peft

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    "@id": "https://agentsignals.ai/agents/peft",
    "name": "peft",
    "description": "PEFT is an advanced library provided by Hugging Face, focusing on parameter-efficient fine-tuning techniques. It allows users to fine-tune large pre-trained models with fewer resources and lower computational costs without compromising model performance, making it suitable for various natural language processing tasks.",
    "url": "https://agentsignals.ai/agents/peft",
    "applicationCategory": "研究",
    "operatingSystem": "GitHub",
    "sameAs": "https://github.com/huggingface/peft",
    "installUrl": "https://github.com/huggingface/peft",
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      "price": "0",
      "priceCurrency": "USD",
      "description": "免费",
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    },
    "featureList": [
      "Supports efficient multi-parameter fine-tuning",
      "Easy to integrate into existing workflows",
      "Compatible with Hugging Face Transformers"
    ],
    "datePublished": "2025-12-05T16:12:39.037594+00:00",
    "dateModified": "2025-12-20T15:27:03.297713+00:00",
    "publisher": {
      "@type": "Organization",
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    "mainEntity": [
      {
        "@type": "Question",
        "name": "What is peft?",
        "acceptedAnswer": {
          "@type": "Answer",
          "text": "A parameter-efficient fine-tuning library for optimizing large-scale pre-trained models."
        }
      },
      {
        "@type": "Question",
        "name": "What features does peft offer?",
        "acceptedAnswer": {
          "@type": "Answer",
          "text": "Supports efficient multi-parameter fine-tuning, Easy to integrate into existing workflows, Compatible with Hugging Face Transformers"
        }
      },
      {
        "@type": "Question",
        "name": "What are the use cases for peft?",
        "acceptedAnswer": {
          "@type": "Answer",
          "text": "Fine-tuning large-scale models, Model optimization in resource-constrained environments, Rapid prototyping"
        }
      },
      {
        "@type": "Question",
        "name": "What are the advantages of peft?",
        "acceptedAnswer": {
          "@type": "Answer",
          "text": "提高微调效率, 减少计算资源需求, 易于使用"
        }
      },
      {
        "@type": "Question",
        "name": "What are the limitations of peft?",
        "acceptedAnswer": {
          "@type": "Answer",
          "text": "可能需要特定的硬件支持, 对于小型模型效果不明显"
        }
      }
    ]
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