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A local iPhone app can compare candidate answers to “Does he like me?”—but its scores cannot reveal what another person feels. In an implementation account, yang9527, maker of 他爱我吗, describes converting the multilingual Laya model to Core ML and running it on-device. The result is a useful deployment experiment, not evidence of mind-reading, relationship accuracy, or proven iPhone performance. The maker also discloses that the article was prepared with AI assistance from implementation notes.
What the iPhone experiment does
Rather than generate an open-ended conversation, the app asks the model to compare choices supplied by the user. For a relationship question, those choices might be “he likes me,” “he doesn’t like me,” and “it’s unclear.” The user provides a question and context, then receives relative scores for the candidate answers and a short, template-based conclusion or suggestion.
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The maker describes this flow: Chinese text or speech goes into the app; speech is transcribed locally when needed; the question, context, and answer options are passed to Laya through Core ML; then the app presents comparative scores and a templated response. Chinese speech transcription uses SenseVoice locally, and the maker says history stays on the device. The described app does not call the Jev API or run a Jev-compatible server. These are implementation details reported by the app’s maker, not independently verified behavior.
What a score means—and what it cannot tell you
As the maker puts it, “A displayed answer score is a relative preference among the options given to the model.” It indicates how the model ranks the choices provided in that particular prompt. It is not a probability that he is in love, or a measurement of his actual feelings.
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More context can help frame the comparison, but it cannot give a model access to someone else’s thoughts. As the maker also cautions, “A model cannot see the other person’s thoughts, and an attractive percentage does not change that.” Treat any displayed number as an output of a comparison, not psychological certainty. [Maker’s implementation account on DEV Community]
What the deployment documentation says about size and limits
The system-one-foundation-models mobile guide describes an iOS Core ML deployment path for the 322-million-parameter laya-multilingual checkpoint. Its estimated model sizes vary with precision:
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| Format in the mobile guide | Estimated model size |
|---|---|
| FP32 | About 1.28 GB |
| FP16 | About 644 MB |
| 8-bit quantized Core ML | About 160 MB |
These are guide estimates, not measurements of the app’s installed size or memory use. The guide reports a Brier-score impact below 0.005 for its quantization conversion; that figure describes the guide’s conversion, not relationship-judgment quality or a device test. It recommends on-demand resource delivery as one way to manage download size and warns that keeping a roughly 160 MB model resident may add memory pressure on older devices with 4 GB of RAM. This is implementation guidance, not a compatibility guarantee for this app. [System One mobile deployment guide]
Input limits can also depend on the specific bundle. A local deployment guide updated September 25, 2026, says one Neural Engine Core ML bundle has a total input limit of 96 tokens, while an alternate multilingual bundle supports 1,024 tokens. The limit matters because the question, context, and candidate answers all compete for the available input. The guide also describes other local deployment routes, including Python, MLX, Node.js/ONNX, CPU-oriented runtimes, and a local server; their existence does not establish which route, if any, suits a particular iPhone app. [Laya local deployment guide]
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Why other Laya benchmark numbers do not validate this app
The Laya project repository reports results on its own typed-decisions benchmark: base checkpoints scored 0.36 and 0.35 against a 0.318 random baseline, while a fine-tuned checkpoint scored 0.766 on the same 2,000 decisions. Those are project-reported results for a separate benchmark. They do not demonstrate that the iPhone app can correctly judge relationships, nor do they establish general model accuracy. The repository recommends held-out evaluation for calibration claims. [Laya project repository]
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What remains unmeasured
The maker’s account reports no app-specific latency, battery, accuracy, or responsiveness benchmarks. The account instead identifies useful questions for a reproducible evaluation: whether reversing the answer-option order changes the leading choice; whether a neutral paraphrase changes the result; whether contradictory details increase uncertainty; and whether reassuring or negative wording dominates otherwise equivalent context.
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A device evaluation would also need to check whether the interface remains responsive while the model loads and speech transcription runs. Without such measurements, the implementation description does not establish how quickly the app responds, how much battery it uses, or how it behaves across iPhone models. A documented test set and repeatable conditions would be more informative than an unsupported accuracy percentage.
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Quick Recap
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- 8GB RAM, Apple A18 6-core CPU (2 performance + 4 efficiency cores), Apple GPU 4-core, 16‑core Neural Engine
- Rear camera: 48MP, f/1.6, wide, Front Camera: 12MP, f/1.9, wide, iOS 18.3.1, upgradable to iOS 18.5
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- Unlocked for freedom to choose your carrier. Compatible with both GSM & CDMA networks. The phone is unlocked to work with all GSM Carriers & CDMA Carriers Including AT&T, T-Mobile, Verizon, Straight Talk., Etc.
How to read the experiment
- As an implementation example: it shows how a maker describes combining a Core ML model, local speech transcription, and a constrained answer-comparison interface.
- As a relationship tool: its output is a relative preference among user-supplied options, not knowledge of another person’s feelings.
- As a performance claim: it offers no app-specific measurements of speed, battery consumption, or accuracy, and the deployment guides are not an iPhone compatibility test.
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