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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 289-312 of 17,271 models

    Text Generation

    microsoft/phi-2

    1.4M
    3,502
    transformers
    Text Generation

    HuggingFaceTB/SmolLM2-135M-Instruct

    1.4M
    413
    transformers
    Text Generation

    farbodtavakkoli/OTel-LLM-27B-IT

    1.3M
    Tabular Regression

    autogluon/mitra-regressor

    1.3M
    32
    Image Text To Text

    sahilchachra/Unlimited-OCR-AWQ

    1.3M
    2
    transformers
    Automatic Speech Recognition

    facebook/wav2vec2-base-960h

    1.3M
    402
    transformers
    Image Text To Text

    gaunernst/gemma-3-27b-it-int4-awq

    1.3M
    40
    transformers
    Image Classification

    microsoft/resnet-50

    1.3M
    506
    transformers
    Text Generation

    zai-org/GLM-5.2-FP8

    1.3M
    265
    transformers
    Any To Any

    unsloth/gemma-4-12B-it-qat-GGUF

    1.3M
    472
    transformers
    Summarization

    facebook/bart-large-cnn

    1.3M
    1,612
    transformers
    Automatic Speech Recognition

    nvidia/parakeet-ctc-1.1b

    1.3M
    58
    nemo
    Image Text To Text

    datalab-to/surya-ocr-2

    1.3M
    102
    transformers
    Fill Mask

    facebook/esm2_t33_650M_UR50D

    1.3M
    88
    transformers
    Image Feature Extraction

    google/vit-base-patch16-224-in21k

    1.3M
    416
    transformers
    Image Text To Text

    cyankiwi/Qwen3.8-27B-AWQ-INT4

    1.3M
    89
    transformers
    Text Generation

    meta-llama/Llama-3.2-1B

    1.3M
    2,569
    transformers
    Sentence Similarity

    Alibaba-NLP/gte-multilingual-base

    1.3M
    374
    sentence-transformers
    Text Generation

    google/gemma-3-270m

    1.3M
    1,086
    transformers
    Text Generation

    mistralai/Mistral-7B-Instruct-v0.2

    1.3M
    3,207
    transformers
    Image Text To Text

    Qwen/Qwen2-VL-7B-Instruct

    1.3M
    1,285
    transformers
    Image Text To Text

    unsloth/Qwen3.6-35B-A3B-GGUF

    1.3M
    1,588
    transformers
    Image Text To Text

    DavidAU/Qwen3.6-27B-Fable-Fusion-711-Uncensored-Heretic-NM-DAU-NEO-MAX-MTP-GGUF

    1.3M
    2,314
    Image Text To Text

    unsloth/Qwen3.6-27B-GGUF

    1.2M
    950
    transformers
    13 / 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.