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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 361-384 of 19,428 models

    Text Generation

    deepreinforce-ai/Ornith-1.0-35B-FP8

    945K
    79
    transformers
    Text Generation

    bartowski/Qwen2.5-32B-Instruct-GGUF

    943K
    91
    Image Text To Text

    HauhauCS/Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive

    943K
    4,018
    Text Generation

    RedHatAI/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-FP8

    942K
    13
    transformers
    Automatic Speech Recognition

    NbAiLab/nb-wav2vec2-1b-nynorsk

    941K
    3
    transformers
    Text Generation

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

    938K
    26
    transformers
    Image Text To Text

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

    936K
    124
    transformers
    Text Generation

    deepseek-ai/DeepSeek-V4-Flash

    936K
    2,348
    transformers
    Sentence Similarity

    sentence-transformers-testing/stsb-bert-tiny-safetensors

    935K
    13
    sentence-transformers
    Feature Extraction

    laion/clap-htsat-unfused

    933K
    95
    transformers
    Sentence Similarity

    jhgan/ko-sroberta-multitask

    931K
    154
    sentence-transformers
    Fill Mask

    almanach/camembert-base

    930K
    115
    transformers
    Object Detection

    PaddlePaddle/PP-DocLayoutV3_safetensors

    929K
    55
    transformers
    Image Text To Text

    LuffyTheFox/Qwen3.6-35B-A3B-Uncensored-Genesis-Hermes-V13-GGUF

    925K
    597
    hermes
    Fill Mask

    albert/albert-base-v2

    925K
    154
    transformers
    Sentence Similarity

    intfloat/e5-base

    924K
    35
    sentence-transformers
    Automatic Speech Recognition

    softcatala/wav2vec2-large-xlsr-catala

    918K
    3
    transformers
    Text Generation

    nvidia/Gemma-4-31B-IT-NVFP4

    915K
    586
    Model Optimizer
    Automatic Speech Recognition

    comodoro/wav2vec2-xls-r-300m-sk-cv8

    914K
    2
    transformers
    Sentence Similarity

    shibing624/text2vec-base-chinese

    912K
    812
    sentence-transformers
    Automatic Speech Recognition

    arijitx/wav2vec2-xls-r-300m-bengali

    909K
    13
    transformers
    Feature Extraction

    unslothai/1

    909K
    1
    transformers
    Image Text To Text

    Jackrong/Qwopus3.8-27B-Flash-V2-GGUF

    907K
    70
    transformers
    Sentence Similarity

    Qdrant/bm25

    904K
    53
    transformers
    16 / 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.