This repository contains HTML versions of various Jupyter notebooks. These files are accessible directly in a web browser, allowing for easy viewing and sharing of notebook content without requiring a Jupyter Notebook environment.

This repository provides Jupyter notebooks for accessing and interacting with various Large Language Model (LLM) APIs using the Julia programming language. It demonstrates how to connect to and utilize Anthropic, Cohere, Mistral, and OpenAI APIs in Julia, allowing for experimentation with tasks like text generation, retrieval, and conversation.

This repository provides a modular framework for interacting with multiple language model APIs (OpenAI, Anthropic, and Mistral). It enables text generation, embedding retrieval, semantic search, and multi-step prompt flows, allowing users to leverage various models in a structured workflow.

This repository contains Jupyter notebooks for accessing and interacting with various Large Language Model (LLM) APIs using Python. The notebooks provide a straightforward approach to setting up API calls and working with different LLM providers, including Anthropic, Cohere, Mistral, and OpenAI.

This repository contains Jupyter notebooks that interact with various Large Language Model (LLM) APIs using the R programming language. It provides a structured approach to accessing Anthropic, Cohere, Mistral, and OpenAI APIs from R, enabling a range of NLP tasks such as text generation, retrieval-augmented generation, and conversation.

This repository provides Jupyter notebooks to interact with Mistral Large Language Models (LLMs) for tasks including chatbot development, retrieval-augmented generation, and text generation. These notebooks are designed to help users leverage Mistral models in a range of applications, from conversational AI to content generation.

A collection of notebooks for fine-tuning LLMs using Unsloth AI

A demo Jupyter Notebook showcasing a simple local RAG (Retrieval Augmented Generation) pipeline to chat with your PDFs.

Colab notebook and source code used to fine-tune Microsoft's Phi-3-mini into a dedicated English-to-Igbo specialist translator. Plus script for safely resuming training after timeouts.

A hands-on collection of practical notebooks for learning and building with LLMs , including prompt engineering, RAG, fine-tuning, and evaluation. Built for aspiring AI engineers using only free and open-source tools.

This repository contains Jupyter notebooks demonstrating various generation tasks with Large Language Models (LLMs). It provides examples for summarization, text generation, few-shot learning, and translation, utilizing different LLM APIs to showcase the capabilities of multiple providers.