large-language-models

Commanding robots using only Language Models' prompts

A Web Crawler based on LLMs implemented with Ray and Huggingface. The embeddings are saved into a vector database for fast clustering and retrieval. Use it for your RAG.

[EMNLP 2025] Official implementation of "SensorLLM: Aligning Large Language Models with Motion Sensors for Human Activity Recognition"

Data and code for paper "M3Exam: A Multilingual, Multimodal, Multilevel Benchmark for Examining Large Language Models"

[ICLR 2024] This is the repository for the paper titled "DePT: Decomposed Prompt Tuning for Parameter-Efficient Fine-tuning"

This paper list focuses on the theoretical and empirical analysis of language models, especially large language models (LLMs). The papers in this list investigate the learning behavior, generalization ability, and other properties of language models through theoretical analysis, empirical analysis, or a combination of both.

GenAI/RAG Optimizer and Toolkit for experimentation using Oracle Database AI Vector Search and NL2SQL

Fine-Tuning Falcon-7B, LLAMA 2 with QLoRA to create an advanced AI model with a profound understanding of the Indian legal context.

Promptdesk is a tool designed for effectively creating, organizing, and evaluating prompts and large language models (LLMs).

This repository is used to collect papers and code in the field of AI.

The HugChat Streamlit app is an LLM-powered chatbot built using Streamlit and HugChat.

🚀 Easy, open-source LLM finetuning with one-line commands, seamless cloud integration, and popular optimization frameworks. ✨

Local-GenAI-Search is a generative search engine based on Llama 3, langchain and qdrant that answers questions based on your local files

LLM-powered agents for scientific research automation

Implementation of contextual engineering pipeline with LangChain and LangGraph Agents

One place to find variety of resources and a community of contributors, ML Guide is the perfect place to learn, build, and collaborate. Let's dive into the exciting world of machine learning together!

[ICLR 2023] "Learning to Grow Pretrained Models for Efficient Transformer Training" by Peihao Wang, Rameswar Panda, Lucas Torroba Hennigen, Philip Greengard, Leonid Karlinsky, Rogerio Feris, David Cox, Zhangyang Wang, Yoon Kim

Application of Large Language Models (LLM) for computational materials science - visit jan-janssen.com/LangSim

A professional list on Multi-Modalities For Time Series Analysis (MM4TSA) Papers and Resource.

Some words that LLM regularly uses