---
license: apache-2.0
tags:
  - coreml
  - apple-silicon
  - embeddings
  - sentence-transformers
library_name: coremltools
base_model: MindscapeRAG/MiA-Emb-4B
pipeline_tag: feature-extraction
---

# MiA Emb 4B CoreML

MiA-Emb-4B converted to CoreML for Apple Silicon (M1/M2/M3/M4)

## Model Details

- **Format**: CoreML ML Program (`.mlpackage`)
- **Precision**: FP16
- **Input**: `input_ids` (1, 512), `attention_mask` (1, 512)
- **Output**: `embeddings` (1, 3584)
- **Target**: macOS 14+ / iOS 17+ / Apple Silicon

## Usage

```python
import coremltools as ct
import numpy as np

# Load model
model = ct.models.MLModel("coreml_fp16.mlpackage")

# Prepare inputs (use your tokenizer)
input_ids = np.zeros((1, 512), dtype=np.int32)
attention_mask = np.ones((1, 512), dtype=np.int32)

# Run inference
output = model.predict({"input_ids": input_ids, "attention_mask": attention_mask})
embeddings = output["embeddings"]  # Shape: (1, 3584)
```

## Performance

Benchmarked on Apple M4:
- **Inference**: ~80-100ms per embedding
- **Load time**: ~13s (first load, cached after)

## Conversion

Converted using coremltools 8.1 with custom op handlers for:
- `new_ones` (GitHub issue #2040)
- Bitwise ops (`and`, `or`) with int→bool casting

## License

Same license as base model: MindscapeRAG/MiA-Emb-4B