vinven7's Stars
rasbt/LLMs-from-scratch
Implementing a ChatGPT-like LLM in PyTorch from scratch, step by step
haotian-liu/LLaVA
[NeurIPS'23 Oral] Visual Instruction Tuning (LLaVA) built towards GPT-4V level capabilities and beyond.
NVIDIA/DeepLearningExamples
State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure.
naklecha/llama3-from-scratch
llama3 implementation one matrix multiplication at a time
openai/tiktoken
tiktoken is a fast BPE tokeniser for use with OpenAI's models.
bentoml/OpenLLM
Run any open-source LLMs, such as Llama 3.1, Gemma, as OpenAI compatible API endpoint in the cloud.
neuml/txtai
đź’ˇ All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows
a16z-infra/companion-app
AI companions with memory: a lightweight stack to create and host your own AI companions
Accenture/AmpliGraph
Python library for Representation Learning on Knowledge Graphs https://docs.ampligraph.org
graph4ai/graph4nlp
Graph4nlp is the library for the easy use of Graph Neural Networks for NLP. Welcome to visit our DLG4NLP website (https://dlg4nlp.github.io/index.html) for various learning resources!
jdagdelen/hyperDB
A hyper-fast local vector database for use with LLM Agents. Now accepting SAFEs at $135M cap.
weihua916/powerful-gnns
How Powerful are Graph Neural Networks?
killiansheriff/LovelyPlots
Matplotlib style sheets to nicely format figures for scientific papers, thesis and presentations while keeping them fully editable in Adobe Illustrator.
plkmo/BERT-Relation-Extraction
PyTorch implementation for "Matching the Blanks: Distributional Similarity for Relation Learning" paper
mnick/scikit-kge
Python library to compute knowledge graph embeddings
jeremyephron/simplegmail
A simple Gmail API client for applications in Python
hwchase17/chroma-langchain
ttrouill/complex
Source code for experiments in the papers "Complex Embeddings for Simple Link Prediction" (ICML 2016) and "Knowledge Graph Completion via Complex Tensor Factorization" (JMLR 2017).
EricLBuehler/xlora
X-LoRA: Mixture of LoRA Experts
wellecks/ntptutorial
Tutorial on neural theorem proving
uclnlp/cqd
Continuous Query Decomposition for Complex Query Answering in Incomplete Knowledge Graphs
nicknochnack/StreamlitTitanic
tigergraph/graph-ml-notebooks
bradenmacdonald/neolace
The next-generation knowledge graph platform.
ShuHuang/batterybert
BatteryBERT: A Pre-trained Language Model for Battery Database Enhancement
WardLT/ai-project-template
Project layout for efficient AI for science
Zaehyeon2/FDGNN-Fraud-Address-Detection-on-Ethereum-using-Graph-Neural-Network
CederGroupHub/Borges
Web Scarping Engines
r-b-1-5/Predicting-Drug-Interactions-using-Graph-Neural-Networks-DGL-
irsalanzaid/Categorizing-figures-from-biomedical-research-articles-using-deep-neural-networks-and-Bioassays
The project’s main objective is to extract knowledge from the biomedical research papers that contain diagrams/charts, i.e., bar graphs, line graph, boxplot, images of CT scans, cells, and other types of biological tests (known as assays). Research papers contain panels that have information in images or diagrams. The goal is to identify each panel from given datasets and categorize it into BioAssay Ontology categories with the help of machine learning and deep neural networks model. An additional focus of the project is to predict and identify the similarities between BioAssay Ontology categories and find their correlation. The dataset we are using is from SourceData, an initiative by EMBO (European Molecular Biology Organization). So, this project will record details of the correlation of BioAssay Ontology categories, predict and identify the panel with the help of a Convolutional neural network model.