This article introduces Scikit-LLM, a Python library that integrates large language models like OpenAI's GPT with the Scikit-learn framework to simplify text analysis tasks. It explains and demonstrates two primary classification methods: zero-shot classification, which assigns labels based solely on the model's general knowledge without prior examples, and few-shot classification, which uses a small set of labeled examples within the prompt to improve accuracy. By following a Scikit-learn-style workflow using fit() and predict() methods, users can easily implement these advanced NLP techniques for tasks such as sentiment analysis and topic labeling.
This article demonstrates how to perform text summarization using the scikit-llm library, which provides a simple interface for utilizing large language models within a scikit-learn style workflow. The guide walks through installing the necessary dependencies and implementing both extractive and abstractive summarization techniques on sample text data.
Key topics include:
- Introduction to the scikit-llm library
- Implementing abstractive summarization using LLMs
- Using scikit-llm for text classification and clustering tasks
- Practical code examples for integrating LLM capabilities into machine learning pipelines
Key topics include:
- Introduction to the scikit-llm library
- Implementing abstractive summarization using LLMs
- Using scikit-llm for text classification and clustering tasks
- Practical code examples for integrating LLM capabilities into machine learning pipelines
OpenMythos is an open-source PyTorch project by Kye Gomez that proposes a theoretical reconstruction of Anthropic's Claude Mythos architecture. Instead of standard transformer layers, it suggests a Recurrent-Depth Transformer (RDT) design where weights loop through multiple iterations to increase reasoning depth during inference. By combining Mixture-of-Experts with Multi-Latent Attention and stability constraints, the model achieves performance parity between 770M parameters and a 1.3B parameter standard transformer.
* open-source PyTorch reconstruction of claude mythos
* proposes recurrent-depth transformer architecture
* reasoning depth scales via inference-time loops rather than parameter count
* uses mixture-of-experts for domain breadth
* implements multi-latent attention to reduce memory usage
* employs lti injection and adaptive computation time for stability
* achieves 1.3b parameter performance with only 770m parameters
* open-source PyTorch reconstruction of claude mythos
* proposes recurrent-depth transformer architecture
* reasoning depth scales via inference-time loops rather than parameter count
* uses mixture-of-experts for domain breadth
* implements multi-latent attention to reduce memory usage
* employs lti injection and adaptive computation time for stability
* achieves 1.3b parameter performance with only 770m parameters
Mounia Lalmas, a Senior Director of Research and Head of Tech Research at Spotify, has been appointed an honorary doctorate at the University of Gothenburg's Faculty of Science and Technology. An expert in engagement and recommendation systems, Lalmas discusses her work bridging the gap between academic research and large-scale industrial application.
These working notes by Russ Tedrake cover nonlinear dynamics and control with a specific focus on mechanical systems. The material explores how to achieve robust, efficient, and graceful robot movement through the integration of mechanical design, passive dynamics, and nonlinear control synthesis. Rather than relying solely on model-free approaches, the text emphasizes using the underlying structure of dynamical equations to develop more data-efficient and robust algorithms via optimization and machine learning.
Main topics include:
* Model systems such as pendulums, acrobots, cart-poles, and quadrotors
* Simple models of walking and running dynamics
* Nonlinear planning and control using trajectory optimization and LQR
* Lyapunov analysis for stability and reachability
* Estimation techniques including Kalman filters and Bayesian methods
* Learning-based approaches such as imitation learning, policy search, and system identification
* Contact-implicit trajectory optimization and hybrid systems
Main topics include:
* Model systems such as pendulums, acrobots, cart-poles, and quadrotors
* Simple models of walking and running dynamics
* Nonlinear planning and control using trajectory optimization and LQR
* Lyapunov analysis for stability and reachability
* Estimation techniques including Kalman filters and Bayesian methods
* Learning-based approaches such as imitation learning, policy search, and system identification
* Contact-implicit trajectory optimization and hybrid systems
Personal website of Alex L. Zhang, a PhD student at MIT CSAIL focusing on the efficiency and utilization of language models. His research spans ML systems, language model benchmarks, and specialized model development.
Key areas of work include:
- Recursive Language Models (RLMs) and Project Popcorn
- GPU programming competitions via KernelBot and GPU MODE
- Benchmarking capabilities through VideoGameBench and KernelBench
- Development of models like Neo-1 and KernelLLM-8B
Key areas of work include:
- Recursive Language Models (RLMs) and Project Popcorn
- GPU programming competitions via KernelBot and GPU MODE
- Benchmarking capabilities through VideoGameBench and KernelBench
- Development of models like Neo-1 and KernelLLM-8B
Personal website of Jamie Simon, a scientist specializing in fundamental theory for deep learning. He runs a research lab at the Redwood Center at UC Berkeley with funding from Imbue and recently completed his PhD under Mike DeWeese. The site serves as a hub for his scientific research, personal blog posts regarding science and life adventures, and custom-made puzzles.
Main topics:
* Deep learning fundamental theory
* Research publications
* Science and lifestyle blog
* Puzzle creation
Main topics:
* Deep learning fundamental theory
* Research publications
* Science and lifestyle blog
* Puzzle creation
A practical pipeline for classifying messy free-text data into meaningful categories using a locally hosted LLM, no labeled training data required.
Learn how to label text without the need for task-specific training data by using zero-shot text classification. This guide explains how pretrained transformer models, such as BART, reframe classification as a reasoning task where labels are treated as natural language statements.
Key topics include:
* The core concept of zero-shot classification and its advantages for rapid prototyping.
* Using the Hugging Face transformers pipeline with the facebook/bart-large-mnli model.
* Implementing multi-label classification for texts belonging to multiple categories.
* Improving accuracy through custom hypothesis template tuning and clear label wording.
Key topics include:
* The core concept of zero-shot classification and its advantages for rapid prototyping.
* Using the Hugging Face transformers pipeline with the facebook/bart-large-mnli model.
* Implementing multi-label classification for texts belonging to multiple categories.
* Improving accuracy through custom hypothesis template tuning and clear label wording.
A comprehensive curated collection of Large Language Model (LLM) architecture figures and technical fact sheets. This gallery provides a visual and data-driven overview of modern model designs, ranging from classic dense architectures like GPT-2 to advanced sparse Mixture-of-Experts (MoE) systems and hybrid attention models. Users can explore detailed specifications including parameter scales, context windows, attention mechanisms, and intelligence indices for various prominent models.
Key features include:
* Detailed architecture fact sheets for a wide array of models such as Llama, DeepSeek, Qwen, Gemma, and Mistral.
* An architecture diff tool to compare two different model designs side-by-side.
* Comparative analysis across dense, MoE, MLA, and hybrid decoder families.
* Links to original source articles and technical reports for deeper research.
Key features include:
* Detailed architecture fact sheets for a wide array of models such as Llama, DeepSeek, Qwen, Gemma, and Mistral.
* An architecture diff tool to compare two different model designs side-by-side.
* Comparative analysis across dense, MoE, MLA, and hybrid decoder families.
* Links to original source articles and technical reports for deeper research.
