Really good data with that style. You don’t need a ton of it (few hundred examples is totally fine), just make sure it’s really high-quality.
DATA
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Axolotl and Mistral for Synthetic Data Generation
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Axolotl. Fucking awesome. Shoutout to @winglian
. For synthetic data, Mistral models are great. Pair that with some real-world data for grounding/diversity/inspiration for good results. @csahil28 is building an even better solution here. Costs depend on the task at hand. Can be -
Fine-tuning Open-ended LLM Agents: Challenges and Personality Experiments
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The most fun, but frustrating to do (due to data challenges) are open-ended agent fine-tunes. Easier ones are more chat/tool-use focused. A really fun one was experimenting with changing a LLM’s personality significantly. Made it “sassy” lol
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Working with Machine Learning Models in Data Science
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Working with #MachineLearning model
by @DataScienceDojo #BigData #MachineLearning #ArtificialIntelligence #ML #MI #DataScience cc: @pbalakrishnarao @rtehrani @pascal_bornet -
Databricks Community Blogs: RAG, Data Intelligence, Unity Catalog
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The Databricks Community blogs are on — with active discussions on: RAG applications Data intelligence platforms #UnityCatalog
…and so much more! Dive into the #data with a vibrant group of practitioners from around the world https://
bit.ly/3NrXhxV -
Advanced RAG: Generation and Evaluation Techniques
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Advanced RAG Series: Generation and Evaluation In the fifth part of this series, we look at techniques for: Generation (CRAG, Self-RAG, RRR) Evaluation (RAGAs, Langsmith, DeepEval) Another awesome blog by @divyanshu_van https://
div.beehiiv.com/p/advanced-rag
-series-generation-evaluation
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Top 6 Data Management Patterns for Effective Data Handling
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How to manage data — here are top 6 #DataManagement patterns!
— Kirk Borne (@KirkDBorne) 23 mars 2024
Source: https://t.co/aO2Ln0CBXG pic.twitter.com/VWTA1iVFCcHow to manage data — here are top 6 #DataManagement patterns! Source: https://
blog.bytebytego.com -
Building RAG Systems for Powerful Data Access
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How to Make a RAG System to Gain Powerful Access to Your Data This article is a great introduction to RAG, and walks through a few important steps of a RAG pipeline: Retrieve Data
Pre-process Data
Implement RAG
Test https://
towardsdatascience.com/how-to-make-a-
rag-system-to-gain-powerful-access-to-your-data-caf4bb9186ea
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That’s a Wrap: Daily Python, Data Science, and ML Content
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That's a wrap! Every day, I share and simply content around Python, Data Science, Machine Learning & Large Language Models. Find me → @Sumanth_077 Like/RT the first tweet and help this reach more people.
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AI Integration Simplifies MLOps and ETL Pipeline Complexity
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By bringing AI directly to data developers and organizations can avoid the complexity of building and integrating MLOps and ETL pipelines as well as migrating and duplicating data across multiple environments. Check this out: https://
superduperdb.com