ArslanKAS

LangChain-for-LLM-App-Development

The framework to take LLMs out of the box. Learn to use LangChain to call LLMs into new environments, and use memories, chains, and agents to take on new and complex tasks.

Functions-Tools-and-Agents-with-LangChain

Explore new advancements like ChatGPT’s function calling capability, and build a conversational agent using a new syntax called LangChain Expression Language (LCEL) for tasks like tagging, extraction, tool selection, and routing.

Building-Machine-Learning-Demos-with-Gradio

In this course you'll learn to use Gradio to create user-friendly apps with minimal code: Summarize text using a large language model, generate image captions, and chat with an open source LLM. Gain practical knowledge to build interactive demos faster.

Large-Language-Models-with-Semantic-Search

Explore from keyword search to dense retrieval and reranking, which injects the intelligence of LLMs into your search system, making it faster and more effective.

Multimodal-RAG---Chat-with-Videos

Python-Chilla-2.0

All the content that I learned through two Courses. One is called "Python Chilla" and the second one is called "100 Days of Machine Learning"

Quality-and-Safety-for-LLM-Applications

Explore new metrics and best practices to monitor your LLM systems and ensure safety and quality

Pair-Programming-with-LLM

Learn how LLMs can enhance, debug, and document your code in this new course built in collaboration with Google

AI-Agents-in-LangGraph

Learn about LangGraph’s components and how they enable the development, debugging, and maintenance of AI agents.

ArslanKAS

Config files for my GitHub profile.

Serverless-LLM-Amazon-Bedrock

You’ll learn how to deploy a large language model-based application into production using serverless technology

Getting-Started-with-Mistral

Learn about selecting the right Mistral model for your use case, and get hands-on with features like effective prompting techniques, function calling, JSON mode, and Retrieval Augmented Generation (RAG).

Docker

FastAPI

Create APIs in Python using FastAPI

LearnDataWithMark

Code and scripts behind the @LearnDataWithMark YouTube channel

Python-API-using-Flask

A little demo on how to create APIs in Python using Flask

Quantization-Fundamentals

Learn to compressing models through methods such as quantization to make them more efficient, faster, and accessible

Reasoning-with-o1

Learn how to effectively prompt and use OpenAI’s o1 model in Reasoning with o1

Data-Science-Gen-AI-Playlist-2024

json_repair

A python module to repair invalid JSON, commonly used to parse the output of LLMs

LLM_Everything

Just a simple repo that covers everything I learn about LLMs (hands-on only)

LLMOps

you’ll go through the LLMOps pipeline of pre-processing training data for supervised instruction tuning, and adapt a supervised tuning pipeline to train and deploy a custom LLM

MLOps-using-Mlflow

Open-Source-Models-with-Hugging-Face

In this course, you’ll select open source models from Hugging Face Hub to perform NLP, audio, image and multimodal tasks using the Hugging Face transformers library.

promptcraft

PromptCraft is a prompt perturbation toolkit from the character, word, and sentence levels for prompt robustness analysis. PyPI Package: pypi.org/project/promptcraft

PyUIBuilder

The webflow for Python GUI. GUI builder for Tkinter, CustomTkinter, Kivy and PySide (upcoming)

simple-local-rag

Build a RAG (Retrieval Augmented Generation) pipeline from scratch and have it all run locally.

test_aws

thefuzz

Fuzzy String Matching in Python

Updated-Langchain

Awesome Series by Krish Naik