Get up and running with Llama 3.2, Mistral, Gemma 2, and other large language models.

Finetune Llama 3.2, Mistral, Phi, Qwen 2.5 & Gemma LLMs 2-5x faster with 80% less memory

An efficient, flexible and full-featured toolkit for fine-tuning LLM (InternLM2, Llama3, Phi3, Qwen, Mistral, ...)

Efficient Triton Kernels for LLM Training

This is a Phi-3 book for getting started with Phi-3. Phi-3, a family of open sourced AI models developed by Microsoft. Phi-3 models are the most capable and cost-effective small language models (SLMs) available, outperforming models of the same size and next size up across a variety of language, reasoning, coding, and math benchmarks.

🔥🔥 LLaVA++: Extending LLaVA with Phi-3 and LLaMA-3 (LLaVA LLaMA-3, LLaVA Phi-3)

Official repository for "Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing". Your efficient and high-quality synthetic data generation pipeline!

Phi3 中文仓库

Windows app for developers. Learn what you can do and how to add AI with local models and APIs in your Windows app.

Chat effortlessly, execute commands, and interpret code with Llama3, Phi3, and more - your local AI assistant. Enjoy seamless interaction while ensuring ultimate privacy

An open-source implementaion for fine-tuning Phi3-Vision and Phi3.5-Vision by Microsoft.

Instruction/chat prompts creation library for text generation LLMs. It supports local and Hugging Face models.

Generative AI playground using Ollama, OpenAI API and JavaScript. Try AI models in your browser!

Scalable AI Inference Server for CPU and GPU with Node.js | Utilizes llama.cpp and parts of llamafile C/C++ core under the hood.

This project implements a web-based chatbot using open source technology

This experiment repository will be used for demonstration on using Phi-3.5, Microsoft's Small Language Model, locally with Ollama and JavaScript for the event of BKK.JS #21 and JavaScript Bangkok 2.0.0

Sentiment analysis with pre-trained language models using TweetEval.

Phi-3 LLM by Microsoft

A RAG system is just the beginning of harnessing the power of LLM. The next step is creating an intelligent Agent. In Agentic RAG the Agent makes use of available tools, strategies and LLM to generate response in a specialized way. Unlike a simple RAG, an Agent can dynamically choose between tools, routing strategy, etc.

This project leverages the Phi3 model and ChromaDB to create a Retrieval-Augmented Generation (RAG) application.