LLM-Finetuning

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    "name": "LLM-Finetuning",
    "description": "LLM-Finetuning is a GitHub-based project that focuses on fine-tuning large language models (LLMs) using parameter-efficient fine-tuning techniques (PEFT). This project provides developers with a set of tools and guidelines to help them train and optimize their custom LLMs more efficiently. By using PEFT, users can significantly reduce training time and resource consumption while maintaining model performance.",
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    "featureList": [
      "Efficient fine-tuning using PEFT technology",
      "Suitable for various large language models",
      "Provides detailed documentation and example code"
    ],
    "datePublished": "2025-12-05T17:14:18.438428+00:00",
    "dateModified": "2025-12-20T11:48:49.761607+00:00",
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          "text": "Fine-tuning large language models using PEFT technology"
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      {
        "@type": "Question",
        "name": "What features does LLM-Finetuning offer?",
        "acceptedAnswer": {
          "@type": "Answer",
          "text": "Efficient fine-tuning using PEFT technology, Suitable for various large language models, Provides detailed documentation and example code"
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      },
      {
        "@type": "Question",
        "name": "What are the use cases for LLM-Finetuning?",
        "acceptedAnswer": {
          "@type": "Answer",
          "text": "Natural Language Processing Research, Enterprise-level Text Generation Applications, Personalized Content Recommendation Systems"
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      },
      {
        "@type": "Question",
        "name": "What are the advantages of LLM-Finetuning?",
        "acceptedAnswer": {
          "@type": "Answer",
          "text": "减少训练资源消耗, 提高模型训练效率, 支持多种 LLM"
        }
      },
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        "name": "What are the limitations of LLM-Finetuning?",
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          "text": "需要一定的编程基础, 可能需要特定的硬件支持"
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