PINNACLE is a flexible geometric deep learning approach that trains on contextualized protein interaction networks to generate context-aware protein representations. It provides protein representations split across various cell-type contexts from different tissues and organs. The tool can be fine-tuned to study the genomic effects of drugs and nominate promising protein targets and cell-type contexts for further investigation. PINNACLE exemplifies the paradigm of incorporating context-specific effects for studying biological systems, especially the impact of disease and therapeutics.

This repository contains the source code and datasets for Graph Chain-of-Thought: Augmenting Large Language Models by Reasoning on Graphs accepted to ACL 2024. It proposes a framework called Graph Chain-of-thought (Graph-CoT) to enable Language Models to traverse graphs step-by-step for reasoning, interaction, and execution. The motivation is to alleviate hallucination issues in Language Models by augmenting them with structured knowledge sources represented as graphs.

PromptAgent is a repository for a novel automatic prompt optimization method that crafts expert-level prompts using language models. It provides a principled framework for prompt optimization by unifying prompt sampling and rewarding using MCTS algorithm. The tool supports different models like openai, palm, and huggingface models. Users can run PromptAgent to optimize prompts for specific tasks by strategically sampling model errors, generating error feedbacks, simulating future rewards, and searching for high-reward paths leading to expert prompts.

ZipPy is a research repository focused on fast AI detection using compression techniques. It aims to provide a faster approximation for AI detection that is embeddable and scalable. The tool uses LZMA and zlib compression ratios to indirectly measure the perplexity of a text, allowing for the detection of low-perplexity text. By seeding a compression stream with AI-generated text and comparing the compression ratio of the seed data with the sample appended, ZipPy can identify similarities in word choice and structure to classify text as AI or human-generated.

LLM-Viewer is a tool for visualizing Language and Learning Models (LLMs) and analyzing performance on different hardware platforms. It enables network-wise analysis, considering factors such as peak memory consumption and total inference time cost. With LLM-Viewer, users can gain valuable insights into LLM inference and performance optimization. The tool can be used in a web browser or as a command line interface (CLI) for easy configuration and visualization. The ongoing project aims to enhance features like showing tensor shapes, expanding hardware platform compatibility, and supporting more LLMs with manual model graph configuration.

ManipVQA is a framework that enhances Multimodal Large Language Models (MLLMs) with manipulation-centric knowledge through a Visual Question-Answering (VQA) format. It addresses the deficiency of conventional MLLMs in understanding affordances and physical concepts crucial for manipulation tasks. By infusing robotics-specific knowledge, including tool detection, affordance recognition, and physical concept comprehension, ManipVQA improves the performance of robots in manipulation tasks. The framework involves fine-tuning MLLMs with a curated dataset of interactive objects, enabling robots to understand and execute natural language instructions more effectively.

This repository provides a Python implementation of a method that leverages pre-trained large language models to construct and utilize world models for model-based task planning. It includes scripts to generate domain models using natural language descriptions, correct domain models based on feedback, and support plan generation for tasks in different domains. The code has been refactored for better readability and includes tools for validating PDDL syntax and handling corrective feedback.

Uncheatable Eval is a tool designed to assess the language modeling capabilities of LLMs on real-time, newly generated data from the internet. It aims to provide a reliable evaluation method that is immune to data leaks and cannot be gamed. The tool supports the evaluation of Hugging Face AutoModelForCausalLM models and RWKV models by calculating the sum of negative log probabilities on new texts from various sources such as recent papers on arXiv, new projects on GitHub, news articles, and more. Uncheatable Eval ensures that the evaluation data is not included in the training sets of publicly released models, thus offering a fair assessment of the models' performance.

AudioSeal is a method for speech localized watermarking, designed with state-of-the-art robustness and detector speed. It jointly trains a generator to embed a watermark in audio and a detector to detect watermarked fragments in longer audios, even in the presence of editing. The tool achieves top-notch detection performance at the sample level, generates minimal alteration of signal quality, and is robust to various audio editing types. With a fast, single-pass detector, AudioSeal surpasses existing models in speed, making it ideal for large-scale and real-time applications.

Aligner is a model-agnostic alignment tool that learns correctional residuals between preferred and dispreferred answers using a small model. It can be directly applied to various open-source and API-based models with only one-off training, suitable for rapid iteration and improving model performance. Aligner has shown significant improvements in helpfulness, harmlessness, and honesty dimensions across different large language models.

JAX AI Stack is a suite of libraries built around the JAX Python package for array-oriented computation and program transformation. It provides a growing ecosystem of packages for specialized numerical computing across various domains, encouraging modularity and innovation in domain-specific libraries. The stack includes core packages like JAX, flax for building neural networks, ml_dtypes for NumPy dtype extensions, optax for gradient processing and optimization, and orbax for checkpointing and persistence utilities. Optional packages like grain data loader and tensorflow are also available for installation.

Nucleoid is a declarative (logic) runtime environment that manages both data and logic under the same runtime. It uses a declarative programming paradigm, which allows developers to focus on the business logic of the application, while the runtime manages the technical details. This allows for faster development and reduces the amount of code that needs to be written. Additionally, the sharding feature can help to distribute the load across multiple instances, which can further improve the performance of the system.

DBRX is a large language model trained by Databricks and made available under an open license. It is a Mixture-of-Experts (MoE) model with 132B total parameters and 36B live parameters, using 16 experts, of which 4 are active during training or inference. DBRX was pre-trained for 12T tokens of text and has a context length of 32K tokens. The model is available in two versions: a base model and an Instruct model, which is finetuned for instruction following. DBRX can be used for a variety of tasks, including text generation, question answering, summarization, and translation.

LongRoPE is a method to extend the context window of large language models (LLMs) beyond 2 million tokens. It identifies and exploits non-uniformities in positional embeddings to enable 8x context extension without fine-tuning. The method utilizes a progressive extension strategy with 256k fine-tuning to reach a 2048k context. It adjusts embeddings for shorter contexts to maintain performance within the original window size. LongRoPE has been shown to be effective in maintaining performance across various tasks from 4k to 2048k context lengths.

Chinese-Tiny-LLM is a repository containing procedures for cleaning Chinese web corpora and pre-training code. It introduces CT-LLM, a 2B parameter language model focused on the Chinese language. The model primarily uses Chinese data from a 1,200 billion token corpus, showing excellent performance in Chinese language tasks. The repository includes tools for filtering, deduplication, and pre-training, aiming to encourage further research and innovation in language model development.

A public repository collecting links to state-of-the-art training sets, QA, benchmarks and other evaluations for various ML and LLM applications in materials science and chemistry. It includes datasets related to chemistry, materials, multimodal data, and knowledge graphs in the field. The repository aims to provide resources for training and evaluating machine learning models in the materials science and chemistry domains.

PINNACLE is a flexible geometric deep learning approach that trains on contextualized protein interaction networks to generate context-aware protein representations. It provides protein representations split across various cell-type contexts from different tissues and organs. The tool can be fine-tuned to study the genomic effects of drugs and nominate promising protein targets and cell-type contexts for further investigation. PINNACLE exemplifies the paradigm of incorporating context-specific effects for studying biological systems, especially the impact of disease and therapeutics.

AIStore is a lightweight object storage system designed for AI applications. It is highly scalable, reliable, and easy to use. AIStore can be deployed on any commodity hardware, and it can be used to store and manage large datasets for deep learning and other AI applications.

cl-waffe2 is an experimental deep learning framework in Common Lisp, providing fast, systematic, and customizable matrix operations, reverse mode tape-based Automatic Differentiation, and neural network model building and training features accelerated by a JIT Compiler. It offers abstraction layers, extensibility, inlining, graph-level optimization, visualization, debugging, systematic nodes, and symbolic differentiation. Users can easily write extensions and optimize their networks without overheads. The framework is designed to eliminate barriers between users and developers, allowing for easy customization and extension.

AIGT is a repository containing scripts for deep learning in guided medical interventions, focusing on ultrasound imaging. It provides a complete workflow from formatting and annotations to real-time model deployment. Users can set up an Anaconda environment, run Slicer notebooks, acquire tracked ultrasound data, and process exported data for training. The repository includes tools for segmentation, image export, and annotation creation.

DeepLearning-Interview-Awesome-2024 is a repository that covers various topics related to deep learning, computer vision, big models (LLMs), autonomous driving, smart healthcare, and more. It provides a collection of interview questions with detailed explanations sourced from recent academic papers and industry developments. The repository is aimed at assisting individuals in academic research, work innovation, and job interviews. It includes six major modules covering topics such as large language models (LLMs), computer vision models, common problems in computer vision and perception algorithms, deep learning basics and frameworks, as well as specific tasks like 3D object detection, medical image segmentation, and more.

NoLabs is an open-source biolab that provides easy access to state-of-the-art models for bio research. It supports various tasks, including drug discovery, protein analysis, and small molecule design. NoLabs aims to accelerate bio research by making inference models accessible to everyone.

OpenCRISPR is a set of free and open gene editing systems designed by Profluent Bio. The OpenCRISPR-1 protein maintains the prototypical architecture of a Type II Cas9 nuclease but is hundreds of mutations away from SpCas9 or any other known natural CRISPR-associated protein. You can view OpenCRISPR-1 as a drop-in replacement for many protocols that need a cas9-like protein with an NGG PAM and you can even use it with canonical SpCas9 gRNAs. OpenCRISPR-1 can be fused in a deactivated or nickase format for next generation gene editing techniques like base, prime, or epigenome editing.

The Ersilia Model Hub is a unified platform of pre-trained AI/ML models dedicated to infectious and neglected disease research. It offers an open-source, low-code solution that provides seamless access to AI/ML models for drug discovery. Models housed in the hub come from two sources: published models from literature (with due third-party acknowledgment) and custom models developed by the Ersilia team or contributors.

OntoGPT is a Python package for extracting structured information from text using large language models, instruction prompts, and ontology-based grounding. It provides a command line interface and a minimal web app for easy usage. The tool has been evaluated on test data and is used in related projects like TALISMAN for gene set analysis. OntoGPT enables users to extract information from text by specifying relevant terms and provides the extracted objects as output.

BIA `bob` is a Jupyter-based assistant for interacting with data using large language models to generate Python code. It can utilize OpenAI's chatGPT, Google's Gemini, Helmholtz' blablador, and Ollama. Users need respective accounts to access these services. Bob can assist in code generation, bug fixing, code documentation, GPU-acceleration, and offers a no-code custom Jupyter Kernel. It provides example notebooks for various tasks like bio-image analysis, model selection, and bug fixing. Installation is recommended via conda/mamba environment. Custom endpoints like blablador and ollama can be used. Google Cloud AI API integration is also supported. The tool is extensible for Python libraries to enhance Bob's functionality.

Scientific Large Language Models (Sci-LLMs) is a repository that collects papers on scientific large language models, focusing on biology and chemistry domains. It includes textual, molecular, protein, and genomic languages, as well as multimodal language. The repository covers various large language models for tasks such as molecule property prediction, interaction prediction, protein sequence representation, protein sequence generation/design, DNA-protein interaction prediction, and RNA prediction. It also provides datasets and benchmarks for evaluating these models. The repository aims to facilitate research and development in the field of scientific language modeling.

Polaris establishes a novel, industry‑certified standard to foster the development of impactful methods in AI-based drug discovery. This library is a Python client to interact with the Polaris Hub. It allows you to download Polaris datasets and benchmarks, evaluate a custom method against a Polaris benchmark, and create and upload new datasets and benchmarks.

The 'awesome-AI4MolConformation-MD' repository focuses on protein conformations and molecular dynamics using generative artificial intelligence and deep learning. It provides resources, reviews, datasets, packages, and tools related to AI-driven molecular dynamics simulations. The repository covers a wide range of topics such as neural networks potentials, force fields, AI engines/frameworks, trajectory analysis, visualization tools, and various AI-based models for protein conformational sampling. It serves as a comprehensive guide for researchers and practitioners interested in leveraging AI for studying molecular structures and dynamics.