Global

8-Flash-Next on any consumer hardware: one-click install for Windows / Linux. Strata inference engine, OpenAI/Anthropic API on localhost, optional image input.

Learn AI Engineering! Learn it. Build it. Ship it for others.

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.

Use it for structured workloads with repeated policies, account context, catalog attributes, or correlated fields. 5-9B, through vLLM. It returns binary, categorical or scored decisions with probabilities.

《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.

Model fingerprint detection with a shared Web and Bun CLI reference bank

ClearAI is an ontology discovery and exploration platform, built on two core concepts:

Implementing LLM architectures from scratch in PyTorch

This repository contains the code for developing, pretraining, and finetuning a GPT-like LLM and is the official code repository for the book Build a Large Language Model (From Scratch).

As part of model development, we evaluate our models on open research problems. We expanded these evaluations after performance on our existing mathematical evaluations saturated. Some outputs build upon earlier results produced by the models.

LIST OF ALL MY JAILBREAKS

0 is a production-ready, easy-to-use agent framework with essential abstractions that keep up with rising model capability.

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）

🪢 Open source agent evals & observability: Trace, evaluate, and improve LLM applications with one open platform.

To download the model weights manually, use the following command:

Qwen3-8B on a real BANKING77 item. Every number is a model output.

Build, test, and deploy ML-driven trading strategies — from data sourcing to live execution.

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

Langflow is a powerful tool for building and deploying AI-powered agents and workflows.

《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.

> Note: You can also use pip if you prefer, but uv is recommended for better performance and more reliable dependency management.

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

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.

What are the principles we can use to build LLM-powered software that is actually good enough to put in the hands of production customers?

Automated Penetration Testing Agentic Framework Powered by Large Language Models

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.