤ 1024) and **re-L2-normalize**
  on the host. Rankings are preserved down to 256; verified to 128.

```python
# Core AI runtime (Python), GPU delegate
import coreai.runtime as rt, numpy as np
from transformers import AutoTokenizer

tok = AutoTokenizer.from_pretrained("tokenizer")
m = await rt.AIModel.load("qwen3-embedding-0.6b_float16_s512_static.aimodel",
        rt.SpecializationOptions.from_preferred_compute_unit_kind(rt.ComputeUnitKind.gpu()))
fn = m.load_function("main")

def embed(text, is_query):
    prefix = ("Instruct: Given a web search query, retrieve relevant passages that "
              "answer the query\nQuery:") if is_query else ""
    enc = tok(prefix + text, padding="max_length", truncation=True, max_length=512,
              return_tensors="np", padding_side="right")
    res = await fn({"input_ids": rt.NDArray(enc["input_ids"].astype(np.int32)),
                    "attention_mask": rt.NDArray(enc["attention_mask"].astype(np.int32))})
    return res["embedding"].numpy()[0]   # [1024], unit-norm
```

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

Downloads this repo on first use and applies the prompts in-process:

```swift
import CoreAIKitEmbeddings

let embedder = try await TextEmbedder(model: .qwen3Embedding0_6B, prompts: .qwen3Embedding)
let query = try await embedder.embed(query: "What is the capital of Japan?")
let doc   = try await embedder.embed(document: "Tokyo is the capital and largest city of Japan.")
let score = TextEmbedder.cosineSimilarity(query, doc)   // unit vectors → dot product = cosine
```

## Bundle layout

```
qwen3-embedding-0.6b_float16_s512_static.aimodel   (~1.1 GB, fp16)
tokenizer/                                          (HF tokenizer files)
reference.json                                      (torch reference embeddings + cosines)
```

## Parity

Precision **fp16**. Verified against the official `sentence-transformers` pipeline (fp32):
per-text embedding cosine **1.000000**, retrieval order identical, MRL rankings preserved at
512 / 256 / 128. On the Core AI GPU delegate the `.aimodel` reproduces the torch reference at
cosine **0.999998** end-to-end (host tokenize → run). Measured ~25 ms (256-grid) / ~45 ms
(512-grid) per embedding on an M4 Max GPU.

## License

Apache-2.0 (upstream model and code are Apache-2.0). Conversion script:
[`conversion/export_qwen3_embedding.py`](https://github.com/john-rocky/coreai-model-zoo/blob/main/conversion/export_qwen3_embedding.py)
in the coreai-model-zoo.