kanana nano 2.1b embedding

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

Released February 20258K context≈ 6,144 words2.1b params

A compact, open-weight embedding model that converts text into dense vector representations within an 8,192-token context window. Its small 2.1B parameter footprint makes it lightweight and deployable in resource-constrained environments. Released under a non-commercial Creative Commons license, it's accessible for research and experimentation but not production commercial use.

Capabilities

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

Instruction Following

Moderate

Creative Writing

kanana nano 2.1b embedding

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

Released February 20258K context≈ 6,144 words2.1b params

A compact, open-weight embedding model that converts text into dense vector representations within an 8,192-token context window. Its small 2.1B parameter footprint makes it lightweight and deployable in resource-constrained environments. Released under a non-commercial Creative Commons license, it's accessible for research and experimentation but not production commercial use.

Capabilities

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

Instruction Following

Moderate

Creative Writing

Glossary

Context WindowThe maximum number of tokens a model can process in a single conversation or prompt.Creative Commons LicenseA standardized legal license (like CC-BY-4.0) that specifies how others can use, share, and modify a work with clear attribution requirements.Dense VectorA compact numerical representation where most values are non-zero, used to efficiently store and compare the meaning of text.EmbeddingA dense numerical vector that represents a word, sentence, or concept in a high-dimensional space.Embedding ModelA model that converts text into numerical vectors that capture semantic meaning, allowing computers to understand and compare the similarity between different pieces of text.Parameter FootprintThe total number of learnable weights in a model, which directly affects its memory requirements and computational cost — smaller footprints run faster on consumer devices.Resource-ConstrainedHardware with limited memory, processing power, or battery life, requiring models to be optimized for efficiency.TokenA small unit of text (a word, subword, or punctuation mark) that a language model breaks input into for processing.

Capabilities

Capabilities

Use Case Fit

Glossary