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发展方向：由专用模型到通用模型 第一步模型选型。针对于应用场景，比较多种大模型相关维度的能力，进行模型评测。经过模型评测初步选型之后，可选定意向大模型。 第二步评估业务场景复杂度。若不复杂，可直接把模型拿过来应用。则继续如果业务场景非常复杂，通常来讲直接开源模型无法满足需求，需要微调、prompt工程等进一步构建。 第三步，判断全参数微调或部分微调等，并计算所需算力，应提前规划，避免微调失败。如果算力足够，可进行全参数微调。如果算力资源比较受限，只能进行部分参数微调，类似把大部分的参数固定住，只调一小部分参数。 第四步，构建基于大模型的智能体需要考虑模型与环境的交互。如果需要调用外部API或与已有业务数据库交互，就需要构建智能体。如果不需要与环境交互，就可以直接将微调好的模型在业务场景中试用。 第五步，模型评测，并评估是否上线应用或继续迭代。 第六步，模型部署。关于软件系统相关性能、安全、功能等方面内容。如考虑如何以更少的资源部署模型，或者如何提升整个应用的吞吐量。 |
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nice!但是写成笔记链接的形式会更好哈~ |
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nice! |
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https://github.com/LordHidy/InternLM_learning/blob/main/Notes/Lesson_1.md |
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nice! |
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https://zqm6a0l4f0l.feishu.cn/wiki/Pv7VwKByIim5OfkPaLScc172ndg?from=from_copylink |
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nice~ |
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https://blog.csdn.net/ynx005/article/details/135484860?spm=1001.2014.3001.5502 |
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nice~ |
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https://blog.csdn.net/lalala12ll/article/details/135487885?spm=1001.2014.3001.5501 |
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nice~ |
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https://p065drvsp5m.feishu.cn/docx/SnXldHZkMoeNQLx2F7qcgsRzn1c?from=from_copylink |
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nice~ |
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nice~ |
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https://blog.csdn.net/jinghangyz/article/details/135488141?spm=1001.2014.3001.5502 |
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nice~ |
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nice~ |
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nice~ |
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https://github.com/zengya55/InterLM_Learning/blob/main/lesson1/InterLM%20course%201.md |
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nice~ |
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https://blog.csdn.net/weixin_52524888/article/details/135491112 |
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nice~ |
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https://blog.csdn.net/zhh763984017/article/details/135491600 |
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nice~ |
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nice~ |
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https://blog.csdn.net/weixin_48396757/article/details/135494823?spm=1001.2014.3001.5501 |
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nice~ |
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https://github.com/ykuang/internlm-tutorial/blob/main/notes/lesson1.md |
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nice~ |
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书生·浦语大模型系列 轻量级：InternLM-7B
数据：书生·万卷 文本数据：50亿个文档，数据量超1TB 高可扩展：支持从8卡到千卡训练，千卡加速效率达92%。 增量续训 全链路开源开放体系|微调 OpenCompass评测体系 全链路开源开放体系|部署 大语言模型特点 低存储设备：消费级显卡、移动端等如何部署？ 如何加速token的生成速度 提升系统整体吞吐量 全链路开源开放体系|智能体 轻量级智能体Lagent 多模态智能体工具箱AgentLego 丰富的工具集合，尤其是提供了大量视觉、多模态相关领域的前沿算法功能 |
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nice~ |
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第一节笔记 1609 |
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nice~ |
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nice~ |
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nice~ |
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https://cewmxvz60v.feishu.cn/wiki/K5tDwibmwiAsWckr9cpcZAipnwc |
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https://blog.csdn.net/2301_80135859/article/details/135540106 |
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哇~ 这个手写笔记我觉得也很nice~ |
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第一课笔记＿Wade |
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不要把讲稿识别出来了当笔记哈~ |
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OK |
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第一节课笔记 |
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nice~ |
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nice~ 你的博客网站好酷哈哈哈啊~ |
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请上传第1节课的笔记在此处~
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