inquisitive
发表于 2025-3-23 11:57:43
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municipality
发表于 2025-3-23 17:54:26
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消耗
发表于 2025-3-23 18:12:26
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Irascible
发表于 2025-3-24 00:24:03
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
Repetitions
发表于 2025-3-24 05:59:48
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
Overthrow
发表于 2025-3-24 07:35:16
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.
GLARE
发表于 2025-3-24 12:52:55
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
palliate
发表于 2025-3-24 18:26:23
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
裂隙
发表于 2025-3-24 20:48:37
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COUCH
发表于 2025-3-25 02:01:20
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.