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Will Thompson's Projects

cv icon cv

Print-friendly, minimalist CV page

deepchem icon deepchem

Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology

deeplearning-nlp-models icon deeplearning-nlp-models

A small, interpretable codebase containing the re-implementation of a few "deep" NLP models in PyTorch. Colab notebooks to run with GPUs. Models: word2vec, CNNs, transformer, gpt.

dna-diffusion icon dna-diffusion

🧬 Understanding the code of life: Generative modeling of regulatory DNA sequences with diffusion probabilistic models 💨

gpt-engineer icon gpt-engineer

Specify what you want it to build, the AI asks for clarification, and then builds it.

jax-triton icon jax-triton

jax-triton contains integrations between JAX and OpenAI Triton

langchain icon langchain

⚡ Building applications with LLMs through composability ⚡

lit-gpt icon lit-gpt

Hackable implementation of state-of-the-art open-source LLMs based on nanoGPT. Supports flash attention, 4-bit and 8-bit quantization, LoRA and LLaMA-Adapter fine-tuning, pre-training. Apache 2.0-licensed.

llm-swarm icon llm-swarm

Manage scalable open LLM inference endpoints in Slurm clusters

llm_steer icon llm_steer

Steer LLM outputs towards a certain topic/subject and enhance response capabilities using activation engineering by adding steering vectors

lora icon lora

Code for loralib, an implementation of "LoRA: Low-Rank Adaptation of Large Language Models"

mesh icon mesh

Mesh TensorFlow: Model Parallelism Made Easier

micrograd icon micrograd

A tiny scalar-valued autograd engine and a neural net library on top of it with PyTorch-like API

mistral-src icon mistral-src

Reference implementation of Mistral AI 7B v0.1 model.

mpire icon mpire

A Python package for easy multiprocessing, but faster than multiprocessing

open_spiel icon open_spiel

OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games.

paxml icon paxml

Pax is a Jax-based machine learning framework for training large scale models. Pax allows for advanced and fully configurable experimentation and parallelization, and has demonstrated industry leading model flop utilization rates.

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