Add model card: document base model mixedbread-ai/mxbai-edge-colbert-v0-32m

README.md

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+---
+base_model: mixedbread-ai/mxbai-edge-colbert-v0-32m
+license: apache-2.0
+library_name: coreml
+pipeline_tag: sentence-similarity
+tags:
+  - coreml
+  - colbert
+  - multi-vector
+  - late-interaction
+  - feature-extraction
+  - sentence-similarity
+  - apple
+  - on-device
+---
+
+# MXBAIEdgeColbert (Core ML)
+
+A [Core ML](https://developer.apple.com/documentation/coreml) export of
+[`mixedbread-ai/mxbai-edge-colbert-v0-32m`](https://huggingface.co/mixedbread-ai/mxbai-edge-colbert-v0-32m)
+for on-device ColBERT late-interaction (multi-vector) encoding on Apple platforms.
+
+## Base model
+
+- **Base model:** [`mixedbread-ai/mxbai-edge-colbert-v0-32m`](https://huggingface.co/mixedbread-ai/mxbai-edge-colbert-v0-32m)
+
+This repository contains only the compiled Core ML encoder (`MXBAIEdgeColbert.mlmodelc`).
+All weights are derived from the base model above; no additional training was performed.
+
+## What was exported
+
+The exported program wraps the base transformer plus its three ColBERT `Dense`
+projection layers (384 → 768 → 768 → 64, no bias / no activation), producing
+per-token embeddings that are L2-normalized and masked by the attention mask.
+
+| Property | Value |
+| --- | --- |
+| Inputs | `input_ids` and `attention_mask`, `int32`, static shape `(1, 256)` |
+| Output | `token_embeddings` (per-token, dim 64, L2-normalized, padding zeroed) |
+| Compute precision | `float16` |
+| Minimum deployment target | iOS 18 / macOS 15 |
+
+Tokenization and the ColBERT `[Q]` / `[D]` prefixing and skiplist masking are
+performed by the host application; this model performs encoding only.
+
+## Usage
+
+Intended for use as the encoder in an on-device ColBERT/PLAID retrieval
+pipeline. Load the compiled model with Core ML, feed padded `int32`
+`input_ids` / `attention_mask`, and read `token_embeddings`.
+
+## License
+
+Released under the Apache-2.0 license, following the base model.