---
license: apache-2.0
base_model: google/gemma-4-E4B-it
tags:
- coreml
- apple-neural-engine
- decision-model
- d1a
library_name: coreml
pipeline_tag: text-classification
---
# D1A-E4B-it v0.6 · Core ML int4 (text, ≤ 512 tokens)
**A small decision model for Apple devices: give it a document and a few questions, and it picks the answers.** Each question
is yes/no, multiple choice or a score; D1A returns a calibrated probability for every option in one forward pass, with no
generated text, so every answer is a valid option.
This is the Core ML int4 conversion of [JohnP1/d1a-e4b-it](https://huggingface.co/JohnP1/d1a-e4b-it) v0.6. It is **text only,
for documents up to 512 tokens**, with int4 weights (per block of 32): 2.1 GB of chunks plus 2.3 GB of embedding tables. The
conversion is the one used for the E2B and E2B-it Core ML builds, unchanged.
## Status: read before use
- **Answers move at 4 bits.** Mac check on an Apple M1 Max: 28 real requests of up to 512 tokens (42 questions), against
the fp32 PyTorch rebuild of the 8-bit export it was converted from.
- **3 of 42 answers change (7.1%), all three on clear-cut questions** (where the reference's top answer leads by more than
0.05); the largest probability change is 0.455.
- For comparison, D1A-E2B-it's int4 Core ML build changes 0 of 42.
- **Speed** on the Mac's GPU (CPU_AND_GPU): p50 6.6 s, p95 9.2 s per request; 469 s to load (first, compiled).
- **Not verified on iPhone.** No int8 Core ML build is planned for E4B-it; for answers that must not move, use the MLX 8-bit build below.
- **Not included:** photo, voice and video (their encoders are separate models, not converted), and inputs longer than 512
tokens.
For decisions that matter, prefer the MLX 8-bit build [JohnP1/d1a-e4b-it-mlx-q8](https://huggingface.co/JohnP1/d1a-e4b-it-mlx-q8)
(1 of 202 demo answers and 3 of 276 labeler answers differ from the served model).
## ``: encode with it
The Gemma 4 E4B-it tokenizer adds no ``, but this model was trained with one at the start of every record. d1a from
jonpol01/d1a#261 on puts it back (bos_token_id 2; `d1a_config.json` records `leading_ids: [2]`). An app that encodes requests
itself must prepend id 2 before right-padding to 512.
## Run it
```bash
pip install coremltools numpy torch transformers safetensors "d1a @ git+https://github.com/jonpol01/d1a"
hf download JohnP1/d1a-e4b-it-coreml-4bit --revision v0.6 --local-dir d1a-e4b-it-coreml-4bit
python d1a-e4b-it-coreml-4bit/reference/run.py d1a-e4b-it-coreml-4bit request.json
```
`request.json` is a System One request, for example:
```json
{"state": "Shoes arrived two weeks late and in the wrong size. Also I see two charges on my card.",
"questions": {"team": {"type": "choice", "instructions": "Which team should handle this?", "criteria": {"returns": null, "shipping": null, "billing": null}},
"urgent": {"type": "noul", "instructions": "Does this need a reply today?"}}}
```
The first run compiles the chunks into `coreml/.compiled/` (several minutes); later runs load them.
## Files
| Path | What |
|---|---|
| `coreml/L512_-.mlpackage` | 7 chunks of the 42-layer text model, int4 weights (per block of 32) |
| `embeddings.safetensors` | the token and per-layer embedding tables (MLX affine-quantized, from JohnP1/d1a-e4b-it-mlx-q8@v0.6), read row by row on the host |
| `head.safetensors`, `d1a_config.json` | the pointer head, the temperature (1.55), the ``, pad and delimiter ids |
| `config.json`, `tokenizer.json`, `tokenizer_config.json` | the Gemma 4 E4B-it model config and tokenizer |
| `io.json` | each chunk's input names |
| `reference/` | Python reference: `run.py` (one request), `workers.py` (one process per chunk), `d1a_coreml.py` (host-side embeddings and masks), `pipeline.py` (all chunks in one process) |
macOS 15 / iOS 18 or later.
## License
Apache-2.0. Base model: Gemma 4 by Google (Apache-2.0). D1A is built on Kev by Jared Palmer (Apache-2.0).