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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

    Showing 337-360 of 19,428 models

    Any To Any

    google/gemma-4-E4B-it-qat-q4_0-gguf

    992K
    169
    Image Text To Text

    ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-Coder-GGUF

    986K
    509
    gguf
    Text Generation

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

    986K
    475
    transformers
    Text Generation

    kindlingai/glm-5.3-exlr8-k2-k3-k4

    984K
    23
    trellis
    Image Text To Text

    bartowski/MiMo-V2.6-Distill-Qwen-9B-GGUF

    983K
    163
    Feature Extraction

    jinaai/jina-embeddings-v5-omni-small

    982K
    174
    transformers
    Fill Mask

    google-bert/bert-base-chinese

    977K
    1,493
    transformers
    Text Generation

    zai-org/GLM-4.5-Air

    975K
    647
    transformers
    Text Generation

    Qwen/Qwen3-30B-A3B

    974K
    973
    transformers
    Image Text To Text

    unsloth/Qwen3.5-35B-A3B-GGUF

    973K
    880
    Sentence Similarity

    intfloat/e5-base-v2

    973K
    171
    sentence-transformers
    Image Text To Text

    google/gemma-3-4b-it

    970K
    1,596
    transformers
    Text To Speech

    openbmb/VoxCPM2

    969K
    1,728
    voxcpm
    Text Generation

    prism-ml/Ternary-Bonsai-27B-mlx-2bit

    966K
    217
    mlx
    Text Generation

    tencent/Hy3

    966K
    987
    transformers
    Image Text To Text

    shawnw3i/Huihui-Qwen3.6-27B-abliterated-AWQ-MTP

    965K
    13
    transformers
    Text Generation

    PrimeIntellect/Qwen3-0.6B

    959K
    transformers
    Text To Speech

    SWivid/F5-TTS

    957K
    1,222
    f5-tts
    Text Generation

    prism-ml/Bonsai-27B-mlx-1bit

    956K
    283
    mlx
    Text Generation

    zai-org/GLM-4.7-Flash

    954K
    1,888
    transformers
    Image Text To Text

    RedHatAI/Qwen3.6-35B-A3B-NVFP4

    951K
    193
    transformers
    Sentence Similarity

    nomic-ai/nomic-embed-text-v2-moe

    950K
    512
    sentence-transformers
    Text Generation

    TinyLlama/TinyLlama-1.1B-Chat-v1.0

    949K
    1,885
    transformers
    Translation

    facebook/nllb-200-distilled-600M

    946K
    997
    transformers
    15 / 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.

    All models

    Every model page in one place, 442 in total.