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    AI Model Hub

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

    17,271 models available

    Showing 313-336 of 17,271 models

    Image Segmentation

    CIDAS/clipseg-rd64-refined

    1.2M
    141
    transformers
    Automatic Speech Recognition

    classla/wav2vec2-xls-r-parlaspeech-hr

    1.2M
    3
    transformers
    Text Generation

    MiniMaxAI/MiniMax-M2.7

    1.2M
    1,246
    transformers
    Text To Image

    Tongyi-MAI/Z-Image-Turbo

    1.2M
    4,506
    diffusers
    Image Text To Text

    ggml-org/Qwen3.8-27B-GGUF

    1.2M
    73
    Any To Any

    google/gemma-4-12B-it-qat-w4a16-ct

    1.2M
    56
    transformers
    Text Generation

    nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4

    1.2M
    393
    transformers
    Text Generation

    Qwen/Qwen3-30B-A3B-Instruct-2507

    1.2M
    833
    transformers
    Automatic Speech Recognition

    NbAiLab/nb-wav2vec2-1b-bokmaal-v2

    1.2M
    transformers
    Any To Any

    lmstudio-community/gemma-4-E4B-it-MLX-4bit

    1.2M
    24
    transformers
    Text Generation

    sshleifer/tiny-gpt2

    1.2M
    36
    transformers
    Automatic Speech Recognition

    gagan3012/wav2vec2-xlsr-nepali

    1.2M
    8
    transformers
    Image Feature Extraction

    timm/vit_small_patch14_dinov2.lvd142m

    1.2M
    8
    timm
    Object Detection

    microsoft/table-transformer-structure-recognition

    1.2M
    227
    transformers
    Any To Any

    lmstudio-community/gemma-4-E4B-it-MLX-8bit

    1.2M
    10
    transformers
    Sentence Similarity

    Qdrant/all-MiniLM-L6-v2-onnx

    1.2M
    7
    transformers
    Feature Extraction

    WhereIsAI/UAE-Large-V1

    1.1M
    237
    sentence-transformers
    Any To Any

    lmstudio-community/gemma-4-E4B-it-MLX-5bit

    1.1M
    transformers
    Automatic Speech Recognition

    Systran/faster-whisper-large-v3

    1.1M
    652
    ctranslate2
    Any To Any

    lmstudio-community/gemma-4-E4B-it-MLX-6bit

    1.1M
    3
    transformers
    Text Classification

    cardiffnlp/twitter-xlm-roberta-base-sentiment

    1.1M
    275
    transformers
    Sentence Similarity

    Qwen/Qwen3-VL-Embedding-8B

    1.1M
    476
    sentence-transformers
    Feature Extraction

    microsoft/wavlm-large

    1.1M
    114
    transformers
    Text To Speech

    k2-fsa/OmniVoice

    1.1M
    1,347
    omnivoice
    14 / 720

    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, 375 in total.