Why I Built It
- >The goal is to understand LLMs as systems, not only consume hosted APIs.
- >Building the tokenizer, model, trainer, data pipeline, and evaluation loop exposes the engineering trade-offs hidden by high-level frameworks.
RumalGPT - Transformer Language Model From Scratch
A hands-on language-model engineering project focused on understanding the full training pipeline by building tokenization, transformer architecture, training loops, evaluation, checkpointing, and scaling experiments from scratch.
PythonPyTorchTransformersBPE TokenizerCUDAMixed PrecisionLLM Training2026What I did
- >Built custom byte-pair encoding/tokenization experiments and trained progressively larger decoder-only transformer models.
- >Implemented configurable training runs with learning-rate scheduling, gradient accumulation, clipping, weight decay, validation, checkpointing, and perplexity tracking.
- >Scaled experiments from sub-million-parameter prototypes toward a roughly 1B-parameter training configuration to study memory, throughput, convergence, and architecture trade-offs.
- >Curated and mixed general-text, mathematical reasoning, and programming-oriented datasets for continued pretraining experiments.
- >Used the project to study quantization, context length, attention, local inference, RAG concepts, evaluation, and the practical limits of consumer GPU hardware.
Case Study
Engineering Focus
- >Decoder-only transformer design with configurable model depth, width, attention heads, and context length.
- >Training stability through warmup, learning-rate decay, gradient accumulation, clipping, optimizer tuning, validation, and checkpoints.
- >Throughput and memory experiments on consumer NVIDIA hardware using mixed precision and GPU-aware training choices.
What It Demonstrates
- >Comfort moving from infrastructure and backend engineering into ML systems and numerical workloads.
- >Ability to debug long-running training pipelines using metrics such as loss, perplexity, tokens per second, memory usage, and checkpoint state.
- >A learning-by-building approach that connects model architecture, data quality, hardware limits, and deployment choices.