keshavaspanda,github

llm-data-annotation

Harness the power of Large Language Models like OpenAI's GPT-3.5 for data annotation and model enhancement. This framework combines human expertise with LLM precision, employs Iterative Active Learning for continuous improvement, and integrates CleanLab (Confident Learning) to ensure high-quality datasets and superior model performance

llm-fundamentals

https://graphacademy.neo4j.com/courses/llm-fundamentals/

llm-guard

The Security Toolkit for LLM Interactions

llm-recsys-id

How to Index Item IDs for Recommendation Foundation Models

llm-twin-course

🤖 LLM Twin FREE Course: Building Your Production-Ready AI Replica | An End-to-End Framework for Production-Ready LLM Systems by Building Your LLM Twin | WIP...

llm4rs

the official implementation of the RecSys 2023 paper “Uncovering ChatGPT's Capabilities in Recommender Systems”

llm4teach-freetext-server

llm_4_plc

LLM4PLC: Harnessing Large Language Models for Verifiable Programming of PLCs in Industrial Control Systems

llm_digital_twin

llm_kg_generator

llmdatahub

A quick guide (especially) for trending instruction finetuning datasets

llmops-gha-demo

llmrec

[WSDM'2024 Oral] "LLMRec: Large Language Models with Graph Augmentation for Recommendation"

llms4ts

lm-question-generation

Multilingual/multidomain question generation datasets, models, and python library for question generation.

lmflow

An Extensible Toolkit for Finetuning and Inference of Large Foundation Models. Large Models for All.

loan-default-risk-prediction

AI Starter Kit to predict probability of a loan default from client using Intel® optimized version of XGBoost

loren

Resources for our AAAI 2022 paper: "LOREN: Logic-Regularized Reasoning for Interpretable Fact Verification".

lsmdc-fillin-improved

Identity-Aware Multi-Sentence Video Description

m-rag

Build your own Multimodal RAG Application using less than 300 lines of code.

m3vit

[NeurIPS 2022] “M³ViT: Mixture-of-Experts Vision Transformer for Efficient Multi-task Learning with Model-Accelerator Co-design”, Hanxue Liang*, Zhiwen Fan*, Rishov Sarkar, Ziyu Jiang, Tianlong Chen, Kai Zou, Yu Cheng, Cong Hao, Zhangyang Wang