RT Avi Chawla (@_avichawla)Finally, Python 3.14 lets you disable GIL!It's a big deal because earlier, even if you wrote multi-threaded code, Python could only run one thread at a time, giving no performance benefit.But now, Python can run your multi-threaded code in parallel.And uv fully supports it! https://t.co/pfqh58En3K ...

Core Feature Update - Python 3.14 allows disabling the Global Interpreter Lock (GIL) [1] - This enables true parallel execution of multi-threaded Python code, improving performance [1] Technology Adoption - uv fully supports the GIL disabling feature in Python 3.14 [1]

RT Avi Chawla (@_avichawla)This should be impossible!You can clean any ML dataset in just three lines of code. Flag outliers, find label errors, and more, across:- Any data (tabular, text, image, etc.)- Any task (classification, entity recognition, etc.)100% open-source, built by MIT researchers. https://t.co/xAaKjK4zIM ...

If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs.Avi Chawla (@_avichawla):This should be impossible!You can clean any ML dataset in just three lines of code. Flag outliers, find label errors, and more, across:- Any data (tabular, text, image, etc.)- Any task (classification, entity recognition, etc.)100% open-source, built by MIT researchers. https://t.co/xAaKjK4zIM ...

Cleanlab's GitHub repo: https://t.co/IiAR1sFSdJ(don't forget to star it ⭐ ) ...

This should be impossible!You can clean any ML dataset in just three lines of code. Flag outliers, find label errors, and more, across:- Any data (tabular, text, image, etc.)- Any task (classification, entity recognition, etc.)100% open-source, built by MIT researchers. https://t.co/xAaKjK4zIM ...

Core Problem of Traditional RAG - Most retrieved chunks in traditional RAG setups do not effectively aid the LLM, leading to increased computational costs, latency, and context processing [1][5] - Classic RAG involves fetching similar chunks from a vector database and directly inputting the retrieved context into the LLM [5] REFRAG Solution by Meta AI - Meta AI's REFRAG introduces a novel approach by compressing and filtering context at a vector level, focusing on relevance [1][2] - REFRAG employs chunk compression, relevance policy (RL-trained), and selective expansion to process only essential information [2] - The process involves encoding documents, finding relevant chunks, using a relevance policy to select chunks, and concatenating token-level representations [3][4] Performance Metrics of REFRAG - REFRAG outperforms LLaMA on 16 RAG benchmarks, demonstrating enhanced performance [5][7] - REFRAG achieves 30.85x faster time-to-first-token, significantly improving processing speed [5][7] - REFRAG handles 16x larger context windows, allowing for more extensive information processing [5][7] - REFRAG utilizes 2-4x fewer tokens, reducing computational resource consumption [5][7] - REFRAG leads to no accuracy loss across RAG, summarization, and multi-turn conversation tasks [7]

Researchers from Meta built a new RAG approach that:- outperforms LLaMA on 16 RAG benchmarks.- has 30.85x faster time-to-first-token.- handles 16x larger context windows.- and it utilizes 2-4x fewer tokens.Here's the core problem with a typical RAG setup that Meta solves:Most of what we retrieve in RAG setups never actually helps the LLM.In classic RAG, when a query arrives:- You encode it into a vector.- Fetch similar chunks from vector DB.- Dump the retrieved context into the LLM.It typically works, but a ...

RT Avi Chawla (@_avichawla)4 must-know model training paradigms for ML engineers: https://t.co/G3KunNYswt ...

4 must-know model training paradigms for ML engineers: https://t.co/G3KunNYswt ...