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Stanford University
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11:42
(UTC -07:00) - https://cs.stanford.edu/~shirwu
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Language: Python
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Tensors and Dynamic neural networks in Python with strong GPU acceleration
Specify what you want it to build, the AI asks for clarification, and then builds it.
Pretrain, finetune and deploy AI models on multiple GPUs, TPUs with zero code changes.
Open-sourced codes for MiniGPT-4 and MiniGPT-v2 (https://minigpt-4.github.io, https://minigpt-v2.github.io/)
JARVIS, a system to connect LLMs with ML community. Paper: https://arxiv.org/pdf/2303.17580.pdf
Graph Neural Network Library for PyTorch
Best Practices on Recommendation Systems
Multilingual Sentence & Image Embeddings with BERT
DSPy: The framework for programming—not prompting—foundation models
An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
Finetune Llama 3, Mistral, Phi & Gemma LLMs 2-5x faster with 80% less memory
RWKV is an RNN with transformer-level LLM performance. It can be directly trained like a GPT (parallelizable). So it's combining the best of RNN and transformer - great performance, fast inference,…
A little word cloud generator in Python
Code for loralib, an implementation of "LoRA: Low-Rank Adaptation of Large Language Models"
Ongoing research training transformer models at scale
An open source implementation of CLIP.
A Python Library for Outlier and Anomaly Detection, Integrating Classical and Deep Learning Techniques
Implementation of Imagen, Google's Text-to-Image Neural Network, in Pytorch
Pytorch implementation of convolutional neural network visualization techniques
A Python implementation of global optimization with gaussian processes.
A tutorial and implement of disease centered Medical knowledge graph and qa system based on it。知识图谱构建,自动问答,基于kg的自动问答。以疾病为中心的一定规模医药领域知识图谱,并以该知识图谱完成自动问答与分析服务。
Model interpretability and understanding for PyTorch
[NeurIPS 2023] Tree of Thoughts: Deliberate Problem Solving with Large Language Models
Robust recipes to align language models with human and AI preferences
Differentiable architecture search for convolutional and recurrent networks
Official implementation for "Multimodal Chain-of-Thought Reasoning in Language Models" (stay tuned and more will be updated)