Discover the Best AI Resources

Generative Models by Stability AI

Reference implementation for Stability AI's diffusion models: SDXL base/refiner/Turbo for text-to-image, plus Stable Video Diffusion, SV3D, and SV4D for image-to-video and 4D synthesis. A modular engine separates samplers, guiders, and conditioners.

MetaGPT

Orchestrates LLM-based roles (product managers, architects, engineers) to turn a one-line requirement into user stories, APIs and a starter code repo. SOP-driven multi-agent workflows with CLI and library APIs for prototype generation and agentic development.

3D Gaussian Splatting for Real-Time Radiance Field Rendering

Enables real-time (≥30 fps) 1080p novel-view synthesis by representing scenes as optimized anisotropic 3D Gaussians plus a visibility-aware splatting renderer; provides the paper's reference implementation, pretrained models and viewers — high-quality training requires CUDA GPU and significant VRAM.

NotebookLM

Upload your own documents, PDFs, slides, or web pages and ask questions answered only from that material, with inline citations pointing to the exact passage used. Audio Overview turns your sources into a downloadable two-host podcast discussion.

Open Interpreter

Lets LLMs run code and control a user’s computer via natural language (Python, JavaScript, Shell, etc.) with interactive approval. Supports local or hosted models, terminal and Colab/Codespaces integrations, streaming output, and configurable safety/auto-run options.

stable-diffusion.cpp

stable-diffusion.cpp is a pure C/C++ implementation for diffusion model inference, based on ggml, supporting models like Stable Diffusion (SD1.x, SD2.x, SDXL), Flux, Wan, Qwen Image, Z-Image, and more. It's lightweight with no external dependencies, supports backends like CPU, CUDA, Vulkan, Metal, and features like LoRA, ControlNet, LCM for efficient local image generation on platforms including Linux, Mac, Windows, and Android.

LLMs-from-scratch

Builds a GPT-style LLM in PyTorch step by step — tokenizer, attention, pretraining, and finetuning — with no external LLM frameworks. Companion code to a Manning book, with bonus chapters on LoRA and modern Llama/Qwen-style architectures.

LiteLLM

Calls 100+ LLM providers — OpenAI, Anthropic, Gemini, Bedrock, Azure — through one OpenAI-compatible API, as a Python SDK or self-hosted proxy. The proxy adds virtual keys, spend tracking, rate limits, and load balancing across models and providers.

OpenRLHF

Trains LLMs with RLHF at scale by splitting actor, critic, reward, and reference models across separate GPU groups via Ray, with vLLM-accelerated generation and DeepSpeed ZeRO-3. Supports PPO, GRPO, REINFORCE++, DPO, plus async and agentic multi-turn RL.

ms-swift

Fine-tunes and deploys 600+ LLMs and 400+ multimodal models in one framework, with SFT, pretraining, RLHF (DPO, PPO, GRPO), and lightweight methods like LoRA and QLoRA. Adds Megatron parallelism, vLLM/SGLang/LMDeploy inference, and a training web UI.

Continue

Open-source AI coding assistant for VS Code and JetBrains that bundles autocomplete, chat, inline edit, and an agent mode behind one config, letting each capability use any model provider rather than a single locked-in vendor.

Fooocus

Runs Stable Diffusion XL behind a Midjourney-style interface, hiding samplers, model swaps, and LoRA weights. A built-in GPT2 expander rewrites prompts into richer styling, and it works fully offline on as little as 4GB of Nvidia VRAM.