clip-models
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falcon-40b-code-alpaca
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View details ↗falcon-7b-code-alpaca
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View details ↗flat-2d-animerge
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View details ↗To run the model on GPU, you need to install Flash Attention. You may either install from pypi (which may not work with fused-dense), or from source. To install from source, clone the GitHub repository: The code provided here should work with commit `43950dd`. Change to the cloned repo and install: If you would like to use fused MLPs (e.g. to use activation checkpointing), you may install fused-dense also from source:
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View details ↗jina-bert-implementation
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View details ↗jina-clip-implementation
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View details ↗jina-clip-v1
jinaai`jina-clip-v1` is a state-of-the-art English **multimodal (text-image) embedding model**. Traditional text embedding models, such as jina-embeddings-v2-base-en, excel in text-to-text retrieval but incapable of cross-modal tasks. Models like openai/clip-vit-base-patch32 effectively align image and text embeddings but are not optimized for text-to-text retrieval due to their training methodologies and context limitations. `jina-clip-v1` bridges this gap by offering robust performance in both domains. Its text component matches the retrieval efficiency of `jina-embeddings-v2-base-en`, while its overall architecture sets a new benchmark for cross-modal retrieval. This dual capability makes it an excellent tool for multimodal retrieval-augmented generation (MuRAG) applications, enabling seamless text-to-text and text-to-image searches within a single model.
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View details ↗jina-clip-v2
jinaaiThis model is based on the paper jina-clip-v2: Multilingual Multimodal Embeddings for Text and Images. `jina-clip-v2` is a **general-purpose multilingual multimodal embedding model for text & images**. Multimodal embeddings enable searching and understanding data across different modalities through a coherent representation. They serve as the backbone of neural information retrieval and multimodal GenAI applications.
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View details ↗`jina-code-embeddings` is an embedding model for code retrieval. The model supports various types of code retrieval (text-to-code, code-to-code, code-to-text, code-to-completion) and technical question answering across 15+ programming languages. Built on Qwen/Qwen2.5-Coder-0.5B, `jina-code-embeddings-0.5b` features: - **Multilingual support** (15+ programming languages) and compatibility with a wide range of domains, including web development, software development, machine learning, data science, and educational coding problems. - **Task-specific instruction prefixes** for NL2Code, Code2Code, Code2NL, Code2Completion, and Technical QA, which can be selected at inference time. - **Flexible embedding size**: dense embeddings are 896-dimensional by default but can be truncated to as low as 64 with minimal performance loss.
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View details ↗`jina-code-embeddings` is an embedding model for code retrieval. The model supports various types of code retrieval (text-to-code, code-to-code, code-to-text, code-to-completion) and technical question answering across 15+ programming languages. Please refer to our technical report of jina-code-embeddings for training details and benchmarks. If you find it useful in your research, please cite the following paper:
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View details ↗jina-colbert-v1-en
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View details ↗jina-colbert-v2
jinaaiJinaColBERT V2 (`jina-colbert-v2`) is a new model based on the JinaColBERT V1 that expands on the capabilities and performance of the `jina-colbert-v1-en` model. Like the previous release, it has Jina AI’s 8192 token input context and the improved efficiency, performance, and explainability of token-level embeddings and late interaction. - Multilingual support for dozens of languages, with strong performance on major global languages. - Matryoshka embeddings, which allow users to trade between efficiency and precision flexibly. - Superior retrieval performance when compared to the English-only `jina-colbert-v1-en`. JinaColBERT V2 offers three different versions for different embeddings dimensions: `jinaai/jina-colbert-v2`: 128 dimension embeddings `jinaai/jina-colbert-v2-96`: 96 dimension embeddings `jinaai/jina-colbert-v2-64`: 64 dimension embeddings
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View details ↗jina-colbert-v2-64
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View details ↗jina-embedding-b-en-v1
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View details ↗jina-embedding-l-en-v1
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