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VideoLLaMA 2: Advancing Spatial-Temporal Modeling and Audio Understanding in Video-LLMs

This is the official implementation of "Blind Image Restoration via Fast Diffusion Inversion"

This hands-on walks you through fine-tuning an open source LLM on Azure and serving the fine-tuned model on Azure. It is intended for Data Scientists and ML engineers who have experience with fine-…

Every practical and proposed defense against prompt injection.

data-to-paper: Backward-traceable AI-driven scientific research

High-quality datasets, tools, and concepts for LLM fine-tuning.

📖 Korean NLU Benchmark

tensorflow를 사용하여 텍스트 전처리부터, Topic Models, BERT, GPT, LLM과 같은 최신 모델의 다운스트림 태스크들을 정리한 Deep Learning NLP 저장소입니다.

AWS-native chatbot using Bedrock + Claude (+Mistral)

Orion-14B is a family of models includes a 14B foundation LLM, and a series of models: a chat model, a long context model, a quantized model, a RAG fine-tuned model, and an Agent fine-tuned model. …

This repo includes Claude prompt curation to use Claude better.

Grok open release

A principled instruction benchmark on formulating effective queries and prompts for large language models (LLMs). Our paper: https://arxiv.org/abs/2312.16171

repo for demos Unlock insights with AWS GenAI services (re:Invent BOA303)

Sample codes for sagemaker immersion day

This hands-on provides a guide to SageMaker MME(Multi-Model-Endpoint) on GPU.

이 프로젝트는 Amazon SageMaker에서 LLama2 Large Language Model (LLM)을 미세 조정하는 방법을 보여줍니다. Hugging Face의 PEFT (Parameter Efficient Fine-tuning) 기법과 QLoRA (Quantized Low-Rank Adapters)를 사용하여, 대규모 언어 모델을 더 효율적…

[CVPR 2024] MagicAnimate: Temporally Consistent Human Image Animation using Diffusion Model

This is a workshop designed for Amazon Bedrock a foundational model service.

Reference implementations of several LangChain agents as Streamlit apps

A collection of Korean NLP hands-on labs on Amazon SageMaker

create sagemaker studio using cdk typescript

It is a chatbot for question and answering using RAG based on LLM

This hands-on lab walks you through a step-by-step approach to efficiently serving and fine-tuning large-scale Korean models on AWS infrastructure.

This hands-on labs modifies the Hugging Face PEFT fine-tuning and model deployment example on Amazon SageMaker.

AI and Machine Learning with Kubeflow, Amazon EKS, and SageMaker