🚀 MyLLM: Building My Meta_Bot — From Scratch, For Real

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⚠️ Work In Progress — Hack at Your Own Risk 🚧

MyLLM isn’t just another library.
It’s a playground for learning, understanding, and building LLMs from scratch — end to end.

This project was born out of one goal:

Understand every single line of a modern transformer stack.
From tokenization → attention → training → RLHF → inference.

Current Status

Area	Status	Description
Interactive Notebooks	✅ Stable	Guided, from-first-principles learning
Modular Mini-Projects	✅ Stable	Focused, reusable experiments
MyLLM Core Framework	⚙️ Active Development	Pure PyTorch, transparent
MetaBot	🛠 Coming Soon	A chatbot that explains itself

⚠️ Some components are stable, others evolve fast.
This repo is meant for exploration, experimentation, and breaking things safely.

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🌱 Why MyLLM Exists

There are already great libraries (🤗 Hugging Face, Lightning, TRL…).

But they hide too much.

MyLLM is intentionally different:

• Minimal — no unnecessary abstractions
• Hackable — everything is visible and editable
• Research-friendly — LoRA, QLoRA, PPO, DPO, quantization
• From scratch — so you actually understand what’s happening

This project is for:

Engineers who want to think like researchers
Researchers who want to ship real systems

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🗺 Architecture: The Three Layers of MyLLM

MyLLM is structured as a learning → experimentation → production pipeline.

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1️⃣ Interactive Notebooks — Learn by Doing

The notebooks/ directory is the entry point.

Each notebook:

• Explains theory
• Implements from scratch
• Encourages modification & experimentation

MyLLM/
└── notebooks/
├── 0.0.WELCOME.ipynb
├── 1.1.DATA.ipynb
├── 1.2.Tokenizer.ipynb
├── 2.1.ATTENTION.ipynb
├── 2.2.More_ATTENTION.ipynb
├── 2.3.GPT.ipynb
├── 2.4.Llama3.ipynb
├── 3.1.TRAIN.ipynb
├── 3.2.TRAIN_Pro.ipynb
├── 4.1.SFT_Text_Classification.ipynb
├── 4.2.SFT_Instruction_Following.ipynb
├── 4.3.SFT_PEFT.ipynb
├── 5.1.RLHF_PPO.ipynb
├── 5.2.RL_DPO.ipynb
├── 6.1.INFERENCE_Text_Generation.ipynb
├── 6.2.KV_Cache.ipynb
├── 6.3.Quantization_1.ipynb
├── 6.4.Quantization_2.ipynb
├── Appendix_A_GPT_2_Llama2.ipynb
└── Appendix_B_Gradio.ipynb

💡 Change an attention mask and immediately see how generation breaks or improves.

That’s real learning.

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2️⃣ Modular Mini-Projects — Targeted Experiments

The Modules/ directory isolates one concept at a time.

MyLLM/
└── Modules/
├── 1.data/        # Dataset loading & preprocessing
├── 2.models/      # GPT, LLaMA-style architectures
├── 3.training/    # Training loops & utilities
├── 4.finetuning/  # SFT, DPO, PPO experiments
└── 5.inference/   # KV cache, quantization

Example: Train a small GPT from scratch

python Modules/3.training/train.py --config configs/basic.yml

This lets you experiment without touching the full framework.

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3️⃣ MyLLM Core Framework — HF-like, but transparent

The myllm/ directory is where everything converges into a research-grade framework.

myllm/
 ├── CLI/             # Command-line tools
 ├── Configs/         # Centralized configs
 ├── Train/           # SFT, DPO, PPO engines
 ├── Tokenizers/      # Production-ready tokenizers
 ├── utils/           # Shared utilities
 ├── api.py           # REST API for serving
 └── model.py         # Core LLM definition

Example Usage

from myllm.model import LLMModel
from myllm.Train.sft_trainer import SFTTrainer

model = LLMModel()

trainer = SFTTrainer(
    model=model,
    dataset=my_dataset
)

trainer.train()

Every line maps to real code. No hidden magic. No black boxes.

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🔮 Coming Soon: MetaBot

MetaBot is the final layer:

A chatbot that answers questions and shows how it generated the answer

Built with:

• MyLLM core
• Gradio UI
• Fully open source (Meta_Bot/)

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📍 Roadmap

Status	Milestone	Description
✅	Interactive Notebooks	From-first-principles learning
✅	Modular Mini-Projects	Reusable experiments
⚙️	Core Framework	SFT, DPO, PPO, quantization
🛠	MetaBot	Interactive chatbot + UI

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⚡ Quick Challenges

Try these to really learn:

• Modify attention masks and observe behavior
• Train a GPT to write haiku poems
• Add a new trainer (TRL-style)
• Quantize a model and benchmark speed
• Implement a new attention variant

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🙌 Inspiration

This project stands on the shoulders of giants:

• Andrej Karpathy — NanoGPT minimalism
• Umar Jamil — Practical transformer intuition
• Sebastian Raschka — Deep theoretical clarity

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🏁 The Vision

A transparent, educational, and production-ready LLM stack built from scratch by people who want to own every line of their AI system.

Let’s remove the black boxes and build LLMs the right way.

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📜 License

This project is licensed under the MIT License. See the LICENSE file for details.