作者: indignant 時間: 2025-3-21 22:51
Book 2024hould grasp after understanding generative AI models. The next chapters cover databases, caching, monitoring, etc., which are the topics necessary to build larger-scale applications. Real-world examples using these models and tools are included..By the end of this book, you should be able to build e作者: certitude 時間: 2025-3-22 01:53 作者: essential-fats 時間: 2025-3-22 07:15 作者: 結(jié)合 時間: 2025-3-22 12:26
Mathematical Location and Land Use Theory as summarization. However, prompt engineering goes beyond this and is increasingly becoming a booming and interesting area . with new research and styles of prompting being proposed regularly. Prompt engineering or becoming a prompt engineer is an emerging but highly relevant role in the new wave of generative AI and AI-powered applications.作者: Heart-Rate 時間: 2025-3-22 14:22
Guardrails and AI: Building Safe + Controllable Apps, your day for you. This agent was able to reason and have access to “the world” via API integrations (the so-called tools). This was a fairly simple application, but it was still autonomous . and when AI is autonomous, there’s always space for things to go wrong if proper safeguards are not in place.作者: constitute 時間: 2025-3-22 18:26 作者: Hemodialysis 時間: 2025-3-22 23:22 作者: 激怒某人 時間: 2025-3-23 04:23 作者: 污穢 時間: 2025-3-23 07:42 作者: inquisitive 時間: 2025-3-23 11:57 作者: municipality 時間: 2025-3-23 17:54 作者: 消耗 時間: 2025-3-23 18:12 作者: Irascible 時間: 2025-3-24 00:24
https://doi.org/10.1007/978-3-540-24785-2bot that answered your questions . could remember the rest of your conversation. This allowed the LLM to become “smarter” by getting context from history. Your chatbot also had access to up-to-date, personal information via a vector database, meaning it was able to answer questions beyond what it wa作者: Repetitions 時間: 2025-3-24 05:59
https://doi.org/10.1007/978-3-540-24785-2 your day for you. This agent was able to reason and have access to “the world” via API integrations (the so-called tools). This was a fairly simple application, but it was still autonomous . and when AI is autonomous, there’s always space for things to go wrong if proper safeguards are not in place作者: Overthrow 時間: 2025-3-24 07:35
https://doi.org/10.1007/978-3-540-24785-2rdrails around ensuring your LLM stays on topic, executes the right flow, and is able to block users. You looked into NeMo and understood how it combines LLMs, Colang, and embedding models to create a generalized set of rules, based on natural language rules you give it.作者: GLARE 時間: 2025-3-24 12:52
Mathematical Location and Land Use Theoryn models. You learned about the whys, whats, and hows of fine-tuning. You learned that fine-tuning can be less resource and time consuming than building and training a model from scratch. The previous chapter talked to you about what happens to the neural network during the fine-tuning process . spe作者: palliate 時間: 2025-3-24 18:26
Mathematical Location and Land Use Theory as summarization. However, prompt engineering goes beyond this and is increasingly becoming a booming and interesting area . with new research and styles of prompting being proposed regularly. Prompt engineering or becoming a prompt engineer is an emerging but highly relevant role in the new wave o作者: 裂隙 時間: 2025-3-24 20:48 作者: COUCH 時間: 2025-3-25 02:01
Monitoring,In Chapter 6, you learned how to fine-tune Llama 2 with using LoRA, a technique to make your model knowledgeable in a new domain, one it hasn’t specifically been trained on.作者: 旁觀者 時間: 2025-3-25 03:21
Introduction to Generative AI,and at some point, a manager is probably going to ask you “can we do generative AI too?” or you’re going to get tempted and hack together an LLM-powered bot at 2 a.m. This chapter introduces you, a software engineer, to the booming world of AI, by cutting through all the hype and demystifying AI. I 作者: Cpr951 時間: 2025-3-25 08:32
LangChain: Your Swiss Army Knife,apter introduces you to LangChain, your Swiss Army knife to building robust applications on top of LLMs and other models. As you build applications beyond just making API calls, you’re going to need various components to connect a model to your own data, to external data, and services, and that’s wh作者: 多產(chǎn)子 時間: 2025-3-25 13:04
Chains, Tools and Agents,bot that answered your questions . could remember the rest of your conversation. This allowed the LLM to become “smarter” by getting context from history. Your chatbot also had access to up-to-date, personal information via a vector database, meaning it was able to answer questions beyond what it wa作者: 規(guī)范要多 時間: 2025-3-25 15:58
Guardrails and AI: Building Safe + Controllable Apps, your day for you. This agent was able to reason and have access to “the world” via API integrations (the so-called tools). This was a fairly simple application, but it was still autonomous . and when AI is autonomous, there’s always space for things to go wrong if proper safeguards are not in place作者: 發(fā)微光 時間: 2025-3-25 20:44
Finetuning: The Theory,rdrails around ensuring your LLM stays on topic, executes the right flow, and is able to block users. You looked into NeMo and understood how it combines LLMs, Colang, and embedding models to create a generalized set of rules, based on natural language rules you give it.作者: BILK 時間: 2025-3-26 03:27
Finetuning: Hands on,n models. You learned about the whys, whats, and hows of fine-tuning. You learned that fine-tuning can be less resource and time consuming than building and training a model from scratch. The previous chapter talked to you about what happens to the neural network during the fine-tuning process . spe作者: invulnerable 時間: 2025-3-26 06:05 作者: 背書 時間: 2025-3-26 11:32 作者: Outspoken 時間: 2025-3-26 14:16 作者: GLOOM 時間: 2025-3-26 18:16
https://doi.org/10.1007/978-3-540-24785-2ory. Your chatbot also had access to up-to-date, personal information via a vector database, meaning it was able to answer questions beyond what it was trained on. This also helped prevent hallucination.作者: 分離 時間: 2025-3-26 21:02 作者: 緊張過度 時間: 2025-3-27 05:02
Chains, Tools and Agents,ory. Your chatbot also had access to up-to-date, personal information via a vector database, meaning it was able to answer questions beyond what it was trained on. This also helped prevent hallucination.作者: Jacket 時間: 2025-3-27 06:36 作者: 冷淡周邊 時間: 2025-3-27 09:38 作者: 珊瑚 時間: 2025-3-27 14:17 作者: SHOCK 時間: 2025-3-27 21:23
Mathematical Location and Land Use Theorycifically that most layers are “frozen” and the final few layers are updated to adapt the model to a new task. The focus was on Reinforcement Learning with Human Feedback (RLHF) and Parameter-Efficient Fine-Tuning (PEFT).作者: dagger 時間: 2025-3-28 01:35
Finetuning: Hands on,cifically that most layers are “frozen” and the final few layers are updated to adapt the model to a new task. The focus was on Reinforcement Learning with Human Feedback (RLHF) and Parameter-Efficient Fine-Tuning (PEFT).作者: 不如樂死去 時間: 2025-3-28 04:39
10樓作者: garrulous 時間: 2025-3-28 08:18
10樓作者: 粗魯性質(zhì) 時間: 2025-3-28 13:05
10樓作者: 我邪惡 時間: 2025-3-28 17:31
10樓
