This project, `autoresearch-opencode`, is an autonomous experiment loop designed for use with OpenCode. It's a port of `pi-autoresearch`, but implemented as a pure skill, eliminating the need for an MCP server and relying solely on instructions the agent follows using its built-in tools. The skill allows users to automate optimization tasks, as demonstrated by the example of optimizing the BogoSort algorithm which achieved a 7,802x speedup by leveraging Python's `bisect` module for sorted-state detection.
The system maintains state using a JSONL file, enabling resume/pause functionality and detailed experiment tracking. It provides a dashboard for monitoring progress and ensures data integrity through atomic writes and validation checks.
The system maintains state using a JSONL file, enabling resume/pause functionality and detailed experiment tracking. It provides a dashboard for monitoring progress and ensures data integrity through atomic writes and validation checks.
A 12-week, 26-lesson curriculum all about Machine Learning, using primarily Scikit-learn and avoiding deep learning.
The `mlabonne/llm-course` GitHub page offers a comprehensive LLM education in three parts: **Fundamentals** (optional math/Python/NN basics), **LLM Scientist** (building LLMs – architecture, training, alignment, evaluation, optimization), and **LLM Engineer** (applying LLMs – deployment, RAG, agents, security). It’s a detailed syllabus with extensive resources for learning the entire LLM lifecycle, from theory to practical application.
The `mlabonne/llm-course` GitHub page offers a comprehensive LLM education in three parts: **Fundamentals** (optional math/Python/NN basics), **LLM Scientist** (building LLMs – architecture, training, alignment, evaluation, optimization), and **LLM Engineer** (applying LLMs – deployment, RAG, agents, security). It’s a detailed syllabus with extensive resources for learning the entire LLM lifecycle, from theory to practical application.
This notebook provides an introduction to Naive Bayes classification, covering concepts, formulas, and implementation.
A curated collection of Awesome LLM apps built with RAG, AI Agents, Multi-agent Teams, MCP, Voice Agents, and more. This repository features LLM apps that use models from OpenAI, Anthropic, Google, xAI and open-source models like Qwen or Llama.
A machine learning library for unsupervised time series anomaly detection. Orion provides verified ML pipelines to identify rare patterns in time series data.
This is a GitHub repository for a Reinforcement Learning Tic Tac Toe project. It contains a single Python file, TicTacToeRL.py. The repository has 0 stars and 0 forks as of the current data.
This repository provides an overview of resources for the paper 's1: Simple test-time scaling', which includes minimal recipes for test-time scaling and strong reasoning performance. It covers artifacts, structure, inference, training, evaluation, data, visuals, and citation details.
History-based Feature Selection (HBFS) is a feature selection tool that aims to identify an optimal subset of features for prediction problems. It is designed to work similarly to wrapper methods and genetic methods, focusing on selecting feature subsets that yield the highest performance for a given dataset and target. HBFS differs from filter methods, which evaluate and rank individual features based on their predictive power. Instead, HBFS evaluates combinations of features over multiple iterations, using a Random Forest regressor to estimate performance and iteratively refining feature sets. This tool supports binary and multiclass classification, as well as regression, and allows for balancing the trade-off between maximizing accuracy and minimizing the number of features through parameters such as maximum features and penalties. Examples provided demonstrate the use of HBFS with various models and metrics, showcasing its ability to improve model performance by identifying optimal feature subsets.
A Github Gist containing a Python script for text classification using the TxTail API
PygmalionAI's large-scale inference engine designed for serving Pygmalion models to a large number of users with blazing fast speeds. Integrates work from projects like vLLM, TensorRT-LLM, xFormers, AutoAWQ, AutoGPTQ, SqueezeLLM, Exllamav2, TabbyAPI, AQLM, KoboldAI, Text Generation WebUI, and Megatron-LM.
