{
 "generated": "2026-11-06",
 "sha": "38139a88e98b",
 "node": {
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  "name": "Ripple-65B Preview",
  "slug": "ripple-65b-preview",
  "date": "2026-11-04",
  "category": "open-llm",
  "category_zh": "开放权重 LLM",
  "access": "open",
  "license": "Apache-2.0",
  "eps": [
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  "page": "/graph/models/#m-ripple-65b-preview",
  "size": "65B 稠密 Transformer，从头预训练，12T token（以英文和代码为主的纯文本），支持最长 1M token 上下文；窄而深：d_model 5120、80 层，加了门控注意力、全局 NoPE、value residual 等改动（模型卡）。是 Ricursive 芯片设计平台里那个自有模型的通用版本（发布页）",
  "avail": "开放权重，不设门槛（HF 未设 gated）：BF16 指令版、NVFP4 版、只做过预训练的 Base 版和 DFlash2 推测解码草稿模型；发布页写可在 Hugging Face、Together AI、GMI Cloud、OpenRouter 上用，Fireworks 当天晚些上线；价格本刊没有核到",
  "license_full": "Apache-2.0（模型卡）",
  "facts": [
   "发布页称 TerminalBench 2.1 得 80.52，是美国开放权重模型里最高，超过 Reflection Beam 501B（80.1）、Meta Muse Spark Open 816B（76.4）、Thinking Machines Inkling 975B（63.8）（厂商自述，自家评测设置）",
   "发布页写从零开始的全部研发成本 3,200 万美元，称约为此前开放权重最佳模型的十分之一，后者是它自己的估算（厂商自述）",
   "开发者是做 AI 芯片设计的实验室，官网写团队做过 AlphaChip；这个模型是它芯片设计平台的底座（官网）",
   "发布日出处：发布页只写 “Today, we release Ripple-65B Preview”，日期取自官方 X 帖（UTC 11-04 18:24）；HF 仓库 UTC 11-04 02:41 建，Base 仓库更早建，都不是发布日"
  ],
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    "url": "https://huggingface.co/ricursiveAI/Ripple-65B-Preview",
    "kind": "official"
   },
   {
    "label": "官方发布帖（X）",
    "url": "https://x.com/RicursiveAI/status/2118046765857546733",
    "kind": "official"
   }
  ],
  "ep_tags": {
   "110": "官方发布帖 UTC 11-04 18:24 · 北京 11-05 凌晨；发布页没写日期"
  },
  "developer_text": "Ricursive Intelligence",
  "degree": 0.0008
 },
 "page": "https://xiaopingfeng.com/graph/e/model/ripple-65b-preview/",
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