Assessing LLM with Langchain

Phi-3-Vision model test - running locally

Converting Phi-3 model into MLFlow format, to enable targeted inference.

Código de ejemplo demo del vídeo "💥¡Crea un ChatGPT en tu equipo sin usar la nube! Con Ollama, Microsoft PHI-3 y Semantic Kernel💥"

Robotics navigation solution (on example of Pioneer 3-DX mobile robot in CoppeliaSim) powered by Phi-3 small language model and AMD AI PC.

⚗️ Phi-3-mini 3.8B instruct model repository

The LLM Dataset Generator is an open source tool for generating text data compatible with various language models supported by LangChain. You can customize it to meet your specific needs, making it a valuable resource for researchers, developers, and organizations working on NLP applications.

Local RAG based LLM Pipeline for Grammar teaching.

AI Discord Bot (GEMM-X) is an intelligent assistant for Discord, leveraging AI technologies from multiple providers to generate images, create music, produce speech, and more. It supports custom personality settings and advanced user/server configurations.

EmbeddedLLM: API server for Embedded Device Deployment. Currently support CUDA/OpenVINO/IpexLLM/DirectML/CPU

This project offers a user-friendly interface that allows users to easily create stories and enrich them with visuals. It supports creativity with story creation and visualisation features.

Fine-tuning Large Language Models (LLMs) for Text Classification Task

這是一本 Phi-3 的入門書。Phi-3 是由微軟開發的一系列開放 AI 模型。Phi-3 模型是目前最強大且具成本效益的小型語言模型（SLM），在各種語言、推理、程式碼和數學基準測試中，表現優於同尺寸和更大尺寸的模型。

Official repo for the paper PHUDGE: Phi-3 as Scalable Judge. Evaluate your LLMs with or without custom rubric, reference answer, absolute, relative and much more. It contains a list of all the available tool, methods, repo, code etc to detect hallucination, LLM evaluation, grading and much more.

Zotero AI plugin chatting papers with ChatGPT, Gemini, Claude, Llama 3.2, Gemma, Mistral and Phi-3.5

Phi-3.5 for Mac: Locally-run Vision and Language Models for Apple Silicon

Azure OpenAI (demos, documentation, accelerators).

Text-To-Speech, RAG, and LLMs. All local!