A curated list of foundation models for vision and language tasks

Tensor library for machine learning

A NodeJS RAG framework to easily work with LLMs and embeddings

An AI-powered application that can guess movie titles based on plot summaries. Built using LangChain, Google Palm LLM, CSVLoader, RetrievalQA, Google Palm Embeddings, and FAISS. Deployed on Streamlit for an interactive user experience, allowing you to enter a plot summary and receive a predicted movie title.

Implementing a ChatGPT-like LLM from scratch, step by step

Maid is a cross-platform Flutter app for interfacing with GGUF / llama.cpp models locally, and with Ollama and OpenAI models remotely.

🦾 A meta-Language for LLMs to produce or parse structured info.

Akeru is an open source AI platform built on top of the Akeru AI edge network. The network runs as a Bittensor Subnet, providing a transparent, safe and highly available AI capacities.

Reverse Engineering: Decompiling Binary Code with Large Language Models

HTML UI for Ollama. Minimal & responsive UI: mobile & desktop. Cross-browser support. Simple installation: host on your own server, run in your browser.

Generalist and Lightweight Model for Named Entity Recognition (Extract any entity types from texts) @ NAACL 24

Remove information gap with AI assistant.

VisualRWKV is the visual-enhanced version of the RWKV language model, enabling RWKV to handle various visual tasks.

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

ReEvo: Large Language Models as Hyper-Heuristics with Reflective Evolution

Drag & drop UI to build your customized LLM flow

Open-source evaluation toolkit of large vision-language models (LVLMs), support GPT-4v, Gemini, QwenVLPlus, 40+ HF models, 20+ benchmarks

日本語LLMまとめ - Overview of Japanese LLMs

Generative AI suite powered by state-of-the-art models and providing advanced AI/AGI functions. It features AI personas, AGI functions, multi-model chats, text-to-image, voice, response streaming, code highlighting and execution, PDF import, presets for developers, much more. Deploy on-prem or in the cloud.

🤖 𝗟𝗲𝗮𝗿𝗻 for 𝗳𝗿𝗲𝗲 how to 𝗯𝘂𝗶𝗹𝗱 an end-to-end 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻-𝗿𝗲𝗮𝗱𝘆 𝗟𝗟𝗠 & 𝗥𝗔𝗚 𝘀𝘆𝘀𝘁𝗲𝗺 using 𝗟𝗟𝗠𝗢𝗽𝘀 best practices: ~ 𝘴𝘰𝘶𝘳𝘤𝘦 𝘤𝘰𝘥𝘦 + 11 𝘩𝘢𝘯𝘥𝘴-𝘰𝘯 𝘭𝘦𝘴𝘴𝘰𝘯𝘴