From mathematical foundations to production-grade AI systems — a deep technical research archive, not a tutorial.
This repository documents a systematic, engineering-first exploration of Large Language Models. The goal is genuine understanding: how they learn, why they fail, how they scale, and how to deploy them responsibly.
What's inside:
- Transformer architecture built from scratch in PyTorch
- Custom training pipelines, optimization experiments, and scaling studies
- LangChain & LangGraph application development
- Hugging Face fine-tuning with LoRA / QLoRA
- Retrieval-Augmented Generation (RAG) systems end-to-end
LLM-Journey/
├── 01_Build_LLM_From_Scratch/ # Transformer, attention, training loop
├── 02_Refine_LLM_From_Scratch/ # RoPE, FlashAttention, RMSNorm, KV cache
├── 03_LangChain/ # Chains, agents, memory, tool calling
├── 04_LangGraph/ # Stateful multi-agent workflows
├── 05_HuggingFace/ # Fine-tuning, PEFT, quantization, deployment
├── 06_RAG/ # Embeddings, vector DBs, retrieval strategies
├── experiments/ # Hyperparameter sweeps, ablations
├── research_notes/ # Paper summaries, conceptual notes
├── datasets/ # Preprocessing and dataset utilities
└── evaluation/ # Metrics, benchmarking, hallucination analysis
Full transformer implementation using raw PyTorch — no high-level APIs.
Mathematical foundations: linear algebra, probability theory, information theory (entropy, KL divergence), optimization.
Architecture: scaled dot-product attention → multi-head attention → causal masking → positional encoding (sinusoidal & learned) → encoder-only, decoder-only, and encoder-decoder variants.
Training pipeline: BPE tokenization, vocabulary construction, batching, padding/masking, next-token prediction objective.
Improving performance and efficiency on the baseline model.
- Training: mixed precision (FP16), gradient accumulation, gradient clipping, cosine LR schedule with warmup
- Architecture: RMSNorm, Rotary Positional Embeddings (RoPE), SwiGLU activation, KV caching, Flash Attention (conceptual study)
- Regularization: dropout tuning, label smoothing, weight decay, early stopping
- Scaling studies: parameter count vs. performance, dataset size, compute tradeoffs
Modular LLM application development.
Core: LLM wrappers, prompt templates, chains, memory, output parsers, tool/function calling, structured output.
Applications built: chatbot with persistent memory, document QA system, API-connected agent, multi-tool reasoning agent.
Stateful, multi-step AI workflow orchestration.
Topics: graph-based execution, conditional branching, retry mechanisms, human-in-the-loop integration, multi-agent collaboration.
Implementations: multi-agent research assistant, tool-using planner agent, decision-tree LLM workflow.
Production-grade LLM tooling and fine-tuning.
- Fine-tuning: full fine-tuning, LoRA, QLoRA, PEFT methods
- Deployment: inference pipelines, model quantization, ONNX export, TorchScript, CPU vs. GPU benchmarking
- Data: dataset loading, preprocessing, streaming
Combining retrieval systems with LLMs for grounded, factual responses.
User Query → Embedding Model → Vector DB → Top-k Retrieval → Context Augmentation → LLM Response
Embedding models: Sentence Transformers, open-source alternatives.
Vector databases: FAISS, ChromaDB, Pinecone (conceptual).
Retrieval strategies: similarity search, hybrid search, Maximal Marginal Relevance (MMR).
Evaluation: retrieval recall, context relevance, answer faithfulness, hallucination analysis.
| Metric | Purpose |
|---|---|
| Perplexity | Language modeling quality |
| BLEU / ROUGE | Text similarity & summarization |
| Exact Match / F1 | QA and retrieval evaluation |
| Latency | Inference performance |
| GPU Memory | Efficiency and cost |
PyTorch · Hugging Face Transformers · LangChain · LangGraph · FAISS · ChromaDB · NumPy · Matplotlib · Weights & Biases
- Attention is a learned, weighted information routing mechanism — not magic.
- Scaling laws matter more than architectural novelty in most practical settings.
- Retrieval significantly reduces hallucination; it is not optional for factual applications.
- Fine-tuning is highly data-sensitive and expensive to do well.
- Prompt engineering has a ceiling that architecture and training do not.
- RLHF implementation
- Direct Preference Optimization (DPO)
- Domain adaptation experiments
- Multimodal / Vision-Language models
- Quantized inference on edge devices
- Attention Is All You Need
- GPT-2 / GPT-3
- LLaMA
- Scaling Laws for Neural Language Models
- RAG (Lewis et al., Meta)
- LoRA
This repository is a personal research archive. Discussions and ideas are welcome.