| --- |
| license: apache-2.0 |
| --- |
| # Ripple-65B Preview NVFP4 (Experimental) |
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|
| <div align="center"> |
| <picture> |
| <source media="(prefers-color-scheme: dark)" srcset="https://framerusercontent.com/images/rSaJGoPP1Saln5kpScZNGCw.png" width="30%"> |
| <source media="(prefers-color-scheme: light)" srcset="https://framerusercontent.com/images/3rFANSmoZuSBhjRwxZolyEU5guo.png" width="30%"> |
| <img alt="Ricursive Logo" src="https://framerusercontent.com/images/3rFANSmoZuSBhjRwxZolyEU5guo.png" width="30%"> |
| </picture> |
| </div> |
|
|
| <p align="center"> |
| <a href="https://huggingface.co/ricursiveAI/Ripple-65B-Preview">BF16</a> | |
| <a href="https://huggingface.co/ricursiveAI/Ripple-65B-Preview-NVFP4">NVFP4</a> | |
| <a href="https://huggingface.co/ricursiveAI/Ripple-65B-Base-Preview">Base Model</a> | |
| <a href="https://huggingface.co/ricursiveAI/Ripple-65B-Preview-DFlash2">DFlash2</a> | |
| <a href="https://www.ricursive.com/ripple">Launch Blog</a> | |
| License: <a href="https://huggingface.co/ricursiveAI/Ripple-65B-Preview/blob/main/LICENSE">Apache 2.0</a> |
| </p> |
|
|
| Ricursive Intelligence introduces **Ripple-65B Preview**, a 65B parameter dense Transformer model pretrained on 12 trillion tokens with support for up to 1M context length. |
| Ripple-65B Preview delivers strong results on agentic coding, scoring **80.52 on TerminalBench 2.1** and |
| surpassing significantly-larger US open-weight models such as Reflection Beam 501B (**80.1**), Meta Muse Spark Open 816B (**76.4**), |
| Thinking Machines Inkling 975B (**63.8**), and NVIDIA Nemotron 3 Ultra 550B (**56.4**). |
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| Our Preview release includes an instruction-tuned model in BF16 and NVFP4 numerics, and a BF16 base model. **This repo contains the NVFP4 instruction-tuned checkpoint.** |
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| This is a conservative quantization of the BF16 checkpoint, using NVFP4 for the MLP layers while keeping attention in BF16. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @misc{ricursive2026ripple65b, |
| title={Ripple-65B: Pushing the Frontier of Local Intelligence}, |
| author={Ricursive Intelligence}, |
| year={2026}, |
| } |
| ``` |
|
|
| ## Contact |
|
|
| For questions and comments, please contact us at [info@ricursive.com](mailto:info@ricursive.com). To learn more, please visit our [website](https://www.ricursive.com). |
