10 minutes? Pretty doable actually, if the data is ready. Spin up a Runpod machine. While that’s starting, upload dataset to huggingface. Then write a yaml for the run config (Axolotl) and upload to Gist. By that time the machine is ready. Run this command: pip uninstall -y
CODE
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Upcoming open-source project launch announcement
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May do this next week! Will open-source of course
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LangChain Chatchat: Open Source Local Q&A Application
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LangChain Chatchat A local Q&A application. The goal is to build a KBQA(Knowledge based Q&A) solution that is friendly to Chinese scenarios and open source models and can run both offline and online. 25k stars on Github! https://
github.com/chatchat-space
/Langchain-Chatchat/blob/master/README_en.md
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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://
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Axolotl and Together Compute for Model Fine-tuning
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I use Axolotl, but services like @togethercompute might be easier. Last I checked, they don’t support only training on outputs, which is important. May have changed since!
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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
