| --- |
| license: other |
| license_name: lfm-open-license-v1.0 |
| license_link: LICENSE |
| base_model: LiquidAI/d1-3B |
| pipeline_tag: zero-shot-classification |
| library_name: coreai |
| tags: |
| - liquid |
| - lfm2.5 |
| - decision |
| - classification |
| - system-one |
| - decision-model |
| - apple |
| - coreai |
| - ane |
| - apple-neural-engine |
| - fp16 |
| - tinkerbox |
| --- |
| |
| # d1-3B on Apple Neural Engine |
|
|
| Core AI conversion of [LiquidAI/d1-3B](https://huggingface.co/LiquidAI/d1-3B) for typed yes/no, choice, and score decisions over text and images, using [tinkerbox-ane](https://github.com/WeGoToMars/tinkerbox). |
|
|
| The language backbone and vision tower run fully on the ANE. Tokenization, image preprocessing, token-embedding lookup, the FP32 multimodal projector, and answer readout run on the CPU while your GPU stays idle and cool 😎❄️. |
|
|
| Requires macOS 27, Apple Silicon M4 or newer, and Python 3.12 or 3.13. |
|
|
| ## Usage |
|
|
| Install `tinkerbox-ane>=2.1.0`. Loading the model downloads only this Mac's bundle: its `checkpoint` folder carries the upstream code, tokenizer and configuration, and the token embeddings, multimodal projector and vision position table the CPU uses. |
|
|
| Evaluate independent questions together, sharing computation over the input: |
|
|
| ```python |
| from tinkerbox_ane import d1 |
| |
| with d1.load() as model: |
| result = model.system_one("I was charged twice. Please refund me today.", { |
| "refund": {"type": "noul", "instructions": "Is this a refund request?"}, |
| "urgent": {"type": "noul", "instructions": "Does this need an urgent response?"}, |
| }) |
| print(result) |
| ``` |
|
|
| Images can accompany text, or be used alone: |
|
|
| ```python |
| from PIL import Image |
| from tinkerbox_ane import d1 |
| |
| with d1.load() as model, Image.open("receipt.jpg") as image: |
| result = model.system_one(None, { |
| "receipt": {"type": "noul", "instructions": "Is this a receipt?"}, |
| }, images=[image]) |
| print(result) |
| ``` |
|
|
| Each result's `usage` reports its tokens, Neural Engine time, and counted FLOPs. |
|
|
| ## Supported architectures |
| | Architecure | SoC name | |
| | --- | --- | |
| | `h16g` | Apple M4 | |
| | `h16s` | Apple M4 Pro | |
| | `h16c` | Apple M4 Max | |
| | `h17g` | Apple M5 | |
| | `h17s` | Apple M5 Pro | |
| | `h17c` | Apple M5 Max | |
| | `h17d` | Apple M5 Ultra | |
| | `h18g` | Apple M6 | |
|
|
| ## Benchmarks |
|
|
| Measured October 19, 2026 on Apple M5 Pro (`h17s`, 24 GiB), macOS 27.0.1. |
|
|
| | Metric | Result | |
| |---|---:| |
| | Top-answer agreement with FP32 text reference | 100% (204/204) | |
| | Absolute probability drift, mean / maximum | 0.000665 / 0.009730 | |
| | One text question, 26–251 tokens: median / p95 | 39.3 / 79.9 ms | |
| | 4,096-token text prompt: median | 1.49 s | |
| | 32,768-token text prompt | 51.8 s | |
| | Two parallel questions over one short text: median / p95 | 39.2 / 39.7 ms | |
| | One image prompt: median / p95 | 216.8 / 220.1 ms | |
|
|
| Upstream checkpoint: `LiquidAI/d1-3B@051bcc464b01b9f92942b364d9586b0ef5912432`. Weights, upstream code, and converted assets retain the [LFM Open License v1.0](LICENSE). See [NOTICE](NOTICE). |
|
|
