---
license: apache-2.0
base_model: Qwen/Qwen3-1.7B
pipeline_tag: text-generation
tags:
  - core-ai
  - apple-core-ai
  - ios
  - apple-neural-engine
  - ane
  - on-device
  - qwen3
  - int8
  - w8
  - 4k-context
  - h16p
---

# Qwen3-1.7B · Apple Core AI · ANE · W8 · 4K · h16p

**A complete, directly downloadable Apple Core AI bundle for Qwen3-1.7B,
AOT-compiled for A17 Pro (`h16p`) with Neural Engine preferred.**

> This is a community conversion for iOS 27. It is not an official Apple or
> Qwen release. Neural Engine participation is measured; exclusive ANE
> execution is not claimed.

**Reproduction, tests, recipe, and companion app:**
[`massif-01/qwen3-1.7b-coreai-ios`](https://github.com/massif-01/qwen3-1.7b-coreai-ios)

**Apple upstream request:**
[`apple/coreai-models#116`](https://github.com/apple/coreai-models/issues/116)

## Why this artifact is different

This release targets the static Apple Neural Engine path rather than packaging
another dynamic GPU conversion:

| Property | This release |
| --- | --- |
| Runtime | Apple Core AI + `LanguageModelSession` |
| AOT target | `h16p` |
| Supported chip in AOT inspection | A17 Pro |
| Preferred compute | Neural Engine |
| Transformer projections | W8 per-tensor K-means palettization |
| Tied embedding | Separate INT8 per-tensor quantization |
| Compute | FP16 |
| KV cache | Fixed-size FP16, 448 MiB at 4K |
| Maximum context | 4,096 tokens |
| AOT functions | 34 |
| Physical-device validation | iPhone 15 Pro, iOS 27 |

The static path must survive shape specialization, prompt/extend bucketing,
mutable KV state, AOT compilation, device specialization, and the Foundation
Models event bridge. A Core AI Instruments trace observed 547 MPSGraph
intervals matched by 547 Apple Neural Engine Prediction intervals.

This does **not** imply that ANE is universally faster than GPU. No same-device,
controlled GPU-versus-ANE energy comparison is claimed.

See the dated comparison with adjacent community artifacts in
[`RELATED_WORK.md`](https://github.com/massif-01/qwen3-1.7b-coreai-ios/blob/main/RELATED_WORK.md).

## Quality-first selection

W8 was selected through a frozen numerical gate, not a successful prompt
smoke. Multiple uniform W4 and mixed W4/W8 candidates were rejected before the
final mechanism was locked.

| Evaluation | Mean logits cosine | Minimum cosine | Top-1 agreement | Mean NLL delta |
| --- | ---: | ---: | ---: | ---: |
| W8 tuning set | 0.997067 | 0.967261 | 98.44% | 0.002985 |
| **W8 frozen holdout** | **0.996598** | **0.959625** | **98.37%** | **0.003167** |

Representative rejected W4 and mixed W4/W8 candidates ranged from `0.856886`
to `0.981232` mean cosine. These measurements establish conversion fidelity;
they are not a general downstream benchmark ranking. Full WikiText-2
perplexity is not claimed as complete.

Machine-readable evidence is included under `evidence/` and maintained in the
[GitHub repository](https://github.com/massif-01/qwen3-1.7b-coreai-ios/tree/main/results).

## Public rebuild verification

The exact artifact uploaded in this repository was rebuilt under a neutral
path, installed on `massif iPhone` (iPhone 15 Pro), and rerun through the same
six-case business suite before publication. The app verified the public source
hash `5e885ec4...0aef73` before loading the AOT asset.

| Public-artifact check | Result |
| --- | ---: |
| Complete cases | 6 / 6 |
| Suite result | `COREAI_SUITE_PASS` |
| Fresh load | 46.176 s |
| Structured JSON TTFT | 1.213 s |
| Structured JSON visible decode | 19.516 tok/s |
| Long-context input | 3,790 tokens |
| Long-context TTFT | 5.709 s |
| Long-context visible decode | 16.748 tok/s |
| Reasoning output / reasoning tokens | 288 / 277 |
| Peak resident memory | 2,766.4 MiB |
| Resident memory after unload | 197.3 MiB |
| Full suite duration | 77.645 s |

The 3,790-token case recovered the frozen tail marker `TARGET-0377`. The fresh
load includes first-use device work and should not be compared directly with
the specialization-warmed load below.

## Physical-device results

The frozen mechanism completed four full six-case suites on iPhone 15 Pro with
zero empty responses, truncations, degenerate loops, context overflows,
crashes, OOMs, or assertion failures.

Final standalone suite:

| Case | Input tokens | Output / reasoning | TTFT | Visible decode |
| --- | ---: | ---: | ---: | ---: |
| Structured JSON | 160 | 60 / 1 | 0.564 s | 17.611 tok/s |
| Hot context turn | 241 | 5 / 1 | 0.428 s | 54.205 tok/s |
| Hot rewrite turn | 275 | 5 / 1 | 0.472 s | 49.455 tok/s |
| Injection boundary | 309 | 5 / 1 | 0.474 s | 54.596 tok/s |
| Long context | 3,790 | 10 / 1 | 5.627 s | 15.828 tok/s |
| Reasoning | 69 | 271 / 260 | 15.925 s | 21.370 tok/s |

Additional measurements:

- load after specialization warm-up: `11.312 s`;
- peak resident memory: `2,963.7 MiB`;
- resident memory after unload: `198.7 MiB`;
- product-runtime near-4K input: `3,194` tokens, `5.138 s` TTFT,
  `13.087 tok/s` visible decode;
- accepted runtime paths included cancellation, unload/reload, offline cold
  start, background recovery, memory-pressure cancellation, and
  delete/reconcile.

The detailed product implementation is intentionally not distributed.

## Download

```bash
hf download massif/Qwen3-1.7B-CoreAI-ANE-W8-4K-h16p \
  --local-dir Qwen3-1.7B-CoreAI-ANE-W8-4K-h16p
```

The downloaded repository root is a complete Core AI resource directory:

```text
Qwen3-1.7B-CoreAI-ANE-W8-4K-h16p/
├── metadata.json
├── tokenizer/
└── qwen3_1_7b_coreai_ane_w8_4k.h16p.aimodelc/
```

Pass that root directory to `CoreAILanguageModel(resourcesAt:)`:

```swift
import CoreAILanguageModels
import FoundationModels

let resources = URL(fileURLWithPath: "/path/to/Qwen3-1.7B-CoreAI-ANE-W8-4K-h16p")
let model = try await CoreAILanguageModel(
    resourcesAt: resources,
    mode: .eager,
    kvCacheStrategy: .fixedSize
)

let session = LanguageModelSession(model: model)
let response = try await session.respond(to: "Reply with exactly COREAI-OK. /no_think")
print(response.content)
```

For an app target, embed the entire downloaded directory as a folder reference
or deliver the same directory through your model-asset system. The
[companion app](https://github.com/massif-01/qwen3-1.7b-coreai-ios/tree/main/companion)
provides a minimal real-device integration.

## Compatibility and limitations

- Direct AOT compatibility reported by `coreai-build inspect`: A17 Pro,
  `h16p`, iOS 27.0 or later.
- This repository does not contain SafeTensors, GPTQ, GGUF, or MLX weights.
- The package is specialized for `h16p`; other Apple architectures require a
  separately compiled artifact.
- Preferred Neural Engine compute is a compilation preference, not proof that
  every operation executes exclusively on ANE.
- The model inherits the capabilities, limitations, and acceptable-use
  considerations of the Qwen3-1.7B base model.

## Provenance and integrity

- Base model: [`Qwen/Qwen3-1.7B`](https://huggingface.co/Qwen/Qwen3-1.7B)
- Hugging Face reference revision:
  `70d244cc86ccca08cf5af4e1e306ecf908b1ad5e`
- Apple `coreai-models` base:
  `04a3fd6cfe9bfae9cf05b1f246cf915d930d1c0a`
- Compiler: `coreai-build-3600.75.3`
- Public `.aimodel` source SHA-256:
  `5e885ec407f1b2690df5098d38b1bed4a3e66f4352c859fb2bb79666bc0aef73`
- Compiled `main-h16p.mlirb` SHA-256:
  `a7eefeef16708a324f9919890355eb92180ec85eef419ebd5822e8c8afd42f5f`

The public artifact was rebuilt under a neutral path and scanned recursively,
including binary containers, before upload. The reusable publication gate is
available in
[`scripts/audit-public-artifact.sh`](https://github.com/massif-01/qwen3-1.7b-coreai-ios/blob/main/scripts/audit-public-artifact.sh).

## License

The converted model is distributed under the base model's Apache License 2.0.
See `LICENSE` and `NOTICE.md`. The GitHub reproduction repository's original
code and documentation use BSD 3-Clause, with Apple-derived material retaining
Apple's BSD terms.