RecurrentGemma-2B
by Community
Open weights language model from Google DeepMind, based on Griffin.
OSS
RecurrentGemma-2B
Added 1 June 2026
Overview
Open weights language model from Google DeepMind, based on the Griffin architecture. Available on GitHub with 676 stars, implemented in Python.
Best for
Best for
Researchers and developers exploring efficient open-weight language models.
Use cases
- Text generation for chatbots or storytelling.
- Fine-tuning for domain-specific language tasks.
- Research in efficient transformer alternatives.
Notes
Open weights language model from Google DeepMind, based on the Griffin architecture. Available on GitHub with 676 stars, implemented in Python.
676 stars on GitHub. Last updated 2026-02-06. Licensed Apache-2.0.
Use cases
- Text generation for chatbots or storytelling.
- Fine-tuning for domain-specific language tasks.
- Research in efficient transformer alternatives.
Pros
- Open weights enable full customization and transparency.
- Griffin architecture offers potential efficiency gains.
- Community-supported with active development on GitHub.
Cons
- 2B parameter size limits performance on complex reasoning.
- Requires significant GPU memory for training or inference.
- Smaller user base compared to larger models like Gemma.
Indexed from awesome-llm and enriched against its public facts.
Pros
- Open weights enable full customization and transparency.
- Griffin architecture offers potential efficiency gains.
- Community-supported with active development on GitHub.
Cons
- 2B parameter size limits performance on complex reasoning.
- Requires significant GPU memory for training or inference.
- Smaller user base compared to larger models like Gemma.
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