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MLX conversion of Google EmbeddingGemma 2 in **8-bit affine** format, retaining text, image, audio, and video encoders. The model produces normalized 768-dimensional embeddings. See the original model card for intended use, training information, evaluation, and limitations. Quantization mode: `affine`, 8 bits, group size 64. The standard MLX-VLM policy quantizes the text encoder and audio projection. The vision and audio towers and vision projection remain BF16. Weight storage: 1.234 GB (decimal). Source revision: `914f7f89142e33e77833254d9c9b90c3cef7303b`. Converted with MLX-VLM revision `3d87e884`, from branch `pc/embeddinggemma-2`, and MLX 0.32.3.

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MLX conversion of Google EmbeddingGemma 2 in **BF16** format, retaining text, image, audio, and video encoders. The model produces normalized 768-dimensional embeddings. See the original model card for intended use, training information, evaluation, and limitations. Weight storage: 1.489 GB (decimal). Source revision: `914f7f89142e33e77833254d9c9b90c3cef7303b`. Converted with MLX-VLM revision `3d87e884`, from branch `pc/embeddinggemma-2`, and MLX 0.32.3. Apply the appropriate task prefixes from `config_sentence_transformers.json`. Queries and documents must use the same embedding dimension. Keep non-quantized weights and activations in BF16; do not cast this model to float16.

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`mdbr-leaf-ir` is a compact high-performance text embedding model specifically designed for **information retrieval (IR)** tasks, e.g., the retrieval stage of Retrieval-Augmented Generation (RAG) pipelines. To enable even greater efficiency, `mdbr-leaf-ir` supports flexible asymmetric architectures and is robust to vector quantization and MRL truncation. If you are looking to perform other tasks such as classification, clustering, semantic sentence similarity, summarization, please check out our `mdbr-leaf-mt` model.

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1. Introduction 2. Technical Report 3. Highlights 4. Benchmarks 5. Quickstart 6. Citation `mdbr-leaf-mt` is a compact high-performance text embedding model designed for classification, clustering, semantic sentence similarity and summarization tasks. To enable even greater efficiency, `mdbr-leaf-mt` supports flexible asymmetric architectures and is robust to vector quantization and MRL truncation.

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