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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

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    Showing 217-240 of 17,271 models

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    google-bert/bert-base-multilingual-cased

    1.8M
    603
    transformers
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    Text To Speech

    ResembleAI/chatterbox

    1.8M
    1,780
    chatterbox
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    Image Text To Text

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

    1.8M
    116
    transformers
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    Text Generation

    trl-internal-testing/tiny-Qwen3ForCausalLM

    1.8M
    1
    transformers
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    Automatic Speech Recognition

    mlx-community/parakeet-tdt-0.6b-v3

    1.8M
    54
    mlx
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    Text Generation

    deepseek-ai/DeepSeek-V4-Flash

    1.8M
    2,187
    transformers
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    Image Classification

    timm/resnet18.a3_in1k

    1.8M
    timm
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    Automatic Speech Recognition

    mlx-community/parakeet-tdt-0.6b-v2

    1.7M
    47
    mlx
    View Details
    Text Generation

    Qwen/Qwen3-14B

    1.7M
    460
    transformers
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    Zero Shot Image Classification

    google/siglip-base-patch16-224

    1.7M
    90
    transformers
    View Details
    Image Text To Text

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

    1.7M
    65
    transformers
    View Details
    Automatic Speech Recognition

    Harveenchadha/vakyansh-wav2vec2-tamil-tam-250

    1.7M
    4
    transformers
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    Text Generation

    Qwen/Qwen3-Coder-Next-FP8

    1.7M
    178
    transformers
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    Automatic Speech Recognition

    openai/whisper-base

    1.7M
    287
    transformers
    View Details
    Zero Shot Image Classification

    openai/clip-vit-base-patch16

    1.7M
    166
    transformers
    View Details
    Text Generation

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

    1.7M
    2,126
    transformers
    View Details
    Sentence Similarity

    sentence-transformers/multi-qa-mpnet-base-dot-v1

    1.7M
    194
    sentence-transformers
    View Details
    Text Generation

    Qwen/Qwen2.5-0.5B

    1.7M
    443
    transformers
    View Details
    Image Text To Text

    Qwen/Qwen3.5-122B-A10B-FP8

    1.7M
    115
    transformers
    View Details
    Automatic Speech Recognition

    airesearch/wav2vec2-large-xlsr-53-th

    1.7M
    28
    transformers
    View Details
    Image Classification

    timm/resnet18.a1_in1k

    1.7M
    14
    timm
    View Details
    Text Generation

    nvidia/Gemma-4-26B-A4B-NVFP4

    1.7M
    136
    Model Optimizer
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    Text Generation

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

    1.7M
    1,772
    transformers
    View Details
    Image Text To Text

    vikhyatk/moondream2

    1.7M
    1,435
    transformers
    View Details
    10 / 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.

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