Prompt Testing framework for LLMs (specifically OpenAI models). Compute NLP and Responsible AI metrics for each model-generated answer.
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Updated
Oct 25, 2023 - Python
Prompt Testing framework for LLMs (specifically OpenAI models). Compute NLP and Responsible AI metrics for each model-generated answer.
This repo contains the code of concepts and projects i learnt from the course "LangChain & Vector Databases in Production", “Training and Fine-tuning LLMs for Production” and "Retrieval Augmented Generation for Production with LangChain & LlamaIndex"
CVPR 2024: Robust Depth Enhancement via Polarization Prompt Fusion Tuning
Tema personalizado de Oh My Posh, e instrucciones de instalación
Cancer Classification using Bottleneck Adapters
AI Prompt Engineering is a Streamlit app that allows users to experiment with different types of prompts for AI language models. The app provides a user-friendly interface for entering text or code and generating summarized or cleaned output.
A tool to test and optimize prompts for ChatGPT API
The code for the paper "Instance-aware Dynamic Prompt Tuning for Pre-trained Point Cloud Models" (ICCV'23).
🖼️ Simple mini service and prompts that allows you to draw pictures in ChatGPT
[ICML'2024] "FlashST: A Simple and Universal Prompt-Tuning Framework for Traffic Prediction"
PyTorch Implementation of Attention Prompt Tuning: Parameter-Efficient Adaptation of Pre-Trained Models for Action Recognition
Prompt Engineering Tool for AI Models with cli prompt or api usage
This is the repo for prompt tuning a language model to improve the given prompt (vague).
Official implementation for "UniST: A Prompt-Empowered Universal Model for Urban Spatio-Temporal Prediction" (KDD 2024)
A simple 'ls' like utility to be used in pre-prompt in any shell.
Unlock the Power of GPT3 and OpenAI with a Single Keyboard Shortcut
Vision Prompt Tuning을 직접 구현하고, Full Fine Tuning과 비교해보았습니다.
This project explains "prompt engineering," a key technique for guiding AI models to desired outputs in tools like chatbots and text summarizers. It highlights the importance of clear instructions and techniques like CoT Prompting for effective communication with large language models. The project also introduces the Langchain library✨.
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