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VIP cheatsheet for Stanford's CME 295 Transformers and Large Language Models
The 500 AI Agents Projects is a curated collection of AI agent use cases across various industries. It showcases practical applications and provides links to open-source projects for implementation…
Professional AI Investment Analysis Suite with Multi-Agent System
FNSPID: A Comprehensive Financial News Dataset in Time Series
An open-source, code-first Python toolkit for building, evaluating, and deploying sophisticated AI agents with flexibility and control.
An open-source, code-first Python toolkit for building, evaluating, and deploying sophisticated AI agents with flexibility and control.
A collection of sample agents built with Agent Development (ADK)
Context management for long-context LLMs, agents, and vibe coding. Instantly build context for an entire repo, selected files, folders, and GitHub issues to generate structured AI-XML context with …
A collection of production-ready Generative AI Agent templates built for Google Cloud. It accelerates development by providing a holistic, production-ready solution, addressing common challenges (D…
Labs and demos for courses for GCP Training (http://cloud.google.com/training).
Minimal reproduction of DeepSeek R1-Zero
Official Repo for Open-Reasoner-Zero
Official repository for "Craw4LLM: Efficient Web Crawling for LLM Pretraining"
Code for the paper "FinRL-DeepSeek: LLM-Infused Risk-Sensitive Reinforcement Learning for Trading Agents" arXiv:2502.07393
This is the repository for NAACL'25 paper "TART: An Open-Source Tool-Augmented Framework for Explainable Table-based Reasoning"
Easily fine-tune, evaluate and deploy gpt-oss, Qwen3, DeepSeek-R1, or any open source LLM / VLM!
Meltano: the declarative code-first data integration engine that powers your wildest data and ML-powered product ideas. Say goodbye to writing, maintaining, and scaling your own API integrations.
🤖 Chat with your SQL database 📊. Accurate Text-to-SQL Generation via LLMs using RAG 🔄.
⚡️ GenBI (Generative BI) queries any database in natural language, generates accurate SQL (Text-to-SQL), charts (Text-to-Chart), and AI-powered insights in seconds.
Use LOTUS to process all of your datasets with LLMs and embeddings. Enjoy up to 1000x speedups with fast, accurate query processing, that's as simple as writing Pandas code
The official GitHub page for the survey paper "Foundation Models for Music: A Survey".
A playbook for effectively prompting post-trained LLMs