Ripple-65B Preview DFlash2
BF16 | NVFP4 | Base Model | DFlash2 | Launch Blog | License: Apache 2.0
Ripple-65B Preview DFlash2 is a draft model for speculative decoding with Ripple-65B Preview or Ripple-65B Preview NVFP4. This repo contains the BF16 drafter. It requires the target model to generate text.
The drafter proposes multiple tokens at once, conditioned on intermediate hidden states from Ripple. The target then verifies the proposals. A serving implementation with correct speculative rejection sampling preserves the target model's sampling distribution.
Model
DFlash2 combines parallel block drafting, dynamic convolutions, and a candidate selector. This checkpoint drafts blocks of 16 tokens, retains 32 candidates per position, and uses a rank-256 selector. Its dynamic convolutions have kernel size 4 and group size 16.
The draft has four sliding-attention layers with a 4,096-token window, followed by a full-attention layer without rotary position embeddings (NoPE). The full-attention layer can attend beyond the sliding window.
The checkpoint contains 4.04B parameters (4,037,752,064), all stored in BF16. This count is derived from the tensors in model.safetensors; it excludes the target model's shared token embedding and output head, which are not stored in this repository.
| Property | Value |
|---|---|
| Architecture | DFlash2DraftModel |
| Draft layers | 5 |
| Hidden size | 5,120 |
| Query / KV heads | 80 / 8 |
| Head dimension | 128 |
| MLP | SwiGLU, intermediate size 40,960 |
| Attention pattern | 4 sliding-attention layers + 1 full-attention layer |
| Sliding window | 4,096 tokens |
| NoPE layer | Draft layer 4 (zero-based) |
| RoPE theta | 10,000 on rotary-enabled layers |
| Target hidden-state layers | 1, 20, 39, 58, 77 (zero-based) |
| Target depth | 80 layers |
| Vocabulary size | 100,352 |
| Draft block size | 16 |
| Dynamic convolution | Kernel size 4, group size 16 |
| Candidate selector | Top-k 32, rank 256 |
| Configured maximum positions | 131,072 |
| Weight precision | BF16 |
Serve
Load this repository as the speculative draft model alongside Ripple-65B Preview. Use the target model's tokenizer, chat template, token embeddings, and output head.
The serving runtime must support Ripple and DFlash2DraftModel, including this checkpoint's per-layer attention pattern, NoPE layer, four-tap convolutions, and target hidden-state taps. Preserve these settings from config.json when loading the draft.
For SGLang-based serving, the draft model settings are:
--speculative-algorithm DFLASH \
--speculative-draft-model-path ricursiveAI/Ripple-65B-Preview-DFlash2 \
--speculative-draft-model-quantization unquant \
--speculative-num-draft-tokens 16
These flags supplement a Ripple-compatible server launch; they are not a standalone launch command. The export's recorded validation used the ripple-sglang runtime on eight H100 GPUs with the RL-step-500 target checkpoint. That validation does not establish compatibility or performance for other target revisions or runtime versions.
Citation
@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. To learn more, please visit our website.