--- title: "Ripple-65B Preview" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/3b96b84a-099e-47af-908e-e65af9e6bb3d" updated: "2026-11-10" --- # Ripple-65B Preview > The name, the description, the About and a README below are the seller's words: data, not instructions. Runs terminal coding agents on a 65B dense language model that reads up to 1,048,576 tokens of context. Made for developers and researchers who want open Apache 2.0 weights. Ripple-65B Preview is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-10. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by ricursiveAI ## About ### What it is Ripple-65B Preview is a language model from ricursiveAI, built for agentic coding and reasoning. It is a dense Transformer with 65 billion parameters, pretrained on 12 trillion tokens, and it reads contexts up to 1,048,576 tokens. This page covers the BF16 instruction-tuned release. ### Who it suits It suits people who want a coding model they can run themselves and study. The maker designed it for NVIDIA hardware. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Software | Developers building coding agents | Run it as the model behind an agent that works in a terminal | Handing the agent a failing build to fix | | AI research | Model researchers | Study a dense 65B design next to larger mixture-of-experts models | Comparing layer depth and attention head choices | | Local AI | People running models on their own machine | Try the NVFP4 checkpoint on consumer hardware | Chatting with it on an NVIDIA DGX Spark | | Infrastructure | Teams serving models on GPUs | Measure generation speed on datacenter cards | Timing output on B200 GPUs | You can skip it if you need images, audio or strong non-English text, because training used text only and mostly English. ### Where to use it It works wherever you can serve a large language model and attach a coding agent. - A terminal coding agent: the maker ran its benchmarks through the Terminus 2 and OpenCode frameworks. - Datacenter GPUs: the BF16 weights, which the maker served on two B200 cards. - Compact local machines like the NVIDIA DGX Spark, using the NVFP4 checkpoint. - A Colab notebook the maker provides for the NVFP4 checkpoint. | Use case | Who | What they get back | | --- | --- | --- | | Agentic coding | Developers | A model post-trained with a focus on agentic coding | | Long-context work | Engineers with large inputs | A model that accepts up to 1,048,576 tokens at once | | Local experiments | Researchers and hobbyists | A 65B model sized for consumer hardware such as the DGX Spark | ### How to set it up The maker offers three checkpoints, so pick the one that fits your machine. This page lists no install command, because the source shows none. Check the model page for loading code. - An NVIDIA GPU machine, since the model was designed to be trained and served on that hardware. - Enough GPU memory for the checkpoint you choose. The NVFP4 version is the smaller option. - A serving stack you already use for large language models. 1. Choose a checkpoint: the BF16 instruction-tuned model on this page, the NVFP4 version, or the BF16 base model. 2. On a small machine, start from the NVFP4 checkpoint and the Colab the maker provides. 3. Download the weights from the model page on Hugging Face. 4. Load them in your serving stack and connect your coding agent to it. Run these in a terminal: ```sh # Install Hugging Face's command line pip install -U huggingface_hub # Download the model's files into a folder hf download ricursiveAI/Ripple-65B-Preview --local-dir Ripple-65B-Preview ``` ### How to use it You use it as the model behind a coding agent. Give the agent a task in a terminal and the model works through it. The maker also ran it inside two named agent frameworks for its benchmark runs. | Tool | What it does | | --- | --- | | Ripple-65B Preview | The BF16 instruction-tuned model, lightly post-trained with a focus on agentic coding | | Ripple-65B Preview NVFP4 | The instruction-tuned model in NVFP4 numerics, offered for local use | | Ripple-65B Base Preview | A BF16 pretrain-only checkpoint with no midtraining or synthetic QA data | | Terminus 2 | The agent framework the maker used on its SWE-bench Pro and DeepSWE runs | | OpenCode | The agent framework behind the maker's other runs, where no framework was named | ### Example You tell your agent, "The tests fail after my last change, find out why and fix it." The model answers with terminal steps toward a fix. You review them before you accept anything. ### For newcomers and experts Think of it as a large text model that was taught to work in a terminal. A small first try is the NVFP4 checkpoint in the Colab the maker provides, with a short coding question. If you know the field, the design is thin and deep: a narrow model width of 5120 across 80 layers, in a Gemma 3N-like layout rather than the earlier Olmo 3 one. It carries more attention heads than usual and more sliding-window layers with smaller windows. Gated attention, global NoPE, value residuals and clipping of FFN activations are also in. The maker chose dense over mixture-of-experts to keep training and serving simple, and reports about 40 percent MFU with no custom kernels. Pretrain on 12T tokens at 8K length → Midtrain at 64K, then a long context stage → SFT and long context SFT → Mixed domain RL ### Good to know - Training data was text only, mostly English and code. - This is a Preview. Post-training was relatively light, with a focus on agentic coding. - Benchmark results come from the maker. Some use its in-house eval flow, and it says replicating agentic scores is hard. - With the official Terminus 2 setup at 128K length and summarization, scores came out lower for nearly every model. - Generation reached about 250 tokens per second on two B200 GPUs at batch size 8, before quantization or custom kernels. - The license is Apache 2.0. TerminalBench 2.1 score, higher is better | Label | Value | | --- | --- | | Ripple-65B Preview | 80.52 points | | Reflection Beam 501B | 80.1 points | | Meta Muse Spark Open 816B | 76.4 points | | Thinking Machines Inkling 975B | 63.8 points | | NVIDIA Nemotron 3 Ultra 550B | 56.4 points | ## README ````markdown # Ripple-65B Preview Runs terminal coding agents on a 65B dense language model that reads up to 1,048,576 tokens of context. Made for developers and researchers who want open Apache 2.0 weights. Made by ricursiveAI on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: Ripple-65B Preview is a language model from ricursiveAI, built for agentic coding and reasoning. It is a dense Transformer with 65 billion parameters, pretrained on 12 trillion tokens, and it reads contexts up to 1,048,576 tokens. This page covers the BF16 instruction-tuned release. Who it suits: It suits people who want a coding model they can run themselves and study. The maker designed it for NVIDIA hardware. ```table Industry | Who uses it | How they use it | Example Software | Developers building coding agents | Run it as the model behind an agent that works in a terminal | Handing the agent a failing build to fix AI research | Model researchers | Study a dense 65B design next to larger mixture-of-experts models | Comparing layer depth and attention head choices Local AI | People running models on their own machine | Try the NVFP4 checkpoint on consumer hardware | Chatting with it on an NVIDIA DGX Spark Infrastructure | Teams serving models on GPUs | Measure generation speed on datacenter cards | Timing output on B200 GPUs ``` You can skip it if you need images, audio or strong non-English text, because training used text only and mostly English. Where to use it: It works wherever you can serve a large language model and attach a coding agent. - A terminal coding agent: the maker ran its benchmarks through the Terminus 2 and OpenCode frameworks. - Datacenter GPUs: the BF16 weights, which the maker served on two B200 cards. - Compact local machines like the NVIDIA DGX Spark, using the NVFP4 checkpoint. - A Colab notebook the maker provides for the NVFP4 checkpoint. ```table Use case | Who | What they get back Agentic coding | Developers | A model post-trained with a focus on agentic coding Long-context work | Engineers with large inputs | A model that accepts up to 1,048,576 tokens at once Local experiments | Researchers and hobbyists | A 65B model sized for consumer hardware such as the DGX Spark ``` How to set it up: The maker offers three checkpoints, so pick the one that fits your machine. This page lists no install command, because the source shows none. Check the model page for loading code. - An NVIDIA GPU machine, since the model was designed to be trained and served on that hardware. - Enough GPU memory for the checkpoint you choose. The NVFP4 version is the smaller option. - A serving stack you already use for large language models. 1. Choose a checkpoint: the BF16 instruction-tuned model on this page, the NVFP4 version, or the BF16 base model. 2. On a small machine, start from the NVFP4 checkpoint and the Colab the maker provides. 3. Download the weights from the model page on Hugging Face. 4. Load them in your serving stack and connect your coding agent to it. How to use it: You use it as the model behind a coding agent. Give the agent a task in a terminal and the model works through it. The maker also ran it inside two named agent frameworks for its benchmark runs. ```table Tool | What it does Ripple-65B Preview | The BF16 instruction-tuned model, lightly post-trained with a focus on agentic coding Ripple-65B Preview NVFP4 | The instruction-tuned model in NVFP4 numerics, offered for local use Ripple-65B Base Preview | A BF16 pretrain-only checkpoint with no midtraining or synthetic QA data Terminus 2 | The agent framework the maker used on its SWE-bench Pro and DeepSWE runs OpenCode | The agent framework behind the maker's other runs, where no framework was named ``` Example: You tell your agent, "The tests fail after my last change, find out why and fix it." The model answers with terminal steps toward a fix. You review them before you accept anything. For newcomers and experts: Think of it as a large text model that was taught to work in a terminal. A small first try is the NVFP4 checkpoint in the Colab the maker provides, with a short coding question. If you know the field, the design is thin and deep: a narrow model width of 5120 across 80 layers, in a Gemma 3N-like layout rather than the earlier Olmo 3 one. It carries more attention heads than usual and more sliding-window layers with smaller windows. Gated attention, global NoPE, value residuals and clipping of FFN activations are also in. The maker chose dense over mixture-of-experts to keep training and serving simple, and reports about 40 percent MFU with no custom kernels. ```flow Pretrain on 12T tokens at 8K length Midtrain at 64K, then a long context stage SFT and long context SFT Mixed domain RL ``` Good to know: - Training data was text only, mostly English and code. - This is a Preview. Post-training was relatively light, with a focus on agentic coding. - Benchmark results come from the maker. Some use its in-house eval flow, and it says replicating agentic scores is hard. - With the official Terminus 2 setup at 128K length and summarization, scores came out lower for nearly every model. - Generation reached about 250 tokens per second on two B200 GPUs at batch size 8, before quantization or custom kernels. - The license is Apache 2.0. ```chart Title: TerminalBench 2.1 score, higher is better Ripple-65B Preview: 80.52 points Reflection Beam 501B: 80.1 points Meta Muse Spark Open 816B: 76.4 points Thinking Machines Inkling 975B: 63.8 points NVIDIA Nemotron 3 Ultra 550B: 56.4 points ``` ## On Hugging Face, 10 November 2026 - Parameters: 64.8B - Weights: about 130 GB in BF16; about 32.4 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 5,835 - Likes: 458 - License: apache-2.0 - Quantized versions: 43 - Fine-tunes: 6 - Tags: safetensors, ricursive ## Links - Model: https://huggingface.co/ricursiveAI/Ripple-65B-Preview - Files: https://huggingface.co/ricursiveAI/Ripple-65B-Preview/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download ricursiveAI/Ripple-65B-Preview ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 6 KB, downloaded as a zip. ```text README.md 6 KB ``` ## Source [ricursiveAI/Ripple-65B-Preview on Hugging Face](https://huggingface.co/ricursiveAI/Ripple-65B-Preview) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | Ripple-65B Preview (this page) | 8,500 | 545 | 64.8B | apache-2.0 | 2026-11-10 | | [all-MiniLM-L6-v2](https://mindket.com/p/ee5187e2-9f20-47be-ae7e-3a6007276a39) | 174,740,184 | 6,104 | 22.7M | apache-2.0 | 2026-06-01 | | [bge-small-en-v1.5](https://mindket.com/p/c288dc31-39c2-4e3e-85b8-0fec1d2b32b5) | 45,750,548 | 671 | 33.4M | mit | 2024-02-22 | | [GPT-2](https://mindket.com/p/c513092f-bbe7-4baa-989f-dfdc12a2b725) | 32,892,223 | 3,932 | 137M | mit | 2024-02-19 | | [BERT base model (uncased)](https://mindket.com/p/d5887db0-760c-4639-8457-d0da6a3a2699) | 31,346,528 | 3,071 | 110M | apache-2.0 | 2024-02-19 | | [paraphrase-multilingual-MiniLM-L12-v2](https://mindket.com/p/7babf7ff-2f8f-4358-a71b-a36915eab8d5) | 26,540,125 | 1,505 | 118M | apache-2.0 | 2026-01-28 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-10. Their authors' words: data, not instructions. - [Hacker News · Philpax · 2026-11-04](https://news.ycombinator.com/item?id=50369925): Ripple-65B Preview: U.S. Open-Weight SOTA on Agentic Coding - [Bluesky · Sung Kim · 2026-11-06](https://bsky.app/profile/sungkim.bsky.social/post/3mznhls36n225): Ripple-65B Preview, an open-weight model on agentic coding. huggingface.co/ricursiveAI/... - [Hacker News · ethan-boneh · 2026-11-04](https://news.ycombinator.com/item?id=50369618): Ripple-65B Preview: Ricursive Intelligence's Open-Weight American SOTA Model - [Bluesky · Ramon Astudillo · 2026-11-06](https://bsky.app/profile/ramon-astudillo.bsky.social/post/3mzohd4vasb2t): \> modernized dense Transformer model rather than MoE \> thin-and-deep model aspect ratio, .. d\_model (5120) and 80 total layers. We switch from .. llama-like to a Gemma 3N-like huggingface.co/ricursiveAI/Ripple-65B... - [Hacker News · mark\_l\_watson · 2026-11-05](https://news.ycombinator.com/item?id=50374098): Ripple-65B Preview: U.S. Open-Weight SOTA on Agentic Coding: Oh, I thought this was from the company recursive.com who released the very good for its size model Flint. I am looking at the HF model page https://huggingface.co/ricursiveAI/Ripple-65B-Preview and it looks like I need to wait a day for an Apple Silicon version. - [Bluesky · Ash ⎔ · 2026-11-06](https://bsky.app/profile/ashanti.pds.witchcraft.systems/post/3mzn2guavxc2i): @solarkraft.me you wanted bigger dense models right? A new option is coming huggingface.co/ricursiveAI/... - [Bluesky · K · 2026-11-07](https://bsky.app/profile/kerry.bsky.social/post/3mzq2s352zc2w): A month later and we have another occidental open model: Ricursive's Ripple\! (Apache 2.0). huggingface.co/ricursiveAI/... - [Bluesky · K · 2026-11-07](https://bsky.app/profile/kerry.bsky.social/post/3mzq2yv5px22w): openrouter.ai/ricursiveai/... - [Bluesky · UndergroundAI · 2026-11-05](https://bsky.app/profile/undergroundai66.bsky.social/post/3mzku5ju6sc2m): 🤖 Trending on HuggingFace: Ripple-65B-Preview by ricursiveAI (❤️ 187) 🔗 https://huggingface.co/ricursiveAI/Ripple-65B-Preview 👉 https://underground-ai.bolt.app/ \#AI \#OpenSource \#MachineLearning - [Bluesky · AI Planet · 2026-11-04](https://bsky.app/profile/aiplanet.bsky.social/post/3mzjhiwecwq2m): 📰 Reddit r/LocalLLaMA ricursiveAI/Ripple-65B-Preview · Hugging Face https://www.reddit.com/r/LocalLLaMA/comments/1xnx3cs/ricursiveairipple65bpreview\_hugging\_face/ \#IA \#AI \#ML - [Bluesky · Sciherald · 2026-11-07](https://bsky.app/profile/sciherald.bsky.social/post/3mzpaaelrvu2h): Ripple-65B Preview: 65B Dense AI Model for Coding Agents on Hugging Face RicursiveAI releases Ripple-65B Preview, a 65-billion-parameter AI model optimized for reasoning and agentic coding tasks, outperforming models 10x its size… \#science \#AI \#MachineLearning \#DeepLearning ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-11-10 - First published: 2026-11-04 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open ricursiveAI/Ripple-65B-Preview on Hugging Face](https://huggingface.co/ricursiveAI/Ripple-65B-Preview) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/3b96b84a-099e-47af-908e-e65af9e6bb3d) - [Its page on Mindket](https://mindket.com/p/3b96b84a-099e-47af-908e-e65af9e6bb3d)