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Official Implementation of "Visual Agentic Memory: Online Long Video Understanding via Online Indexing, Hierarchical Memory, and Agentic Retrieval"

This is a multi-modal fusion method based on VGG16 and FastText for identifying useful information collected from social media platforms.

Package for bimodal training of deep neural networks on neurological data. Pypi: https://pypi.org/project/BiModNeuroCNN/

Thinking-native compiler for LEET Language Understanding item sets — killer-item design grammar, verification separation, deterministic gate chain, 3-model benchmark

Source-code level analysis of LLM RL training infra: async RL, weight sync, FP8, MoE routing | LLM RL 训练基础设施源码级分析

AI-Inference-Managed-by-AI: Go binary for managing AI inference on edge devices

Hikari's curated English measured notes on local LLM systems (serving, specdec, training, steering).

Turn any NVIDIA GPU into a local AI platform. Inference + fine-tuning in your browser. One command to start, automatic clustering.

TokenPowerBench: Benchmarking the power consumption of LLM inference across engines and deployment configurations

💰 Exocomp Agentic Environment for Go

Automated Triton w8a8 block FP8 kernel tuning tool for vLLM. Auto-detects model architecture, supports Qwen3-Coder-30B-A3B-Instruct-FP8/DeepSeek-V3/custom models, multi-GPU parallel tuning, and generates optimized kernel configs for quantization.

A curated collection of Claude Code agent skills that accelerate the entire vLLM development lifecycle.

本项目是一个完整的RAG系统教程，涵盖了从基础概念到高级技术的全面内容。通过Jupyter Notebook的形式，逐步讲解如何构建和优化一个高效的检索增强生成系统。本项目使用的所有模型都是本地化部署的，在3090上可以运行。

A complete roadmap to become an AI/LLM Engineer in 2026. Free & paid courses, 5 portfolio projects, interview prep, and resources. For software engineers with Python & ML basics who want to break into AI. Covers Transformers, RAG, fine-tuning, agents, LangChain, MLOps, and deployment.

⚡️ The "1-Minute RAG Audit" — Generate QA datasets & evaluate RAG systems in Colab, Jupyter, or CLI. Privacy-first, async, visual reports.

In this course navigates through the LLMOps pipeline, enabling you to preprocess training data for supervised fine-tuning and deploy custom Large Language Models (LLMs).

A simplified fine tune and deploy code based on bert for text matching.

Fine-tune a local LLM on your own app's data in 15 minutes. Runs entirely on-device, zero cloud after training. Apple Silicon + CUDA.

🏠 OpenStreetMap, AI import tool for buildings in Poland

Measuring generalization properties of graph neural networks