hongbinye

LLaMA-Factory

Unified Efficient Fine-Tuning of 100+ LLMs & VLMs (ACL 2024)

LLaVA

[NeurIPS'23 Oral] Visual Instruction Tuning (LLaVA) built towards GPT-4V level capabilities and beyond.

MiniCPM-o

MiniCPM-o 2.6: A GPT-4o Level MLLM for Vision, Speech and Multimodal Live Streaming on Your Phone

opencompass

OpenCompass is an LLM evaluation platform, supporting a wide range of models (Llama3, Mistral, InternLM2,GPT-4,LLaMa2, Qwen,GLM, Claude, etc) over 100+ datasets.

OLMo

Modeling, training, eval, and inference code for OLMo

DoRA

[ICML2024 (Oral)] Official PyTorch implementation of DoRA: Weight-Decomposed Low-Rank Adaptation

segyio

Fast Python library for SEGY files.

STEAD

STanford EArthquake Dataset (STEAD):A Global Data Set of Seismic Signals for AI

LargeST

LargeST: A Benchmark Dataset for Large-Scale Traffic Forecasting (NeurIPS 2023)

IEPile

[ACL 2024] IEPile: A Large-Scale Information Extraction Corpus

TSB-UAD

An End-to-End Benchmark Suite for Univariate Time-Series Anomaly Detection

Force-2020-Machine-Learning-competition

the results, code and the data for the Force 2020 Machine learning competition after the completion of the competition in October 2020.

OneGen

[EMNLP 2024 Findings] OneGen: Efficient One-Pass Unified Generation and Retrieval for LLMs.

numpy-

开源的测井数据集 Open source well logging data set 适用于机器学习分析地下储层岩性识别与分类

SeisCLIP

The code of Paper 'SeisCLIP: A seismology foundation model pre-trained by multimodal data for multipurpose seismic feature extraction'

2020_FORCE_Lithology_Prediction

FORCE-2020-Lithology

A collection of notebooks exploring ideas for the Force 2020 Lithology Classification Challenge.

pretrain-time-series-cloudops

Official code repository for the paper "Pushing the Limits of Pre-training for Time Series Forecasting in the CloudOps Domain"

affiliation-metrics-py

Python 3 implementation of the affiliation metrics and tests for reproducing the experiments described in "Local Evaluation of Time Series Anomaly Detection Algorithms" [KDD 2022]

Force-2020-Machine-Learning-competition_predict-lithology-EDA

Exploratory Data Analysis for the Lithology prediction part of the 2020 FORCE Machine Learning Contest

PNW-ML

A ML-ready curated data set for a wide range of seismic signals from Pacific Northwest

Force_2020_Classification

Check out my solution for FORCE 2020 Lithology Classification Contest. The objective of the competition is to create machine learning model to correctly predict lithology labels using provided well logs, provided NPD Lithostratigraphy and well location X,Y position

force-ml-2020-wells

FORCE-2020-Lithology

FORCE-2020-Lithology " https://xeek.ai/challenges/force-well-logs/overview "

2020-ml-contest

FORCE 2020: Machine Predicted Lithology

FORCE_ML_lithology

Repository for the FORCE 2020 Machine Lithology Prediction competition

FORCE-CONTEST-2020

Prediction of lithology

Force-2020-Facies-Classification

This project demonstrates facies classification using machine learning techniques. It includes data preprocessing, different model training , and hyperparameter tuning to improve prediction accuracy. The notebook is designed for geoscientists and machine learning enthusiasts working on subsurface data interpretation.

K-means-clustering-with-well-log-data-in-FORCE2020-competition

force_2020

Force 2020 well log competition repo