ULTRATHINK Documentation
This folder contains user and developer documentation for the ULTRATHINK project.
Complete guides for training state-of-the-art language models.
Getting Started (5 minutes)
Start here if you're new to ULTRATHINK:
- Installation - Set up your environment
- Getting Started - Your first training run
- Training Small Models - Best practices for small datasets
Training Guides
Basic Training
- Training Small Models - Start with small datasets (recommended)
- Google Colab - Train with free GPU in your browser
- Datasets - Using built-in, custom, and mixed datasets
Advanced Training
- DeepSpeed - ZeRO optimization for memory efficiency
- Distributed Training - Multi-GPU with Accelerate/DDP
- Advanced Features - 4D parallelism, RLHF, MoE
Reference
- Model Card - Architecture specifications and limitations
- Benchmarks - Performance metrics and results
- Framework Comparison - vs GPT-NeoX, Megatron-LM, Axolotl
- Troubleshooting - Common issues and solutions
- Testing Guide - Running and writing tests
- Development - Code structure and contributing
- Evaluation - Benchmarking your models
- FAQ - Common questions and solutions
Planning & Community
- Roadmap - Future plans and features
- Marketing Guide - Promotion strategy
- Quick Start Promotion - 7-day launch plan
Monitoring & Tools
ULTRATHINK includes production-grade monitoring:
from src.monitoring import MetricsLogger
# Track metrics
metrics = MetricsLogger(window_size=100)
metrics.log(loss, lr, model, batch_size, seq_length)
See Testing Guide for details.
Quick Reference
| Task | Command |
|---|---|
| Train tiny model | python train_ultrathink.py --hidden_size 256 --num_layers 2 |
| Profile model | python scripts/profile_model.py --size tiny |
| Run tests | pytest |
| Clean cache | python scripts/cleanup.py |
Need Help?
- Issues: GitHub Issues
- Discussions: GitHub Discussions
- FAQ: faq.md — Frequently asked questions