Open Source
RecurrentGemma-2B
Open weights language model from Google DeepMind, based on Griffin.
Our verdict
We have not reviewed RecurrentGemma-2B yet
No one at Enterprise DNA has checked this listing against its sources. The description comes from public listings gathered automatically. Any fact below shows where it came from and when it was last checked.
From the listing
About RecurrentGemma-2B
Open weights language model from Google DeepMind, based on the Griffin architecture. Available on GitHub with 676 stars, implemented in Python.
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
676 stars on GitHub. Last updated 2026-02-06. Licensed Apache-2.0.
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.
Key facts
What we have checked
- Latest version
- 1.0.1 (released 6 Feb 2026)
- GitHub stars
- 685
Each value shows when we last confirmed it and where it came from. Values we have not confirmed are left out, never guessed.
Stars and usage
How RecurrentGemma-2B is growing
676 to 685
Sources
Where this comes from
- GitHubOfficial
- github api via nightly crawl, recovered from git history 20e874270887c1008f860ee0198e461a23c9e438Registry
A source shows what was published when we checked it. It does not certify security, performance or a working installation. Read our method.
Changelog
What changed
Released 1.0.1
Released 1.0.0
Pairs with
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A high-throughput and memory-efficient inference and serving engine for LLMs
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