An in-process analytics database, DuckDB can work with surprisingly large data sets without having to maintain a distributed multiserver system. Best of all? You can analyze data directly from your Python app.
The workflow triggers on push events on the 'master', 'main', and 'fix' branches, and runs on Ubuntu. It installs Make, caches the Cosmocc toolchain, sets up Cosmocc and Ape Loader, builds the project, makes a specific Llamafile, executes the Llama CLI CPU, and more.
Discusses the trends in Large Language Models (LLMs) architecture, including the rise of more GPU, more weights, more tokens, energy-efficient implementations, the role of LLM routers, and the need for better evaluation metrics, faster fine-tuning, and self-tuning.
LlamaFS is a self-organizing file manager that automatically renames and organizes files based on their contents. It supports various file types and even images and audio. It can run in two modes - batch mode and watch mode. In batch mode, LlamaFS suggests a file structure and organizes files. In watch mode, it watches your directory and proactively learns your file organization habits. The project is built on a Python backend and Electron for the frontend.
An article discussing the new open-source project called LlamaFS, a self-organizing file system that utilizes Llama-3, a large language model, to automate and improve the organization of digital files by understanding their context and content.
An article discussing a simple and free way to automate data workflows using Python and GitHub Actions, written by Shaw Talebi.
An article discussing a paper that proposes a new framework, MetRag, for retrieval augmented generation. The framework is designed to improve the performance of large language models in knowledge-intensive tasks.
This article discusses a method for automatically curating high-quality datasets for self-supervised pre-training of machine learning systems. The method involves successive and hierarchical applications of k-means on a large and diverse data repository to obtain clusters that distribute uniformly among data concepts, followed by a hierarchical, balanced sampling step from these clusters. The experiments on three different data domains show that features trained on the automatically curated datasets outperform those trained on uncurated data while being on par or better than ones trained on manually curated data.
This article is part of a series titled ‘LLMs from Scratch’, a complete guide to understanding and building Large Language Models (LLMs). In this article, we discuss the self-attention mechanism and how it is used by transformers to create rich and context-aware transformer embeddings.
The Self-Attention mechanism is used to add context to learned embeddings, which are vectors representing each word in the input sequence. The process involves the following steps:
1. Learned Embeddings: These are the initial vector representations of words, learned during the training phase. The weights matrix, storing the learned embeddings, is stored in the first linear layer of the Transformer architecture.
2. Positional Encoding: This step adds positional information to the learned embeddings. Positional information helps the model understand the order of the words in the input sequence, as transformers process all words in parallel, and without this information, they would lose the order of the words.
3. Self-Attention: The core of the Self-Attention mechanism is to update the learned embeddings with context from the surrounding words in the input sequence. This mechanism determines which words provide context to other words, and this contextual information is used to produce the final contextualized embeddings.
With the addition of profiling to OpenTelemetry, we expect continuous production profiling to hit the mainstream.
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