🚧 Important Development Notice 🚧
This repository is under active construction!
"I'm building in public to stay accountable - some features below exist as goals rather than working code... yet!"
Current Stable Components: Core training pipeline, Basic GPT implementation
Experimental Features: DPO, Multi-GPU training (partial support)
| Aspect | What You’ll Achieve |
|---|---|
| Learn by Building | Create an AI that understands its own architecture and teaches others. |
| Full LLM Pipeline | From tokenization to RLHF (PPO/DPO), using PyTorch for low-level control. |
| Scalable Design | Notebooks → Modular code → Distributed training → Production-ready bot. |
| Meta-AI Magic | Interact with Meta_Bot to debug models, explain code, and guide your learning journey. |
| Component | Key Capabilities |
|---|---|
| Interactive Notebooks | Prototype tokenizers, model layers, and training loops with Colab/Jupyter. |
| Modular Framework | Reusable modules for datasets (data/), models (models/), and training scripts. |
| Scalable Pipeline | Multi-GPU/TPU training, custom tokenizers, and RLHF with PPO/DPO. |
| Meta_Bot | Deploy a chatbot that explains its own codebase and answers LLM theory questions. |
- Basic GPT Implementation
- Single-GPU Training Pipeline
- Notebook Prototypes (Tokenization, SFT)
current_focus = [
"Meta_Bot Gradio Interface (50% complete)",
"Custom Tokenizer (30% implemented)",
"DPO Optimization (experimental)"
]# Planned Features
Q1 2025:
- Quantization Support
- BERT-style Pretraining
- Comprehensive Evaluation Suite
<<<<<<< HEAD
---
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>>>>>>> 66aaebd965d19763a51f796ca786c45397a70e16
## **⚡ Quick Start**
### **Prerequisites**
- Python 3.10+
- PyTorch 2.0+
- CUDA 11.8 (recommended)
### **Installation**
```bash
git clone https://github.com/silvaxxx1/MyLLM101.git
cd MyLLM101
pip install -r requirements.txt# Start small-scale training (CPU/GPU)
python train.py --config configs/starter.ymlpython -m metabot.chat --mode basic# Experimental - May require code adjustments
torchrun --nproc_per_node=4 train.py --config configs/distributed.ymlfrom modules import FlexibleTrainer
trainer = FlexibleTrainer(
model=your_model,
strategy="mixed_precision", # Options: [basic, mixed_precision, ddp]
auto_scale=True # Automatic batch size adjustment
)We welcome brave contributors!
Given the project's early stage, please:
- Check open issues for known limitations
- Discuss major changes via GitHub Discussions first
- Focus on completing existing modules before adding new features
Contribution Guide:
graph LR
A[Fork] --> B[Branch]
B --> C[Code]
C --> D[Test]
D --> E[Pull Request]
This project draws inspiration from:
MIT License - See LICENSE for details.
"Build freely, learn deeply!" 🛠️🧠
```
