---
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).