embeddinggemma-2-oQ6e

EmbeddingGemma 2 is an open multimodal embedding model built by Google DeepMind which maps text (incl. code), images, video, and audio inputs—and combinations thereof—into a single, unified 768-dimensional vector space. The model has 740M total parameters, combining a 270M parameter text model with modular vision (170M) and audio (300M) encoders. Designed to run on consumer hardware such as mobile devices and laptops, EmbeddingGemma 2 delivers low-latency semantic representations for on-device applications, like search, retrieval-augmented generation (RAG), classification, and clustering. EmbeddingGemma 2 builds upon the architectural and capability advancements of Gemma 4, offering several…

Model · Feature extraction

embeddinggemma-2-oQ8e

Jun Kim

This model was quantized using oQ (oMLX v0.7.1) mixed-precision quantization with Improved oQe Quantization (jundot/omlx#4385). A mixed-precision MLX checkpoint of google/embeddinggemma-2, Google DeepMind's multimodal embedding model that maps text, images, video and audio into one 768-dimensional vector space. It includes the text encoder, the vision encoder and the audio encoder. oQ quantizes the text encoder; the vision and audio encoders stay in BF16. oMLX serves text and image inputs through POST /v1/embeddings (jundot/omlx#4333) and does not load the audio encoder. The effective average is 13.258 bits/weight for the complete checkpoint. oQ8e allocates bits by measured layer…

Model · Feature extraction

embeddinggemma-2-oQ4e

Jun Kim

This model was quantized using oQ (oMLX v0.7.1) mixed-precision quantization with Improved oQe Quantization (jundot/omlx#4385). A mixed-precision MLX checkpoint of google/embeddinggemma-2, Google DeepMind's multimodal embedding model that maps text, images, video and audio into one 768-dimensional vector space. It includes the text encoder, the vision encoder and the audio encoder. oQ quantizes the text encoder; the vision and audio encoders stay in BF16. oMLX serves text and image inputs through POST /v1/embeddings (jundot/omlx#4333) and does not load the audio encoder. The effective average is 11.991 bits/weight for the complete checkpoint. oQ4e allocates bits by measured layer…

Model · Feature extraction

voxtral-mini-audio-extractor

Vxltxr Llc

A lightweight standalone audio feature extractor derived from Voxtral-Mini-3B-2507. This repository isolates the Whisper-based audio encoder and multi-modal projector from the original 3B language model. The extracted module is intended for offline audio preprocessing, dataset preparation, feature caching, and downstream multimodal training pipelines. The language-model decoder is not included. It does not contain the 3B LLaMA language-model decoder. For offline preprocessing, loading the complete multimodal language model is unnecessary when the only required output is the projected audio representation. This standalone checkpoint can therefore be used as a dedicated audio feature…

Model · Feature extraction

CoVisco-L-14

Ernie Research

CoVisco-L-14 is the Stage 1 vision encoder of the CoVisco pipeline (Codec-Native Vision Encoder with Native Token Compression for Efficient Unified Visual Understanding). It encodes images and videos in a unified way and is trained by SigLIP-style distillation against two teacher branches. This repository releases only the vision-encoder weights in the safe.safetensors format, together with the minimal model code and a self-contained loader. The model is a transformers.PreTrainedModel and is loadable via frompretrained (see below). - 4D RoPE (ropetheta=10000) for spatio-temporal positions - Three projection heads (each LayerNorm + Linear, output L2-normalized): - toimagecaption → 1536-d…

Model · Feature extraction

Qwen3-Embedding-0.6B

Qwen

The Qwen3 Embedding model series is the latest proprietary model of the Qwen family, specifically designed for text embedding and ranking tasks. Building upon the dense foundational models of the Qwen3 series, it provides a comprehensive range of text embeddings and reranking models in various sizes (0.6B, 4B, and 8B). This series inherits the exceptional multilingual capabilities, long-text understanding, and reasoning skills of its foundational model. The Qwen3 Embedding series represents significant advancements in multiple text embedding and ranking tasks, including text retrieval, code retrieval, text classification, text clustering, and bitext mining. Exceptional Versatility: The…