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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 97-120 of 17,271 models

    Fill Mask

    google-bert/bert-base-cased

    3.7M
    371
    transformers
    Automatic Speech Recognition

    jonatasgrosman/wav2vec2-large-xlsr-53-dutch

    3.7M
    17
    transformers
    Automatic Speech Recognition

    pyannote/voice-activity-detection

    3.6M
    241
    pyannote-audio
    Text Generation

    Qwen/Qwen3-4B-Instruct-2507

    3.5M
    953
    transformers
    Text Generation

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

    3.5M
    1,002
    transformers
    Text Classification

    BAAI/bge-reranker-base

    3.5M
    242
    sentence-transformers
    Automatic Speech Recognition

    Qwen/Qwen3-ASR-1.7B

    3.5M
    1,072
    Text Generation

    ornith-ai/Ornith-1.5-9B-GGUF

    3.5M
    312
    transformers
    Zero Shot Image Classification

    laion/CLIP-ViT-L-14-laion2B-s32B-b82K

    3.5M
    65
    open_clip
    Image Text To Text

    lmstudio-community/Qwen3.8-27B-MLX-8bit

    3.5M
    21
    transformers
    Text Generation

    Qwen/Qwen3-1.7B

    3.4M
    528
    transformers
    Text Generation

    EleutherAI/pythia-160m

    3.4M
    45
    transformers
    Image Text To Text

    lmstudio-community/Qwen3.8-27B-MLX-6bit

    3.4M
    12
    transformers
    Image Text To Text

    lmstudio-community/Qwen3.8-27B-MLX-5bit

    3.4M
    transformers
    Automatic Speech Recognition

    jonatasgrosman/wav2vec2-large-xlsr-53-greek

    3.3M
    4
    transformers
    Text Classification

    cardiffnlp/twitter-roberta-base-sentiment-latest

    3.3M
    828
    transformers
    Zero Shot Classification

    facebook/bart-large-mnli

    3.2M
    1,606
    transformers
    Any To Any

    google/gemma-4-E2B-it

    3.2M
    938
    transformers
    Image Text To Text

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

    3.2M
    693
    transformers
    Text Generation

    ornith-ai/Ornith-1.5-35B-A3B-GGUF

    3.2M
    385
    transformers
    Any To Any

    google/gemma-4-12B-it

    3.1M
    1,526
    transformers
    Automatic Speech Recognition

    openai/whisper-small

    3.0M
    590
    transformers
    Image Feature Extraction

    facebook/dinov2-base

    3.0M
    195
    transformers
    Image Text To Text

    Qwen/Qwen3-VL-2B-Instruct

    2.9M
    459
    transformers
    5 / 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.