So Ricursive Intelligence (not to be confused with Recursive, the makers of Flint) just dropped the preview of their new dense 65B model, which appears to have really impressive performance, putting it on the frontier of American open-weights models of any size. Their benchmarks compare it with Muse Spark Open (816B) and Reflection's Beam (501B), and it trades blows with them. They "use a standard post-training recipe consisting of midtraining, long context, SFT, long context SFT, and mixed domain RL. Our Preview release has gone through relatively light posttraining, with a focus on agentic coding." 4:1 SWA:global attention "thin & deep" model aspect ratio (80 layer x 5120 dims) FFN activation clipping, gated attention, global NoPE, and value residuals pretrain-only checkpoint without any midtraining or synthetic QA data DFlash2 draft model A lot of folks here have been clamoring for a new ~70B dense model for a long time now, and if it's not benchmaxxed as hell, this could be a really cool new tool in our arsenal. If this is only lightly post-trained for agentic coding, not the full potential of the model, this could be a really powerful release in the long run. Some interesting design decisions as well. AtomicChat GGUFs (imatrix coming soon) -- Q8_0: 68.8 GB, Q4_K_M: 38.8 GB. Handful of benchmarks (see model card for more) it shares with Qwen models: Benchmark Ripple-65B-Preview Qwen3.8-27B Qwen3.8-Flash-Next Terminal-Bench 2.1 80.5 73.0 — SWE-bench Pro 61.4 61.7 62.5 DeepSWE 1.1 44.3 42.2 58.7 IFBench 79.2 79.5 81.3 GPQA Diamond 85.2 89.2 91.7 To build the Ricursive llama.cpp fork: git clone https://github.com/ggml-org/llama.cpp cd llama.cpp git checkout 9e2a04a439edc49f08c5acdafefae19dfa25a658 curl -LO https://huggingface.co/AtomicChat/Ripple-65B-Preview-GGUF/resolve/main/ricursive-port.patch git apply ricursive-port.patch cmake -B build -DGGML_CUDA=ON # Mac: drop -DGGML_CUDA=ON cmake --build build -j --config Release --target llama-server llama-cli If you try it our, report back with results! I'm downloading it now. Some questions about how it will perform given it's an early checkpoint, and they weren't exactly consistently clear about the actual training (seems like they said some contradictory things). Thoughts?   submitted by   /u/returnity [link]   [comments]