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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 337-360 of 17,271 models

    Sentence Similarity

    intfloat/e5-base-v2

    1.1M
    157
    sentence-transformers
    Image Text To Text

    raxcore-dev/Rax-4.5

    1.1M
    5
    transformers
    Image Text To Text

    datalab-to/surya-ocr-2-gguf

    1.1M
    19
    transformers
    Text Generation

    ibm-research/PowerMoE-3b

    1.1M
    22
    transformers
    Feature Extraction

    BAAI/bge-large-zh-v1.5

    1.1M
    646
    sentence-transformers
    Text Generation

    Qwen/Qwen3-4B-Instruct-2507-FP8

    1.1M
    82
    transformers
    Sentence Similarity

    sentence-transformers/distiluse-base-multilingual-cased-v2

    1.1M
    209
    sentence-transformers
    Text Generation

    nvidia/Qwen3.5-122B-A10B-NVFP4

    1.1M
    52
    Model Optimizer
    Text Generation

    nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16

    1.1M
    423
    transformers
    Sentence Similarity

    sentence-transformers/paraphrase-MiniLM-L3-v2

    1.1M
    30
    sentence-transformers
    Image Text To Text

    google/diffusiongemma-26B-A4B-it

    1.1M
    1,204
    transformers
    Text Classification

    Xenova/ms-marco-MiniLM-L-6-v2

    1.1M
    9
    transformers.js
    Image Text To Text

    google/medgemma-4b-it

    1.1M
    1,044
    transformers
    Translation

    Helsinki-NLP/opus-mt-nl-en

    1.1M
    10
    transformers
    Image Text To Text

    QuantTrio/Qwen3.5-9B-AWQ

    1.1M
    26
    transformers
    Text To Speech

    Qwen/Qwen3-TTS-12Hz-0.6B-CustomVoice

    1.1M
    182
    Text Generation

    openai-community/gpt2-large

    1.1M
    358
    transformers
    Text To Speech

    onnx-community/Kokoro-82M-v1.0-ONNX

    1.1M
    256
    transformers.js
    Image Classification

    timm/resnet50.ram_in1k

    1.1M
    timm
    Text Generation

    Qwen/Qwen2.5-1.5B

    1.1M
    213
    transformers
    Automatic Speech Recognition

    Yehor/w2v-xls-r-uk

    1.1M
    8
    transformers
    Text Generation

    allenai/OLMo-2-0425-1B

    1.1M
    81
    transformers
    Sentence Similarity

    thenlper/gte-small

    1.1M
    190
    sentence-transformers
    Automatic Speech Recognition

    saattrupdan/wav2vec2-xls-r-300m-ftspeech

    1.1M
    transformers
    15 / 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.