Yes actually. Put yourself in an immersive environment on the beach or something, pull up a huge monitor to type code, pull up another monitor next to it to monitor the output, have another monitor to your left with your music player. I think you could get into a flow state and
CODE
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Build Chat PDF App with LangChain OpenAI Streamlit
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Build a Chat PDF app in Python with LangChain, OpenAI, and Streamlit In this tutorial, you'll learn how to build a project by using Langchain and Streamlit to develop GUI-based ChatGPT for your PDF documents. Code: https://
github.com/strongSoda/cha
t-with-pdf-tutorial
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Evaluating LLM Applications: LangSmith Features
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"Is my LLM application getting better or worse over time?" This is one of the biggest questions that AI engineers struggle with. Evaluation of LLM application is still quite difficult. We've recently released two features in LangSmith to make this easier First, to help
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AI breakthroughs emerge from hands-on experimentation in Jupyter notebooks
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Important breakthroughs in AI will come from tinkering around with ideas and models on Jupyter notebooks.
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IPAdapters ComfyUI Tutorial Guide for Beginners
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If you haven’t played with IPAdapters much, @cubiq
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RAG on User Account Data for Personalized AI Systems
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Slick! Do they also RAG on user specific account data to many it even more personalized?
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MLX LoRA Fine-tuning Guide for Language Models
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https://
github.com/ml-explore/mlx
-examples/blob/main/llms/mlx_lm/LORA.md
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SymbolicAI: Logic-Based Generative Models and Solvers
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SymbolicAI: A framework for logic-based approaches combining generative models and solvers Dinu et al.: https://
arxiv.org/abs/2402.00854 #ArtificialIntelligence #DeepLearning #MachineLearning -
Database Adaptation for MLX LoRA Implementation
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I took the original database and adjusted it using a Python code so that it could fit the format required by MLX. You can see all the information here. https://
github.com/ml-explore/mlx
-examples/tree/main/lora
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OLMo: Accelerating the Science of Language Models
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OLMo: Accelerating the Science of Language Models Groeneveld et al.: https://
arxiv.org/abs/2402.00838 #ArtificialIntelligence #DeepLearning #MachineLearning