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    AI Model Hub

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

    19,428 models available

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    Showing 193-216 of 19,428 models

    Automatic Speech Recognition

    gigant/romanian-wav2vec2

    1.7M
    20
    transformers
    View Details
    Image Classification

    Falconsai/nsfw_image_detection

    1.7M
    1,219
    transformers
    View Details
    Sentence Similarity

    Qdrant/bge-small-en-v1.5-onnx-Q

    1.6M
    14
    transformers
    View Details
    Text Generation

    distilbert/distilgpt2

    1.6M
    661
    transformers
    View Details
    Zero Shot Image Classification

    google/siglip2-base-patch16-224

    1.6M
    157
    transformers
    View Details
    Image Text To Text

    deepseek-ai/DeepSeek-V4.1-Flash

    1.6M
    4,742
    transformers
    View Details
    Image Text To Text

    Qwen/Qwen2.5-VL-3B-Instruct

    1.6M
    737
    transformers
    View Details
    Fill Mask

    google-bert/bert-base-multilingual-uncased

    1.6M
    173
    transformers
    View Details
    Text Generation

    nvidia/Qwen2.5-VL-7B-Instruct-NVFP4

    1.6M
    27
    Model Optimizer
    View Details
    Feature Extraction

    jinaai/jina-embeddings-v3

    1.6M
    1,172
    transformers
    View Details
    Text Generation

    nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16

    1.6M
    140
    transformers
    View Details
    Image Text To Text

    thinkingmachines/Inkling

    1.6M
    1,827
    transformers
    View Details
    Automatic Speech Recognition

    airesearch/wav2vec2-large-xlsr-53-th

    1.5M
    38
    transformers
    View Details
    Text Generation

    ornith-ai/Ornith-1.0-35B-GGUF

    1.5M
    1,064
    transformers
    View Details
    Image Text To Text

    unsloth/gemma-4-12b-it-GGUF

    1.5M
    931
    View Details
    Text To Speech

    pnnbao-ump/VieNeu-TTS-v3-Turbo

    1.5M
    90
    View Details
    Sentence Similarity

    sentence-transformers/multi-qa-mpnet-base-dot-v1

    1.5M
    206
    sentence-transformers
    View Details
    Automatic Speech Recognition

    comodoro/wav2vec2-xls-r-300m-cs-250

    1.5M
    12
    transformers
    View Details
    Text Generation

    deepseek-ai/DeepSeek-R1

    1.5M
    14,410
    transformers
    View Details
    Image Classification

    timm/resnet50.a1_in1k

    1.5M
    52
    timm
    View Details
    Automatic Speech Recognition

    anuragshas/wav2vec2-large-xlsr-53-telugu

    1.5M
    10
    transformers
    View Details
    Image Text To Text

    HuggingFaceTB/SmolVLM2-500M-Video-Instruct

    1.5M
    198
    transformers
    View Details
    Sentence Similarity

    sentence-transformers/distiluse-base-multilingual-cased-v1

    1.5M
    145
    sentence-transformers
    View Details
    Text Generation

    ukisai/Swift-1.5-Qwen3.8-27B-GSQ-RCO-GGUF

    1.5M
    432
    gguf
    View Details
    9 / 810

    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.

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