Open Embedding and Reranker Models
Embedding and reranker models for semantic search and RAG, such as BGE, Qwen3-Embedding and Nomic Embed.
A family of AI models for self-hosted agents, with open weights and training data, supporting Ollama and llama.cpp on NVIDIA GPUs.
2.3KUpdated 1 week agoApache-2.0
Linux#GGUF#Guardrails#Hugging Face integration
Nemotron is NVIDIA's family of AI models for developers building agents that reason, write code and use tools. Teams can evaluate and adapt the models using published weights, training data and training recipes.
Datasets and Synthetic DataEmbedding and Reranker ModelsLLM Fine-TuningOpen-Weight LLMsVision-Language Models+0
An open-source embedding model supports text, code and media search locally and offline on consumer CPUs or GPUs under Apache 2.0.
5.8KUpdated 1 month agoApache-2.0
#Hugging Face integration#Multilingual#Quantization
EmbeddingGemma is a local AI embedding model for developers adding search and retrieval to applications. It represents text, images, audio and video in a shared space so apps can find related content across media types.
Embedding and Reranker Models+0
Text embedding models support self-hosted multilingual and code search, with custom vector sizes and CPU or NVIDIA GPU inference.
2.1KUpdated 1 year ago
Docker#Batch processing#Hugging Face integration#Multilingual
Qwen3-Embedding is a family of models for developers building search and text analysis on their own hardware or servers. It represents text as numerical vectors so applications can compare passages by meaning.
Embedding and Reranker Models+0
Text encoder models for local search and classification support long inputs and Hugging Face Transformers, with task-specific fine-tuning required.
1.8KUpdated 8 months agoApache-2.0
#Hugging Face integration
ModernBERT is a family of text encoder models for developers building search and classification systems on their own hardware. The base checkpoints predict masked words; retrieval, classification and question answering require task-specific fine-tuning.
Embedding and Reranker Models+0
A local AI document retrieval library searches page images without separate OCR and includes training code; its engine package is deprecated.
2.9KUpdated 3 months agoMIT
macOS#Batch processing#Hugging Face integration#LoRA
ColPali is a document retrieval library for developers and researchers building search or retrieval-augmented generation systems. It matches text queries to page images, using visual content alongside text. The colpali-engine package is deprecated, and maintainers recommend Sentence Transformers for new projects and production use.
Embedding and Reranker ModelsInference Libraries and BindingsRAG and Vector Search+0
A Python library runs and trains CLIP image-text models locally, with local checkpoint loading, Hugging Face weights and multi-GPU training.
14.3KUpdated 4 days ago
#Hugging Face integration#Multimodal input
OpenCLIP is a Python library for developers and researchers building image-text matching and zero-shot classification applications. It supports pretrained CLIP models and training models on your own hardware.
Embedding and Reranker ModelsInference Libraries and BindingsTraining FrameworksVision-Language Models+0
An open-source AI model family you can run locally through Ollama or LM Studio, with language, vision and speech models under Apache 2.0.
44Updated 2 months agoApache-2.0
Linux#Guardrails#Hugging Face integration#LM Studio integration
Granite is IBM's family of AI models for developers and businesses building applications on their own hardware or servers. You can customize the models for enterprise tasks, including coding assistants and document analysis.
Coding ModelsEmbedding and Reranker ModelsOpen-Weight LLMsSpeech-to-Text ModelsVision-Language Models+0
A local text embedding model turns queries and documents into vectors for semantic search, with Apache 2.0 weights and adjustable embedding dimensions.
mixedbread.comEmbedding and Reranker Models
#Hugging Face integration
mxbai-embed is Mixedbread's text embedding model family for developers building semantic search and retrieval systems. The downloadable mxbai-embed-large-v1 model converts queries and documents into vectors that applications can compare by meaning.
Embedding and Reranker Models+0
A Python toolkit runs BGE embedding models and multilingual rerankers for semantic search and RAG, with model fine-tuning under an MIT license.
12.3KUpdated 3 weeks agoMIT
#Multilingual#Multimodal input#Semantic search
FlagEmbedding is a Python toolkit for developers building semantic search and retrieval-augmented generation (RAG). It runs embedding models to find relevant text and rerankers to refine the order of retrieved results.
Embedding and Reranker ModelsFine-Tuning and TrainingInference Libraries and BindingsLLM Evaluation and TestingRAG and Vector Search+0
A local text embedding model for semantic search and RAG, with adjustable vector sizes and Apache 2.0 licensing.
huggingface.coEmbedding and Reranker Models
Docker#Batch processing#Hugging Face integration#ONNX
Nomic Embed Text v1.5 is an English text embedding model for developers building search and RAG applications. It represents text numerically so applications can find passages with similar meanings.
Embedding and Reranker Models+0
A text embedding model family for local search and retrieval, with English, multilingual and instruction-based checkpoints on Hugging Face.
22.2KUpdated 2 months agoMIT
#Hugging Face integration#Multilingual
E5 Embeddings is a family of text embedding models for developers building search and retrieval systems. It represents text as numerical vectors so applications can match queries with relevant passages on their own hardware.
Embedding and Reranker Models+0
An open-weight local LLM family for phones, PCs and servers, with Ollama and LM Studio support and a JAX library for fine-tuning.
5.8KUpdated 1 month agoApache-2.0
Android#LM Studio integration#LoRA#Multilingual
Gemma is Google DeepMind's family of open-weight AI models for developers building applications on their own hardware. It includes compact models for mobile and IoT devices and larger models for reasoning on personal computers and servers.
Edge and Embedded AIEmbedding and Reranker ModelsLLM Fine-TuningOpen-Weight LLMsVision-Language Models+0
An embedding model family supports semantic search and RAG with multilingual retrieval and reranking through the MIT-licensed FlagEmbedding toolkit.
12.3KUpdated 3 weeks agoMIT
#Multilingual#Semantic search
BGE Embeddings is a family of models for developers who need to find relevant passages for search or retrieval-augmented generation (RAG). It includes embedding models for finding candidate text and rerankers for assessing how closely those results match a query.
Embedding and Reranker ModelsFine-Tuning and TrainingInference Libraries and BindingsRAG and Vector Search+0
A local text embedding library that runs on CPU, distills sentence transformers without a dataset, and integrates with LangChain.
2.3KUpdated 4 days agoMIT
#Hugging Face integration#Multilingual
Model2Vec is a Python package for developers who need text embeddings for search or classification on their own hardware. It turns sentence transformers into smaller static embedding models, trading a small drop in performance for lower model size and faster CPU inference.
Embedding and Reranker ModelsFine-Tuning and TrainingInference Libraries and Bindings+0
An open-source image classification model that matches pictures to text labels without task-specific training and runs locally on CPU or CUDA GPUs.
34.6KUpdated 8 months agoMIT
#Batch processing#Multimodal input
CLIP is an image and text model for developers and researchers who want to classify pictures without building a labeled dataset for each task. You define the categories in natural language, and the model scores how closely each description matches an image.
Computer Vision ModelsEmbedding and Reranker ModelsVision-Language Models+0
An open-source neural search library for your own text collections, with BERT-based retrieval, CPU search and GPU indexing under the MIT license.
4KUpdated 1 year agoMIT
#Batch processing#Distributed execution#Hugging Face integration
ColBERT is a self-hosted neural search library for developers building document search or retrieval for AI applications. It finds relevant passages in your own text collections and stores their search representations on disk. The Python project is open source under the MIT license.
Embedding and Reranker ModelsRAG and Vector Search+0
Text embedding models support local semantic search and similarity scoring, with English and multilingual variants and Python or JavaScript inference.
huggingface.coEmbedding and Reranker Models
#Batch processing#Hugging Face integration#Multilingual
GTE is Alibaba's family of text embedding models for developers building semantic search and text matching into applications. The models turn text into vectors that let an application compare queries with documents by meaning.
Embedding and Reranker Models+0
Local vision foundation models extract image features through PyTorch, with ViT and ConvNeXt backbones and Hugging Face Transformers support.
11.7KUpdated 4 months ago
#Hugging Face integration
DINOv3 is Meta AI's family of vision foundation models for researchers and developers building image analysis systems on their own hardware. Its pretrained models extract features for individual image regions that can support multiple vision tasks without fine-tuning the backbone.
Computer Vision ModelsEmbedding and Reranker Models+0
Embedding models encode multilingual text and media for search and RAG, with local GGUF and MLX options, a hosted API and on-premises deployment.
jina.aiEmbedding and Reranker Models
Docker#GGUF#LoRA#MLX
Jina Embeddings is a family of models for developers building semantic search, retrieval-augmented generation (RAG) and AI agent applications. The models convert content into vectors that a search system can compare for relevance.
Embedding and Reranker Models+0
Text embedding models for semantic search and document retrieval, with compact and long-context variants under the Apache 2.0 license.
91Updated 2 years agoApache-2.0
#Hugging Face integration
Snowflake Arctic Embed is an open-source family of text embedding models for developers building semantic search and document retrieval. It represents queries and documents as vectors so a search system can match them by meaning.
Embedding and Reranker Models+0
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