Hey there!
My email is [my-github-handle]@gmail.com
My linkedin: https://www.linkedin.com/in/danielhanchen/
Our goal is to make AI accessible to everyone, and make LLMs able to plan and reason!
Name: Daniel Han
Type: User
Bio: Unsloth - 2x faster 70% less VRAM finetuning Llama-3.1, Mistral, Gemma-2, Phi-3
Twitter: danielhanchen
Location: San Francisco
Hey there!
My email is [my-github-handle]@gmail.com
My linkedin: https://www.linkedin.com/in/danielhanchen/
Our goal is to make AI accessible to everyone, and make LLMs able to plan and reason!
Project Aeros aims to optimise data science processes in Python, and provides the user with easy to use functions and APIs
Data Science Society of UNSW Lecture Series
A PyTorch Extension: Tools for easy mixed precision and distributed training in Pytorch
efficient arrays of booleans for Python
BLAS-like Library Instantiation Software Framework
Finetuning LLaMA with cleaned datasets
cuML - RAPIDS Machine Learning Library
NumPy-like API accelerated with CUDA
A collection of machine learning notes
The official PyTorch implementation of Google's Gemma models
We used NSW Air Quality data, health data, transport OPAL congestion data and also some novel disease spreading algorithms to try model and find correlations between Air Quality, Congestion and Influenza rates.
We developed a data mining solution to scrape data from multiple sources to get the latest and greatest information about health grants. AND - we designed a cool Jupyter Notebook Solution to visualise our 2nd challenge- to get data constantly from a source, and show it in Plotly
LLM inference in C/C++
Llama 2 Everywhere (L2E)
I noticed that traditional methods to predict a disease outbreak was by performing sentiment analysis on Twitter posts and Google Search terms. Unfortunately, these methods were inadequate, as Twitter and Google is not popular in all countries. So, I created a system to model and predict outbreaks without the need for social media. The system was able to update the probabilities of a virus from spreading from A to B in real time, and I plan to release it to the public next year. I also used Machine Learning and Deep Learning to predict larger long-term virus trends with Google Trends, and this acted as a validator for the MSIRD model.
We created a new Health Rating system that aggregated data from QANTAS, ABS, Victoria Health and supermarket data to reveal correlations between them. We got Special Commendations for our great work!
Created a Machine Learning system to predict if someone is going to suicide, and proposed some mechanisms to counter and help potential victims.
We're creating a full Earth simulation to predict the future of everything and make JARVIS a reality!
RAPIDS Sample Notebooks
🤗 PEFT: State-of-the-art Parameter-Efficient Fine-Tuning.
Fast, flexible and easy to use probabilistic modelling in Python.
Convert HTML to Markdown
Sometimes we want to ”reverse” a Markov Chain process. Taking the inverse of the transition matrix allows this to work, but the inverse result is not a transition matrix. If I wanted to model a population going to work, and then going back home, negative and greater than 1 probabilities in the inverse matrix will cause issues. I propose a method to compute the ”inverse” of a transition matrix, and the result is still a transition matrix
sciblox - Easier Data Science and Machine Learning
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.