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    AI Model Hub

    Browse AI models for multimodal decomposition and recomposition pipelines: plug any model into your extractors.

    18,837 models available

    Showing 1-24 of 18,837 models

    Featured Models

    Benchmarked
    HFVisual Embeddings

    openai/clip-vit-large-patch14

    Contrastive Language-Image Pre-Training for zero-shot visual understanding

    6.3M
    3 benchmarks
    HFVisual Embeddings

    google/siglip-base-patch16-224

    Sigmoid Loss for Language Image Pre-Training, efficient contrastive learning

    1.4M
    3 benchmarks
    HFVisual Embeddings

    google/siglip2-giant-opt-patch16-384

    Multilingual vision-language encoder with dense features and localization

    2.6M
    2 benchmarks
    HFVisual Embeddings

    facebook/dinov2-large

    Self-supervised vision foundation model producing all-purpose visual features

    799K
    2 benchmarks
    PyTorchVisual Embeddings

    facebook/dinov3-large

    Next-generation self-supervised vision model with Gram anchoring and 6.7B scaling

    450K
    1 benchmarks
    HFVisual Embeddings

    laion/CLIP-ViT-bigG-14-laion2B-39B-b160k

    Open-source CLIP trained on 2B image-text pairs at giant scale

    77K
    2 benchmarks
    Sentence Similarity

    sentence-transformers/all-MiniLM-L6-v2

    218.1M
    6,341
    sentence-transformers
    Feature Extraction

    BAAI/bge-small-en-v1.5

    59.4M
    643
    sentence-transformers
    Sentence Similarity

    sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2

    47.2M
    1,476
    sentence-transformers
    Fill Mask

    google-bert/bert-base-uncased

    34.4M
    3,431
    transformers
    Sentence Similarity

    BAAI/bge-m3

    32.5M
    3,893
    sentence-transformers
    Text Generation

    Qwen/Qwen3-0.6B

    30.5M
    1,815
    transformers
    Translation

    google-t5/t5-small

    23.8M
    683
    transformers
    Image Classification

    timm/mobilenetv3_small_100.lamb_in1k

    21.5M
    151
    timm
    Zero Shot Image Classification

    openai/clip-vit-base-patch32

    18.6M
    1,616
    transformers
    Sentence Similarity

    sentence-transformers/all-mpnet-base-v2

    18.1M
    1,395
    sentence-transformers
    Text Generation

    openai-community/gpt2

    16.0M
    4,254
    transformers
    Text Classification

    BAAI/bge-reranker-v2-m3

    15.9M
    1,258
    sentence-transformers
    Automatic Speech Recognition

    jonatasgrosman/wav2vec2-large-xlsr-53-japanese

    15.6M
    107
    transformers
    Sentence Similarity

    nomic-ai/nomic-embed-text-v1.5

    13.1M
    977
    sentence-transformers
    Image Classification

    google/vit-base-patch16-224

    12.8M
    1,037
    transformers
    Text Generation

    Qwen/Qwen3-4B

    12.6M
    759
    transformers
    Object Detection

    mudler/locate-anything.cpp-gguf

    12.3M
    52
    gguf
    Automatic Speech Recognition

    argmaxinc/whisperkit-coreml

    11.9M
    249
    whisperkit
    Image Text To Text

    google/gemma-4-26B-A4B-it

    11.3M
    1,651
    transformers
    Fill Mask

    FacebookAI/xlm-roberta-base

    10.8M
    958
    transformers
    Text To Speech

    hexgrad/Kokoro-82M

    10.5M
    7,252
    Feature Extraction

    BAAI/bge-large-en-v1.5

    10.1M
    773
    sentence-transformers
    Sentence Similarity

    intfloat/multilingual-e5-small

    9.8M
    464
    sentence-transformers
    Text Generation

    Qwen/Qwen3-8B

    9.7M
    2,118
    transformers
    1 / 785

    Choosing a model for multimodal retrieval

    Which of these models does Mixpeek actually run?

    Nine, and they are not the same thing as the catalog. The managed extractors run intfloat/multilingual-e5-large-instruct for text (1024-d), google/siglip-base-patch16-224 for images (768-d), google/vertex-multimodal (1408-d) and google/gemini-embedding-2 (3072-d) for unified multimodal, insightface ArcFace for faces (512-d), CLAP for audio fingerprints (512-d), facebook/dinov2-base for visual similarity and jinaai/jina-embeddings-v2-base-code for code (both 768-d, inside the web scraper). The rerank retriever stage runs BAAI/bge-reranker-v2-m3. Everything else in this catalog is documented here, not hosted here.

    Can I run any model from this catalog on Mixpeek?

    Not by naming it. No extractor takes a Hugging Face model id as a parameter, so there is no field to put one in. Three paths do work. Use a managed extractor and get the model it runs. Run the model on your own hardware and upsert the vectors through POST /v1/namespaces/{namespace_id}/documents/upsert, which stores them beside everything else. Or on Enterprise, upload the weights through POST /v1/namespaces/{namespace_id}/models, which accepts the huggingface format, and load them from a custom plugin.

    Should I use a text embedding model or a multimodal one?

    Ask whether a text query has to reach a non-text asset directly. If your video is searchable through its transcript and your images through their captions, a text model over that generated text is cheaper and usually more accurate, because retrieval quality on words is a solved problem and cross-modal alignment is not. If the query is 'find the shot that looks like this' or the visual content carries meaning no caption records, you need a shared space and a multimodal model. Most production systems run both indexes rather than choosing.

    What does the embedding dimension cost to store?

    A float32 vector is 4 bytes per dimension, so a million items costs 4 GB at 1024 dimensions, 3 GB at 768, and 12 GB at 3072. That is before any quantization and before payload. Models trained with Matryoshka representation learning, such as Gemini Embedding 2, let you truncate to a shorter prefix without re-encoding, so the width becomes an index-time decision rather than a model-selection one. Dimensions are fixed at namespace creation in Mixpeek, so changing a width later means re-indexing.

    Why does the download count on a model page differ from HuggingFace?

    It is the monthly figure from the HuggingFace API at the time of the last sync, not a live read, so it lags. The sync date is on each model page. Download count is a popularity signal and a poor quality signal: the most-downloaded model in a category is frequently an older checkpoint that a tutorial pinned years ago.

    All models

    Every model page in one place, 430 in total.