Yudi Xue (binarycrayon)

binarycrayon

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Location:Canada

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argoproj-labs

Yudi Xue's repositories

altair

Declarative statistical visualization library for Python

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alignment-handbook

Robust recipes for to align language models with human and AI preferences

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argo-client-python

Python client for Argo Workflows

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argo-python-dsl

Python DSL for Argo Workflows

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axolotl

Go ahead and axolotl questions

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BlenderGPT

Use commands in English to control Blender with OpenAI's GPT-4

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cheatsheets

Collection of Dash cheat sheets

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clozurecl-dash

Clozure CL docs for Dash.app

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cookiecutter-pypackage

Cookiecutter template for a Python package.

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Dash-User-Contributions

Dash repo of user contributed docsets

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django-rest-framework

Web APIs for Django.

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emacs.d

An Emacs configuration bundle with batteries included

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ForgeryPy

An easy to use forged data generator for Python

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gen

Common generator scripts for all client libraries

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lisp-koans

Common Lisp Koans is a language learning exercise in the same vein as the ruby koans, python koans and others. It is a port of the prior koans with some modifications to highlight lisp-specific features. Structured as ordered groups of broken unit tests, the project guides the learner progressively through many Common Lisp language features.

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lit-llama

Implementation of the LLaMA language model based on nanoGPT. Supports flash attention, Int8 and GPTQ 4bit quantization, LoRA and LLaMA-Adapter fine-tuning, pre-training. Apache 2.0-licensed.

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llm-env-bootstrap

Simplified installers forked from one-click-installers with modifications

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prelude

Prelude is an enhanced Emacs 24 distribution that should make your experience with Emacs both more pleasant and more powerful.

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pytudes

Python programs to practice or demonstrate skills.

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setupmac

Setup mac with ansible

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spacemacs

A community-driven Emacs distribution - The best editor is neither Emacs nor Vim, it's Emacs *and* Vim!

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spacemacs-dash

Spacemacs doc for Dash.app

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tensorflow

Computation using data flow graphs for scalable machine learning

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YOW2017

bibliography for my talk, Machines that learn through action, from YOW! 2017

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