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blog.daniemon.com | ||
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blog.pamelafox.org
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| | | | | Recently, as part of my work on Azure OpenAI code samples, I've been experimenting with different ways of streaming data from a server i... | |
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til.simonwillison.net
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| | | | | Here's the pattern I figured out for using the openai Python library to extract structured data from text using a single call to the model. | |
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isthisit.nz
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| | | | | August 2024 Update: Now a solved problem. Use Structured Outputs. Large language models (LLMs) return unstructured output. When we prompt them they respond with one large string. This is fine for applications such as ChatGPT, but in others where we want the LLM to return structured data such as lists or key value pairs, a parseable response is needed. In Building A ChatGPT-enhanced Python REPL I used a technique to prompt the LLM to return output in a text format I could parse. | |
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amatria.in
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| | | [AI summary] The provided text is an extensive overview of various large language models (LLMs) and their architectures, training tasks, and applications. It includes detailed descriptions of models like GPT, T5, BERT, and others, along with their pre-training objectives, parameter counts, and specific use cases. The text also references key research papers, surveys, and resources for further reading on LLMs and related topics. | ||
