Instructions to use OpenMOSE/RWKV-Reka-3.1-Flash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- RWKV
How to use OpenMOSE/RWKV-Reka-3.1-Flash with RWKV:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
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README.md
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### Model Description
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- **Developed by:** OpenMOSE
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- **Model type:** Hybrid Linear-Attention Language Model
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### Model Description
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RWKV-Reka-3.1 Flash is an RNN hybrid architecture model that combines RWKV v7's linear attention mechanism with Group Query Attention (GQA) layers. Built upon the Reka-flash3.1 21B foundation, this model replaces most Transformer attention blocks with RWKV blocks while strategically maintaining some GQA layers to enhance performance on specific tasks.
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- **Developed by:** OpenMOSE
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- **Model type:** Hybrid Linear-Attention Language Model
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