Global

AirLLM dramatically reduces inference memory usage, letting 70B large language models run on a single 4GB GPU card — without quantization, distillation, or pruning. 8-Flash-Next (125B) on 6GB, and DeepSeek-V3 (671B) on 12GB. 8-Flash-Next (125B) under 6GB.

《深入理解 AI Agent：设计原理与工程实践》（李博杰 著）开源主仓库：全书正文、编译版 PDF 与按章配套代码

NOTE] > Based on this course, I co-wrote the LLM Engineer's Handbook, a hands-on book that covers an end-to-end LLM application from design to deployment. The LLM course will always stay free, but you can support my work by purchasing this book.

Non-autoregressive System 1 decision engine. Typed choice, score and yes/no decisions over any text in a single forward pass, in 100+ languages, with a router that picks the right checkpoint per request.

py starts the SGLang server automatically and sends concurrent requests for an image directory or PDF:

Fine-tune an 8B model on a 4 GB laptop GPU. Layer streaming keeps the frozen base out of VRAM and feeds it to the GPU one decoder layer at a time. 32 GB.

VoxCPM is a tokenizer-free Text-to-Speech system that directly generates continuous speech representations via an end-to-end diffusion autoregressive architecture, bypassing discrete tokenization to achieve highly natural and expressive synthesis.

Local UI to run and train LLMs and diffusion models. 8, DeepSeek-V4, MiniMax-H3, Gemma 4, FLUX and more.

> Prefer to Clone Locally? > > This repository includes 50+ language translations which significantly increases the download size. To clone without translations, use sparse checkout: > > Bash / macOS / Linux: >

pth） - 支持跨不同 GPU 数量恢复（自动调整 step） - 支持 wandb 训练记录连续性（自动恢复同一个 run）

If you like the course, don't hesitate to ⭐ star this repository. This helps us to make the course more visible 🤗.

AI System Design - Learn how to design AI systems built on LLMs, RAG, and AI Agents step by step.

《动手学大模型Dive into LLMs》系列编程实践教程

We advise to run all examples through Google Colab for the easiest setup. Google Colab allows you to use a T4 GPU with 16GB of VRAM for free. All examples were mainly built and tested using Google Colab, so it should be the most stable platform.

🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.

MiniCPM-V and MiniCPM-o are multimodal LLM series designed for strong performance and efficient deployment on devices. MiniCPM-V focuses on efficient vision-language understanding across image, video and text inputs.

Jev-like family of decision models built on top of Qwen3.5/3.8 you can train and run on your own

🐧 PenguinHarness: Local-first multi-agent app development platform - create, fine-tune, deploy AI apps automatically!

Comprehensive open-source library of AI research and engineering skills for any AI model. Package the skills and your claude code/codex/gemini agent will be an AI research agent with full horsepower. Maintained by Orchestra Research.

nanochat is the simplest experimental harness for training LLMs. It is designed to run on a single GPU node, the code is minimal/hackable, and it covers all major LLM stages including tokenization, pretraining, finetuning, evaluation, and inference.

Update Nov 2025 nanoGPT has a new and improved cousin called nanochat. It is very likely you meant to use/find nanochat instead. nanoGPT (this repo) is now very old and deprecated but I will leave it up for posterity.

TimesFM (Time Series Foundation Model) is a pretrained time-series foundation model developed by Google Research for time-series forecasting.

🤖 Agent 应用层 - ✅ Agent / Workflow - LangGraph、OpenAI Agents SDK - AutoGen、CrewAI、Pydantic AI - Dify、n8n、Flowise - ✅ 任务形态 - Research / Coding / Web Agent - Multi-Agent / Supervisor / Handoff - Computer Use / Browser Automation

This release contains training, two inference backends, benchmark scoring, temperature calibration, and a browser demo.

Meshy models every role of an RL run as an independent service. Samples flow between services through a single TransferQueue data plane, control flow is driven by data availability, and the whole topology is derived locally by each process from one declarative recipe.

Infrastructure for the next generation of voice agents, designed to provide universal memory. It is divided into a left brain and a right brain, storing information and emotions respectively, while a fully streaming architecture eliminates latency at the fundamental level.

The book is available on: - Amazon - O'Reilly - Kindle - Audible

Gaussian blur reveals how Valen-Preview-0923 adapts its decisions and confidence as visual detail decreases. 2% at the strongest blur.

DSPy: The framework for programming—not prompting—language models

> 第八章聚焦 GRPO、OPD、Search-R1 与 ReTool。更多 Agentic RL 算法、训练代码与实验实践，可以前往作者持续维护的另一个仓库 agentic-rl-lab；该仓库更新频率更高，会持续跟进新的算法与环境。