RecurrentGemma Models by Google DeepMind
Details
About
RecurrentGemma is a family of open-weight language models developed by Google DeepMind, known for their cutting-edge Griffin architecture. This hybrid design blends linear recurrences with local attention mechanisms, allowing the models to excel in a range of language tasks with reduced memory overhead and efficient inference, especially on lengthy sequences. Unlike traditional transformer models that require memory scaling linearly with sequence length, RecurrentGemma maintains a fixed-sized state, resulting in faster processing speeds. Both pre-trained and instruction-tuned variants are available, the latter being tailored for tasks like dialogue and instruction following. Accessible through platforms like Hugging Face and Kaggle, RecurrentGemma-2B achieves performance akin to Gemma-2B despite being trained on fewer tokens, demonstrating its efficiency and versatility 23910.
Current Variants
Use-when guidance is derived from seed capabilities, context, release, and replacement fields.
Use when the workload needs 4k context and 9B parameters.
| Model | Use when | Released | Signals | Status |
|---|---|---|---|---|
| RecurrentGemma 9B | Use when the workload needs 4k context and 9B parameters. | 2024-06 | 4k context9B parameters | Current |
Release Timeline
2 release groupsSpecifications(2 models)
| Model | Released | Context | Parameters |
|---|---|---|---|
| RecurrentGemma 9B | 2024-06 | 4k | 9B |
Available From(1 provider)
Frequently Asked Questions
- What is RecurrentGemma used for?
- RecurrentGemma is used for chatbot and role-playing use cases. The family description and listed model capabilities point to those workloads as the best fit.
- How does RecurrentGemma compare to Gemma 4?
- RecurrentGemma by Google DeepMind is strongest where you need chatbot and role-playing use cases, while Gemma 4 by Google DeepMind is the closest related family to check for multimodal. RecurrentGemma has 2 listed variants and reaches up to 4k context, while Gemma 4 reaches up to 256k context, so compare the specs and pricing tables before choosing a production model.
- Which RecurrentGemma model should I use?
- If price is the main constraint, use the pricing table first because RecurrentGemma does not have complete provider pricing in the local data. For the most capable/latest local choice, evaluate RecurrentGemma 9B with 4k context.
