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7 steps to master large language models (LLMs) 

Data Science Dojo

Want to build a custom llm application? However, mastering LLMs requires a comprehensive understanding of their underlying principles, architectures, and training techniques. Step 2: Explore LLM architectures LLMs come in various architectures, each with its strengths and limitations.

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Large language models: A complete guide to understanding LLMs

Data Science Dojo

That’s essentially what an LLM is! By analyzing this data, LLMs become experts at recognizing patterns and relationships between words. Some LLMs, like LaMDA by Google AI , can help you brainstorm ideas and even write different creative text formats based on your initial input. Read more about it here.

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Harnessing LLM chatbots: Real-life applications, building techniques and LangChain’s Finetuning

Data Science Dojo

The next generation of Language Model Systems (LLMs) and LLM chatbots are expected to offer improved accuracy, expanded language support, enhanced computational efficiency, and seamless integration with emerging technologies. To overcome these challenges, Large Language Models (LLMs) come to the rescue.

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Top 7 software development use cases of Generative AI

Data Science Dojo

Code generation: OpenAI’s large language models can be used to generate code snippets, complete code, and even write entire applications. For example: Prompt: If you ask ChatGPT to “generate a function that takes a list of numbers and returns the sum of the even numbers,” it will generate the following Python code: 2.

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Breaking GPT-4 Safety: Pyromaniac Edition

Towards AI

I experimented with breaking LLM safety. In recent years, Large Language Models (LLMs) have revolutionized various industries, from natural language processing to creative writing and customer service. The following list is not comprehensive, but it… Read the full blog for free on Medium.

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This AI newsletter is all you need #98

Towards AI

What happened this week in AI by Louie This week, we were again watching developments in AI-powered robots and LLMs. A collaboration between Nvidia and UPenn released DrEureka, a new open-source model that uses an LLM agent to write code for training robots in simulation and then write further code to transfer to real-world deployment.

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Build a Customer Support Bot in 20 Minutes With Tanuki + GPT4

Towards AI

In addition, LLMs in zero-shot fashion are known to be misaligned (language models may understand the task differently) and produce hallucinations. To counter this, we have created Tanuki, a simple way to create aligned LLM-powered applications and ensure structured and typed outputs.

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