Data Science | Machine Learning with Python for Researchers

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💻 ACU - Awesome Agents for Computer Use

A project that contains a carefully selected list of resources about AI agents designed to run autonomously on your computers.

It includes research studies, projects, frameworks, guides and various tools.

Agents support task analysis and decision making functions for interacting with any interface.

▪️ Github

#aiagents #awesome #agents

https://t.me/DataScienceT ✅

Are They the Same? Exploring Visual Correspondence Shortcomings of Multimodal LLMs

🖥 Github: https://github.com/zhouyiks/CoLVA/tree/main

📕 Paper: https://arxiv.org/pdf/2501.04670v1.pdf

⭐️ Dataset: https://paperswithcode.com/dataset/bdd100k

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Automating the Search for Artificial Life with Foundation Models

paper: https://arxiv.org/pdf/2412.17799v1.pdf

Code: https://github.com/sakanaai/asal

https://t.me/DataScienceT 💙

CogAgent: A Visual Language Model for GUI Agents

Paper: https://arxiv.org/pdf/2312.08914v3.pdf

CVPR 2024: http://openaccess.thecvf.com//content/CVPR2024/papers/Hong_CogAgent_A_Visual_Language_Model_for_GUI_Agents_CVPR_2024_paper.pdf

Code1: https://github.com/thudm/cogvlm
Code2: https://github.com/digirl-agent/digirl
Code3: https://github.com/THUDM/CogAgent

Dataset: TextVQA

https://t.me/DataScienceT 🩵

KAG: Boosting LLMs in Professional Domains via Knowledge Augmented Generation

Paper: https://arxiv.org/pdf/2409.13731v3.pdf

Code: https://github.com/openspg/kag

Dataset: 2WikiMultiHopQA

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Large Language Models Course: Learn by Doing LLM Projects

🖥 Github: https://github.com/peremartra/Large-Language-Model-Notebooks-Course

📕 Paper: https://doi.org/10.31219/osf.io/qgxea

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🀄 GuoFeng Webnovel: A Discourse-Level and Multilingual Corpus of Web Fiction

🖥 Github: https://github.com/longyuewangdcu/guofeng-webnovel

📕 Paper: https://arxiv.org/abs/2412.11732v1

🌟 Dataset: www2.statmt.org/wmt24/literary-trans

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⚡️ Byte Latent Transformer: Patches Scale Better Than Tokens

Byte Latent Transformer architecture (BLTs), a new byte-level LLM architecture that for the first time, matches tokenization-based LLM performance at scale, with significant improvements in inference efficiency and robustness.

🖥 Github: https://github.com/facebookresearch/blt

📕 Paper: https://arxiv.org/abs/2412.09871v1

🌟 Dataset: https://paperswithcode.com/dataset/mmlu

https://t.me/DataScienceT ✅

OASIS Alzheimer's Detection

Large-scale brain MRI dataset for deep neural network analysis

About Dataset
The dataset used is the OASIS MRI dataset (https://sites.wustl.edu/oasisbrains/), which consists of 80,000 brain MRI images. The images have been divided into four classes based on Alzheimer's progression. The dataset aims to provide a valuable resource for analyzing and detecting early signs of Alzheimer's disease.

To make the dataset accessible, the original .img and .hdr files were converted into Nifti format (.nii) using FSL (FMRIB Software Library). The converted MRI images of 461 patients have been uploaded to a GitHub repository, which can be accessed in multiple parts.
For the neural network training, 2D images were used as input. The brain images were sliced along the z-axis into 256 pieces, and slices ranging from 100 to 160 were selected from each patient. This approach resulted in a comprehensive dataset for analysis.

Patient classification was performed based on the provided metadata and Clinical Dementia Rating (CDR) values, resulting in four classes: demented, very mild demented, mild demented, and non-demented. These classes enable the detection and study of different stages of Alzheimer's disease progression.

During the dataset preparation, the .nii MRI scans were converted to .jpg files. Although this conversion presented some challenges, the files were successfully processed using appropriate tools. The resulting dataset size is 1.3 GB.

https://t.me/datasets1 🌟

2DMatGMM: An open-source robust machine learning platform for real-time detection and classification of 2D material flakes

🖥 Github: https://github.com/jaluus/2dmatgmm

📕 Paper: https://arxiv.org/abs/2412.09333v1

⭐️ Dataset: https://paperswithcode.com/task/instance-segmentation

https://t.me/DataScienceT 🏳

🌟 BioNeMo: A Framework for Developing AI Models for Drug Design.

NVIDIA BioNeMo2 Framework is a set of tools, libraries, and models for computational drug discovery and design.

It accelerates the most time-consuming and expensive steps in building and adapting biomolecular AI models by providing optimized models and tools that are easily integrated into GPU-based computing resources.

The framework enables the creation, training and tuning of models, and its capabilities span a variety of workloads and therapeutic mechanisms: molecule generation, protein structure prediction, protein-ligand prediction and representation learning.

In addition to pipeline code, scripts and utilities, BioNeMo2 Framework contains:

▶️ Pre-trained models:

🟢 ESM-2 is a pre-trained bidirectional encoder (BERT-like) for amino acid sequences. BioNeMo2 includes checkpoints with parameters 650M and 3B;

🟢 Geneformer is a tabular scoring model that generates a dense representation of a cell's scRNA by examining co-expression patterns in individual cells.


▶️ Datasets:

🟠 CELLxGENE is a collection of publicly available single-cell datasets collected by the CZI (Chan Zuckerberg Initiative) with a total volume of 24 million cells;


🟠 UniProt is a database of clustered sets of protein sequences from UniProtKB, created on the basis of translated genomic data.


📌 Licensing: Apache 2.0 License.


🟡 Project page
🟡 Documentation
🖥 GitHub

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❇️ AniGS: Animatable Gaussian Avatar from a Single Image with Inconsistent Gaussian Reconstruction 🔥


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🌟 INTELLECT-1: Release of the first decentralized learning model.

PRIME Intellect has published INTELLECT-1 ( Instruct + Base ), the first 10 billion parameter language model collaboratively trained in 50 days by 30 participants worldwide.

PRIME Intellect used its own PRIME platform, designed to address the main problems of decentralized learning: network unreliability and dynamic management of computing nodes.

The platform utilized a network of 112 H100 GPUs across 3 continents and achieved a compute utilization rate of 96% under optimal conditions.

The training corpus consisted of 1 trillion public dataset tokens with the following percentage distribution: 55% fineweb-edu, 10% fineweb, 20% Stack V1, 10% dclm-baseline, 5% open-web-math.

▶️ Technical specifications:

🟢 Parameters: 10B;
🟢 Layers: 42;
🟢 Attention Heads: 32;
🟢 Hidden Size: 4096;
🟢 Context Length: 8192;
🟢 Vocabulary Size: 128256.

INTELLECT-1 achieved 37.5% accuracy on the MMLU test and 72.26% on HellaSwag, and outperformed several other open-source models on WinoGrande with a score of 65.82%.

While these figures lag slightly behind today's popular models, the results of the experiment are a critical step toward democratizing AI development and preventing the consolidation of AI capabilities within a few organizations.

▶️ GGUF quantized versions of INTELLECT-1_Instruct in 3-bit (5.46 GB) to 8-bit (10.9 GB) bit depths from the LM Studio community.

▶️ Example of inference on Transformers:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

torch.set_default_device("cuda")
model = AutoModelForCausalLM.from_pretrained("PrimeIntellect/INTELLECT-1")
tokenizer = AutoTokenizer.from_pretrained("PrimeIntellect/INTELLECT-1")

input_text = "%prompt%"
input_ids = tokenizer.encode(input_text, return_tensors="pt")
output_ids = model.generate(input_ids, max_length=50, num_return_sequences=1)
output_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)

print(output_text)

📌 Licensing: Apache 2.0 License.


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OrientedFormer: An End-to-End Transformer-Based Oriented Object Detector in Remote Sensing Images


Publication date: IEEE Transactions on Geoscience and Remote Sensing 2024

Topic: Object detection

Paper: https://arxiv.org/pdf/2409.19648v1.pdf

GitHub: https://github.com/wokaikaixinxin/OrientedFormer

Description:

In this paper, we propose an end-to-end transformer-based oriented object detector, consisting of three dedicated modules to address these issues. First, Gaussian positional encoding is proposed to encode the angle, position, and size of oriented boxes using Gaussian distributions. Second, Wasserstein self-attention is proposed to introduce geometric relations and facilitate interaction between content and positional queries by utilizing Gaussian Wasserstein distance scores. Third, oriented cross-attention is proposed to align values and positional queries by rotating sampling points around the positional query according to their angles.

https://t.me/DataScienceT ✅

⭐️ Region-Aware Text-to-Image Generation via Hard Binding and Soft Refinement

RAG-Diffusion now supports FLUX.1 Redux!

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🔗 Code: https://github.com/NJU-PCALab/RAG-Diffusion

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O1 Replication Journey -- Part 2: Surpassing O1-preview through Simple Distillation, Big Progress or Bitter Lesson?

🖥 Github: https://github.com/gair-nlp/o1-journey

📕 Paper: https://arxiv.org/abs/2411.16489v1

🌟 Dataset: https://paperswithcode.com/dataset/lima

https://t.me/DataScienceT ✅

I highly recommend downloading the app, there is a solid guide to mastering AI.

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Explore "Pretraining LLMs," a short course developed with upstageai.

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🧹🪣 MOP+MiHo+NCC 🖼️👀: Image Matching Filtering and Refinement by Planes and Beyond

🖥 Github: https://github.com/fb82/miho

📕 Paper: https://arxiv.org/abs/2411.09484v1

🌟 Dataset: https://paperswithcode.com/dataset/scannet

https://t.me/DataScienceT ✅

OpenCoder doesn't get enough love

They open-sourced the entire pipeline to create QwenCoder-level code models.

This includes:
- Large datasets
- High-quality models
- Eval framework

Tons of great lessons and observations in the paper

📝 Paper: arxiv.org/abs/2411.04905

https://t.me/DataScienceT ✅

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Most classical ML algorithms cannot be trained with a batch implementation.

This is concerning because enterprises typically deal with tabular data and classical ML algorithms, such as tree-based methods, are frequently used for modeling.

For instance, to train a random forest from sklearn, the entire dataset must be present in memory. This limits its usage to only small/intermediate datasets.

There are two ways to extend random forests to large datasets.

1) Use big-data frameworks like Spark MLlib to train them.

2) Use random patches, which I learned from the PhD thesis of Dr. Gilles Louppe — Understanding Random Forests.

> Here’s what he proposed.

Note: This approach only works in an ensemble setting. So, you would have to train multiple models.

The idea is to sample random data patches (both rows and columns) and train a decision tree model on the patch.

Repeat this step multiple times to obtain the entire random forest model.

> Here's why it works.

The core objective of Bagging is to build trees that are as different as possible.

In this case, the dataset overlap between any two trees is NOT expected to be huge compared to the typical random forest. This aids in the Bagging objective.

His thesis presented benchmarks on 13 datasets:
- Random patches performed better than the random forest on 11 datasets.
- On the other two datasets, the difference was quite small (~0.05).

And this is how we can train a random forest model on large datasets that do not fit into memory.

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OmniGen: Unified Image Generation

Paper: https://arxiv.org/pdf/2409.11340v1.pdf

Code: https://github.com/vectorspacelab/omnigen

Datasets: DreamBooth - MagicBrush

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Docling Technical Report

Paper: https://arxiv.org/pdf/2408.09869v3.pdf

Code 1: https://github.com/DS4SD/docling
Code 2: https://github.com/DS4SD/docling-core

https://t.me/DataScienceT ✅

📌 Practical exercises and additional materials for the book "Build a Large Language Model (From Scratch)"

A Github repository with practical exercises, notebooks with code for developing, pre-training, and fine-tuning a GPT-type LLM model based on one of the best books on building an LLM from scratch.

▶️ About the book:
In this book, you will learn and understand how large language models work from the inside, creating your own LLM step by step, with a detailed explanation of each stage in clear language, diagrams and examples.

The method described in the book demonstrates the approach used to create large fundamental models such as those underlying ChatGPT.

In the repository, each chapter of the book has several (3-4) applied examples in ipynb format or as an executable python script. The code is aimed at a wide audience, is designed to run on regular laptops and does not require specialized equipment.

▶️ The main value of the repository is additional practical materials that will help you to study in more depth the subtleties and nuances of the process of setting up and learning LLM:

Setting

🟢 Tips on Setting Up Python
🟢 Installing Python Packages and Libraries
🟢 Docker Environment Setup Guide

Chapter 2: Working with Text Data

🟠 Comparison of different implementations of Byte Pair Encoding (BPE)
🟠 Understanding the difference between embedding and line layers
🟠 Dataloader Intuition with Prime Numbers

Chapter 3: Code of Attention Mechanisms

🟢 Comparison of Effective Implementations of Multi-Head Attention
🟢 PyTorch Buffers

Chapter 4: Implementing the GPT Model from Scratch

🟠 FLOPS Analysis

Chapter 5: Pre-training on unlabeled data

🟢 Alternative Loading of HuggingFace Scales Using Transformers
🟢 Pre-training GPT on the Project Gutenberg dataset
🟢 Adding more features to the learning cycle
🟢 Hyperparameter optimization for pretraining
🟢 Creating a user interface for interacting with LLM
🟢 Convert GPT to Llama
🟢 Llama 3.2 from scratch
🟢 Memory-efficient model loading

Chapter 6: Fine-tuning for Classification

🟠 More experiments on fine-tuning the different layers and using larger models
🟠 Fine-tuning various models based on a 50K row IMDB movie review dataset.
🟠 Building a User Interface for Interacting with a GPT-Based Spam Classifier

Chapter 7: Fine-tuning to Follow Instructions

🟢 Dataset utilities for finding close duplicates and creating passive voice entries
🟢 Evaluating responses to instructions using OpenAI and Ollama APIs
🟢 Creating a dataset for fine-tuning instructions
🟢 Improving the dataset for fine-tuning instructions
🟢 Creating a Preference Dataset with Llama 3.1 70B and Ollama
🟢 DPO for LLM Alignment procedure
🟢 Creating a user interface for interacting with a GPT model with fine-tuning of instructions

🖥 Github

https://t.me/DataScienceT ✅

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Constrained Diffusion Implicit Models!

We use diffusion models to solve noisy inverse problems like inpainting, sparse-recovery, and colorization. 10-50x faster than previous methods!

Paper: arxiv.org/pdf/2411.00359

Demo: https://t.co/m6o9GLnnZF

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Don’t sleep on Vision Language Models (VLMs).

With the releases of Llama 3.2 and ColQwen2, multimodal models are gaining more and more traction.

VLMs are multimodal models that can handle image and text modalities:

Input: Image and text
Output: Text

They can be used for many use cases, including visual question answering or document understanding (as in the case of ColQwen2).

How do they work under the hood?

The main challenge in VLMs is to unify the image and text representations.

For this, a typical VLM architecture consists of the following components:

• image encoder (e.g., CLIP, SigLIP)
• embedding projector to align image and text representations
• text decoder (e.g., Vicuna, Gemma)

huggingface.co/blog/vlms

https://t.me/DataScienceT

📖 LLM-Agent-Paper-List is a repository of papers on the topic of agents based on large language models (LLM)! The papers are divided into categories such as LLM agent architectures, autonomous LLM agents, reinforcement learning (RL), natural language processing methods, multimodal approaches and tools for developing LLM agents, and more.

🖥 Github

https://t.me/DataScienceT ✅

SAM2Long: Enhancing SAM 2 for Long Video Segmentation with a Training-Free Memory Tree

🖥 Github: https://github.com/mark12ding/sam2long

📕 Paper: https://arxiv.org/abs/2410.16268v1

🤗 HF: https://huggingface.co/papers/2410.16268

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Benchmarking Agentic Workflow Generation"! ⭐️

ArXiv: https://arxiv.org/abs/2410.07869

Website: https://www.zjukg.org/project/WorFBench/

Data: https://huggingface.co/collections/zjunlp/worfbench-66fc28b8ac1c8e2672192ea1

Github: https://github.com/zjunlp/WorFBench

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estimating body and hand motion from a pair of glasses 🤓

website: http://egoallo.github.io

code: http://github.com/brentyi/egoallo

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Efficiently Democratizing Medical LLMs for 50 Languages via a Mixture of Language Family Experts

💻 Github: https://github.com/freedomintelligence/apollomoe

🔖 Paper: https://arxiv.org/abs/2410.10626v1

🤗 Dataset: https://paperswithcode.com/dataset/mmlu

https://t.me/DataScienceT 🏵

Generalizable and Animatable Gaussian Head Avatar

🖥 Github: https://github.com/xg-chu/gagavatar

📕 Paper: https://arxiv.org/abs/2410.07971v1

https://t.me/DataScienceT 🏵