plamo 3 nict 2b base

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

Released October 2025context N/A2b params

A compact, open-weight Japanese-focused base model from PFNet, PLaMo 3 NICT 2B Base sits at the smaller end of the parameter scale, making it lightweight and accessible for local deployment. As a base model, it has no instruction tuning or chat formatting applied — it's a raw foundation intended for further fine-tuning rather than direct conversation. Its Japanese language orientation reflects a collaboration with NICT, suggesting particular attention to Japanese text handling.

Capabilities

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

Long Context

Moderate

Multilingual

Moderate

Creative Writing

Use Case Fit

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

plamo 3 nict 2b base

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

Released October 2025context N/A2b params

A compact, open-weight Japanese-focused base model from PFNet, PLaMo 3 NICT 2B Base sits at the smaller end of the parameter scale, making it lightweight and accessible for local deployment. As a base model, it has no instruction tuning or chat formatting applied — it's a raw foundation intended for further fine-tuning rather than direct conversation. Its Japanese language orientation reflects a collaboration with NICT, suggesting particular attention to Japanese text handling.

Capabilities

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

Long Context

Moderate

Multilingual

Moderate

Creative Writing

Use Case Fit

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

Glossary

AttentionA mechanism that lets the model focus on relevant parts of the input when generating each output token.Base ModelA pretrained model that completes text patterns but hasn't been trained to follow instructions, serving as a starting point for customization through fine-tuning.Fine-TuningThe process of further training a pre-trained model on new data to adapt it for specific tasks or domains.Local DeploymentRunning a model directly on your own computer or server instead of sending requests to a remote service.Parameter ScaleThe total number of trainable weights in a model, often expressed in billions (B); larger models generally have more capacity but require more computing power.