Laknath1996
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Solve puzzles. Improve your pytorch.
Prospective Learning: Learning for a Dynamic Future (NeurIPS 2024)
🐟 A simple theme for Jekyll. Live at https://eliottvincent.github.io/bay/
A library to train, evaluate, interpret, and productionize decision forest models such as Random Forest and Gradient Boosted Decision Trees.
The 100 line AI agent that solves GitHub issues or helps you in your command line. Radically simple, no huge configs, no giant monorepo—but scores >74% on SWE-bench verified!
verl: Volcano Engine Reinforcement Learning for LLMs
A reimplementation of Stable Diffusion 3.5 in pure PyTorch
Minimal reproduction of DeepSeek R1-Zero
Search-R1: An Efficient, Scalable RL Training Framework for Reasoning & Search Engine Calling interleaved LLM based on veRL
Letta is the platform for building stateful agents: AI with advanced memory that can learn and self-improve over time.
Build Real-Time Knowledge Graphs for AI Agents
A version of verl to support diverse tool use
SWE-bench: Can Language Models Resolve Real-world Github Issues?
Official code release for the NeurIPS 2021 article Training for the Future: A Simple Gradient Interpolation Loss to Generalize Along Time
This includes the original implementation of SELF-RAG: Learning to Retrieve, Generate and Critique through self-reflection by Akari Asai, Zeqiu Wu, Yizhong Wang, Avirup Sil, and Hannaneh Hajishirzi.
[NeurIPS 2024] Continuous Temporal Domain Generalization
A training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.
MuJoCo is a physics engine for detailed, efficient rigid body simulations with contacts. mujoco-py allows using MuJoCo from Python 3.
A toolkit for developing and comparing reinforcement learning algorithms.
Genome modeling and design across all domains of life
Build a RAG (Retrieval Augmented Generation) pipeline from scratch and have it all run locally.
GitHub Repo for ICLR 2023 Paper "Temporal Domain Generalization with Drift-Aware Dynamic Neural Networks"
A dataset of atomic wikipedia edits containing insertions and deletions of a contiguous chunk of text in a sentence. This dataset contains ~43 million edits across 8 languages.