gte large onnx

Open WeightModel weights are publicly available — can be downloaded and self-hosted

Released August 20231K context≈ 384 words

A specialist that does one thing and does it well: turning text into dense vector embeddings. It operates with a compact 512-token context window, so it works best with shorter passages rather than long documents. Packaged in ONNX format by Qdrant, it's built for efficient, portable deployment without heavy framework dependencies.

Capabilities

Capability scores are AI-generated based on model documentation, benchmarks, and technical specifications. Learn more

Long Context

Basic

Multilingual

Basic

Factual Knowledge

Use Case Fit

Fit scores are AI-generated based on model capabilities, intended use, and technical specifications. Learn more

gte large onnx

Open WeightModel weights are publicly available — can be downloaded and self-hosted

Released August 20231K context≈ 384 words

A specialist that does one thing and does it well: turning text into dense vector embeddings. It operates with a compact 512-token context window, so it works best with shorter passages rather than long documents. Packaged in ONNX format by Qdrant, it's built for efficient, portable deployment without heavy framework dependencies.

Capabilities

Capability scores are AI-generated based on model documentation, benchmarks, and technical specifications. Learn more

Long Context

Basic

Multilingual

Basic

Factual Knowledge

Use Case Fit

Fit scores are AI-generated based on model capabilities, intended use, and technical specifications. Learn more

Glossary

Context WindowThe maximum number of tokens a model can process in a single conversation or prompt.Dense VectorA compact numerical representation where most values are non-zero, used to efficiently store and compare the meaning of text.Dense Vector EmbeddingsNumerical representations of text where each word or sentence is converted into a list of numbers that capture its meaning, allowing the model to compare semantic similarity.EmbeddingsNumerical representations of text that capture semantic meaning, allowing the model to measure similarity between different words or phrases.ONNXAn open standard format for saving and running machine learning models that works across different frameworks and platforms, making models more portable and efficient.ONNX FormatAn open standard file format for storing trained machine learning models so they can run efficiently across different platforms and frameworks.TokenA small unit of text (a word, subword, or punctuation mark) that a language model breaks input into for processing.Vector EmbeddingsNumerical representations of text where each word or sentence becomes a list of numbers that capture its meaning in a way computers can process.