JZhou3083

JZhou's starred repositories

ollama

Get up and running with Llama 3.1, Mistral, Gemma 2, and other large language models.

annotated_deep_learning_paper_implementations

🧑‍🏫 60 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠

Language:PythonMIT52447 436 130

nanoGPT

The simplest, fastest repository for training/finetuning medium-sized GPTs.

Language:PythonMIT35342 360 307

llm-course

Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.

Language:Jupyter NotebookApache-2.035058 364 65

OpenBBTerminal

Investment Research for Everyone, Everywhere.

Language:PythonNOASSERTION26621 258 2032

tuning_playbook

A playbook for systematically maximizing the performance of deep learning models.

NOASSERTION26049 282 39

minGPT

A minimal PyTorch re-implementation of the OpenAI GPT (Generative Pretrained Transformer) training

Language:PythonMIT19594 254 72

CnC_Remastered_Collection

Language:C++NOASSERTION18299 524 99

continue

⏩ Continue is the leading open-source AI code assistant. You can connect any models and any context to build custom autocomplete and chat experiences inside VS Code and JetBrains

Language:TypeScriptApache-2.013978 70 1214

minbpe

Minimal, clean code for the Byte Pair Encoding (BPE) algorithm commonly used in LLM tokenization.

Language:PythonMIT8833 82 36

easy-rl

强化学习中文教程（蘑菇书🍄），在线阅读地址：https://datawhalechina.github.io/easy-rl/

Language:Jupyter NotebookNOASSERTION8797 79 141

espnet

End-to-End Speech Processing Toolkit

Language:PythonApache-2.08182 178 2337

team-learning

主要展示Datawhale的组队学习计划。

2189 119 18

peerdb

Fast, Simple and a cost effective tool to replicate data from Postgres to Data Warehouses, Queues and Storage

Language:GoNOASSERTION2026 14 324

End-to-end-Autonomous-Driving

[IEEE T-PAMI] All you need for End-to-end Autonomous Driving

MIT1842 51 1

Cradle

The Cradle framework is a first attempt at General Computer Control (GCC). Cradle supports agents to ace any computer task by enabling strong reasoning abilities, self-improvment, and skill curation, in a standardized general environment with minimal requirements.

Language:PythonMIT1482 19 17

visualblocks

Visual Blocks for ML is a Google visual programming framework that lets you create ML pipelines in a no-code graph editor. You – and your users – can quickly prototype workflows by connecting drag-and-drop ML components, including models, user inputs, processors, and visualizations.

Language:PythonApache-2.01138 20 8

evolutionary-model-merge

Official repository of Evolutionary Optimization of Model Merging Recipes

Language:PythonApache-2.01125 39 11

torch-template-for-deep-learning

Pytorch Implementations of large number classical backbone CNNs, data enhancement, torch loss, attention, visualization and some common algorithms.

Language:PythonApache-2.01005 8 2

leetcode-notes

🐳 LeetCode 算法笔记：面试、刷题、学算法。在线阅读地址：https://datawhalechina.github.io/leetcode-notes/

NOASSERTION642 4 321

pyan

pyan is a Python module that performs static analysis of Python code to determine a call dependency graph between functions and methods. This is different from running the code and seeing which functions are called and how often; there are various tools that will generate a call graph in that way, usually using debugger or profiling trace hooks - for example: https://pycallgraph.readthedocs.org/ This code was originally written by Edmund Horner, and then modified by Juha Jeronen. See README for the original blog posts and links to their repositories.

Language:PythonGPL-2.0618 160