{
 "generated": "2026-11-06",
 "sha": "38139a88e98b",
 "node": {
  "id": "model:sarvam-prism",
  "type": "Model",
  "in_eps": [
   110
  ],
  "name": "Sarvam Prism",
  "slug": "sarvam-prism",
  "date": "2026-11-03",
  "category": "decision",
  "category_zh": "决策模型（这几周新出现的一类）",
  "access": "open",
  "license": "Apache-2.0",
  "eps": [
   110
  ],
  "page": "/graph/models/#m-sarvam-prism",
  "size": "Sarvam 105B（约 105B 总参数、10B 激活的 MoE）上的 LoRA 微调：输入状态、问题和选项，一次前向给出每个选项的概率，不写文字；上下文最长 131,072 token；FP8",
  "avail": "开放权重，不设门槛（HF 未设 gated）；FP8 跑在单张至少 120GB 显存的卡上；HF 卡写权重是自定义 FP8 布局，不能用 AutoModelForCausalLM 或通用推理服务器加载；Sarvam API 上也能调",
  "license_full": "Apache-2.0（博客、HF 卡）",
  "facts": [
   "Sarvam 自测：三个指数平均 71.8 分、B200 上 44 毫秒，是被测 25 个决策模型里 100 毫秒以内分最高的；分更高的有 mammoth-1（75.2，284 毫秒）和 GLiDE 1.2（73.2，116 毫秒）。在 Jev Decision Index 上是 67.0，低于 Perplexity Decider v1.2 的 67.7（博客，厂商自述）",
   "口径限定：25 个模型是 Sarvam 自己跑的三个指数，其中一个是它自己重建的 Artificial Analysis 决策指数，另一个是内部的印度语言基准；部分对手的耗时用的是它们公开报告的数（博客）",
   "博客写这一类“决策模型”的名字来自 TypeSafe 上月发布的 Jev；本期的 H2O-Lightning 同属这一类。HF 建仓 11-02，提交 11-03"
  ],
  "sources": [
   {
    "label": "Sarvam 博客：Introducing Sarvam Prism",
    "url": "https://www.sarvam.ai/blogs/sarvam-prism",
    "kind": "official"
   },
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    "label": "HF 模型页",
    "url": "https://huggingface.co/sarvamai/prism",
    "kind": "official"
   },
   {
    "label": "API 文档",
    "url": "https://docs.sarvam.ai/api/token-factory/models/sarvam-prism",
    "kind": "official"
   }
  ],
  "ep_tags": {
   "110": "官方博客 11-03"
  },
  "developer_text": "Sarvam AI（印度）",
  "degree": 0.0008
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 "page": "https://xiaopingfeng.com/graph/e/model/sarvam-prism/",
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   "unit": "",
   "role": "primary",
   "status": "base",
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     "label": "本站新模型台账",
     "text": "新模型台账：开发者栏写作“Sarvam AI（印度）”；Sarvam 博客：Introducing Sarvam Prism",
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     "text": "AI 新模型台账：Sarvam Prism（决策模型（这几周新出现的一类））",
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