large language model – Page 4 – Andrew Fairless, Ph.D.

What I Read: What We Learned Building LLMs

By Andrew Fairless on August 19, 2024June 4, 2024

https://www.oreilly.com/radar/what-we-learned-from-a-year-of-building-with-llms-part-i What We Learned from a Year of Building with LLMs (Part I)By Eugene Yan, Bryan Bischof, Charles Frye, Hamel Husain, Jason Liu and Shreya ShankarMay 28, 2024 “We’ve identifiedContinue readingWhat I Read: What We Learned Building LLMs

What I Read: Merge Large Language Models

By Andrew Fairless on August 15, 2024June 4, 2024

https://huggingface.co/blog/mlabonne/merge-models Merge Large Language Models with mergekitJanuary 9, 2024Maxime Labonne “Model merging is a technique that combines two or more LLMs into a single model. It’s a relatively new andContinue readingWhat I Read: Merge Large Language Models

What I Read: LLM Pipelines, DSPy

By Andrew Fairless on August 13, 2024May 25, 2024

https://www.databricks.com/blog/optimizing-databricks-llm-pipelines-dspy Optimizing Databricks LLM Pipelines with DSPyby Arnav Singhvi and Daniel Pechi (JetBlue)May 23, 2024 “The key component of DSPy is self-improving pipelines…. Just as data pipelines and machine learningContinue readingWhat I Read: LLM Pipelines, DSPy

What I Read: LLM evaluation

By Andrew Fairless on August 7, 2024May 25, 2024

https://huggingface.co/blog/clefourrier/llm-evaluation Let’s talk about LLM evaluationMay 23, 2024Clémentine Fourrier “There are, to my knowledge, at the moment, 3 main ways to do evaluation: automated benchmarking, using humans as judges, andContinue readingWhat I Read: LLM evaluation

What I Read: LLM, DSPy Assertions and Suggestions

By Andrew Fairless on August 6, 2024May 25, 2024

https://learnbybuilding.ai/tutorials/guiding-llm-output-with-dspy-assertions-and-suggestions Guiding LLM Output with DSPy Assertions and SuggestionsBill Chambers “Assertions in DSPy allow you to define strict rules and constraints that the LLM’s output must (or maybe that youContinue readingWhat I Read: LLM, DSPy Assertions and Suggestions

What I Read: Platonic Hypothesis

By Andrew Fairless on July 29, 2024May 24, 2024

https://phillipi.github.io/prh The Platonic Representation HypothesisMinyoung Huh, Brian Cheung, Tongzhou Wang, Phillip IsolaMITPosition Paper in ICML 2024 “Neural networks, trained with different objectives on different data and modalities, are converging toContinue readingWhat I Read: Platonic Hypothesis

What I Read: Game Theory, AI

By Andrew Fairless on July 25, 2024May 24, 2024

https://www.quantamagazine.org/game-theory-can-make-ai-more-correct-and-efficient-20240509 Game Theory Can Make AI More Correct and EfficientSteve NadisMay 9, 2024 “Researchers are drawing on ideas from game theory to improve large language models and make them moreContinue readingWhat I Read: Game Theory, AI

What I Read: LLMs, Open Source

By Andrew Fairless on July 15, 2024May 6, 2024

https://www.aidancooper.co.uk/how-to-beat-proprietary-llms How to Beat Proprietary LLMs With Smaller Open Source ModelsAidan CooperApr 26, 2024 “…we explore the unique advantages of open source LLMs, and how you can leverage them toContinue readingWhat I Read: LLMs, Open Source

What I Read: Structured Generation, Constrained Decoding

By Andrew Fairless on June 20, 2024April 16, 2024

https://www.aidancooper.co.uk/constrained-decoding A Guide to Structured Generation Using Constrained DecodingApr 8, 2024 “…there are techniques that ensure language models only return outputs that conform to your requirements. This article serves asContinue readingWhat I Read: Structured Generation, Constrained Decoding

Tag: large language model