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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 313-336 of 19,428 models

    Translation

    Helsinki-NLP/opus-mt-nl-en

    1.1M
    18
    transformers
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    Text Generation

    openbmb/MiniCPM5-2B-DSpark

    1.1M
    54
    transformers
    View Details
    Feature Extraction

    WhereIsAI/UAE-Large-V1

    1.1M
    239
    sentence-transformers
    View Details
    Zero Shot Image Classification

    patrickjohncyh/fashion-clip

    1.1M
    303
    transformers
    View Details
    Image Text To Text

    LuffyTheFox/Qwen3.6-35B-A3B-Uncensored-Genesis-Hermes-V9-GGUF

    1.0M
    542
    hermes
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    Fill Mask

    microsoft/deberta-v3-large

    1.0M
    308
    transformers
    View Details
    Text Generation

    mlx-community/Llama-3.1-8B-Instruct-4bit

    1.0M
    4
    mlx
    View Details
    Sentence Similarity

    thenlper/gte-small

    1.0M
    200
    sentence-transformers
    View Details
    Text Generation

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

    1.0M
    438
    transformers
    View Details
    Text Generation

    MiniMaxAI/MiniMax-M2.5

    1.0M
    1,526
    transformers
    View Details
    Text Classification

    autotrust/JEV-35B

    1.0M
    676
    transformers
    View Details
    Image Text To Text

    orcarouter/Qwen3.8-Flash-Next-Uncensored-GGUF

    1.0M
    1,125
    gguf
    View Details
    Image Text To Text

    thinkingmachines/Inkling-Small

    1.0M
    429
    transformers
    View Details
    Sentence Similarity

    Alibaba-NLP/gte-multilingual-base

    1.0M
    390
    sentence-transformers
    View Details
    Image Text To Text

    unsloth/Qwen3.8-Flash-Next-GGUF

    1.0M
    1,267
    View Details
    Text Generation

    HuggingFaceTB/SmolLM2-135M-Instruct

    1.0M
    462
    transformers
    View Details
    Image Text To Text

    unsloth/gemma-4-26B-A4B-it-GGUF

    1.0M
    1,249
    View Details
    Automatic Speech Recognition

    Yehor/w2v-xls-r-uk

    1.0M
    10
    transformers
    View Details
    Image Text To Text

    LuffyTheFox/Qwen3.6-35B-A3B-Uncensored-Genesis-Hermes-V11-GGUF

    1.0M
    579
    hermes
    View Details
    Audio To Audio

    nvidia/bigvgan_v2_44khz_128band_512x

    1.0M
    84
    PyTorch
    View Details
    Text Generation

    legraphista/glm-4-9b-chat-IMat-GGUF

    1.0M
    5
    gguf
    View Details
    Text Generation

    unsloth/Llama-3.2-1B-Instruct

    1.0M
    108
    transformers
    View Details
    Image Text To Text

    RadixArk/Qwen3.8-27B-NVFP4

    1000K
    119
    Model Optimizer
    View Details
    Text Generation

    Qwen/Qwen2.5-Coder-32B-Instruct-AWQ

    999K
    49
    transformers
    View Details
    14 / 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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