MiniMind is an educational open-source toolkit for training small language models entirely from scratch using native PyTorch. It provides a complete pipeline including custom BPE tokenizer training, pretraining, supervised fine-tuning (SFT), LoRA, RLHF (DPO), RLAIF (PPO/GRPO/CISPO), agentic reinforcement learning, and knowledge distillation. The system is designed to be accessible (low compute cost, ~3 RMB and 2 hours on a single GPU) and includes inference interfaces such as a CLI, Streamlit web UI, and an OpenAI-compatible API server. While primarily a software application for training and deploying LLMs, it also contains dataset preparation, evaluation benchmarks, and research experiments, giving it a hybrid character that leans toward application due to its focus on providing reusable training scripts and deployment tools.
ScrapeGraphAI is a Python library that extracts structured data from websites and local documents using LLM-driven graph pipelines. Instead of a traditional web scraper with hardcoded selectors, users define extraction through natural-language prompts and optional Pydantic schemas; the library then executes a directed graph of nodes such as fetching, parsing, chunking, and answer generation. The public API is a set of graph classes (SmartScraperGraph, SearchGraph, JSONScraperGraph, CSVScraperGraph, SpeechGraph, ScriptCreatorGraph, etc.) in scrapegraphai/graphs, each composing reusable nodes from scrapegraphai/nodes and running them on a BaseGraph execution engine. It is consumed programmatically in Python and supports many LLM providers (OpenAI, Ollama, Groq, Mistral, Anthropic, DeepSeek, xAI, NVIDIA, MiniMax) and multiple fetching backends (Playwright, Selenium, Chromium, BrowserBase, Scrape.do). The architecture is a lightweight DAG framework: nodes have typed inputs/outputs, can branch conditionally, iterate, merge results, and accumulate cost/token metadata; BaseGraph walks the graph, handles state, and collects execution information. The repository also contains document loaders, model token/cost registries, prompt templates, telemetry, proxy rotation, screenshot-scraping utilities, a GraphBuilder that constructs graphs from natural language, and integrations such as Burr and Indexify. While the README aggressively promotes a commercial hosted API at scrapegraphai.com, the repository itself is a self-contained open-source library with examples and tests. The codebase is generally well structured and modular, with clear separation between graphs, nodes, prompts, helpers, utils, and models. It shows signs of rapid growth: some typos, duplicated test fixtures, hardcoded model pricing tables, a Python version mismatch between pyproject and Dockerfile, and dependency fragility around LangChain and optional browser/OCR packages. These issues are manageable but give the codebase a medium technical drift. Overall it is an active, useful library for LLM-based extraction, and several utilities are good candidates for reuse outside the project.
This repository is a literature survey on recent advances in large language model benchmarks against data contamination, specifically the transition from static to dynamic evaluation. It contains a single README.md file that lists and categorizes numerous research papers, benchmarks, and methods. There is no executable code, data pipelines, notebooks, or any software artifacts. The repository serves as a curated reference list and taxonomy for researchers, but offers no reusable code or data assets. It is not a functional application or a data research project with ETL or model training components.