Modern LLM Training Pipeline Comparison | Generated by AI
llmTrain
This is an educational end-to-end LLM training pipeline — train a BPE tokenizer from raw text, then train a ~100M parameter Transformer from scratch. The model (“MiniLLM-0.1B”) was published on HuggingFace Hub.
vs. nanoGPT
| Aspect | nanoGPT | llmTrain |
|---|---|---|
| Architecture | Classic GPT-2 (learned positional embeddings, GELU) | Modern: RoPE + SwiGLU + pre-norm |
| Attention | Manual attention math | Flash Attention via scaled_dot_product_attention |
| Position encoding | Learned absolute embeddings | Rotary (RoPE) |
| Activation | GELU | SwiGLU (gated linear units) |
| Tokenizer | Uses GPT-2 tokenizer (tiktoken) | Trains its own BPE from scratch |
| Data pipeline | Memory-mapped files | Pre-tokenized .pt tensors + async DataLoader |
| Compilation | torch.compile |
torch.compile |
| Scope | Full GPT-2 replication + fine-tuning | Training pipeline only (no fine-tuning) |
| Focus | Reproduce GPT-2 results | Show full pipeline: data → tokenizer → model |
| HF integration | Minimal | Native — saves as AutoModelForCausalLM |
Key difference: nanoGPT replicates GPT-2’s exact architecture and can reproduce published results. llmTrain uses a more modern architecture (closer to LLaMA-style) and adds a complete tokenizer training stage that nanoGPT skips. It’s less focused on benchmarks and more focused on showing the full pipeline end-to-end.